Descriptor: "MESH: Systems Biology" - Searchworks@Jio Institute Digital Library Search Results

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37 results on '"MESH: Systems Biology"'

1. In vivo commensal control of Clostridioides difficile virulence

Author: Mario L. Arrieta-Ortiz, Johann Peltier, Andrew B. Onderdonk, Selva Rupa Christinal Immanuel, Narjol Gonzalez-Escalona, Nicholas DiBenedetto, Richard Lavin, Nitin S. Baliga, Marc W. Allard, Maria Hoffman, Abraham L. Sonenshein, Yan Luo, Christopher K Cummins, Brintha P. Girinathan, Lynn Bry, Bruno Dupuy, Jay N. Worley, Mary L. Delaney, Georg K. Gerber, Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), National Center for Biotechnology Information (NCBI), Pathogénèse des Bactéries Anaérobies / Pathogenesis of Bacterial Anaerobes (PBA (U-Pasteur_6)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institute for Systems Biology [Seattle] (ISB), U.S. Food and Drug Administration (FDA), Harvard-MIT Division of Health Sciences and Technology [Cambridge], Massachusetts Institute of Technology (MIT), Tufts University School of Medicine [Boston], This work was supported by the BWH Precision Medicine Institute, R01AI153605 from the National Institute of Allergy and Infectious Diseases (NIAID) and P30DK034854 from the National Institute of Diabetes, Digestive and Kidney Diseases to L.B., R01AI128215, R01AI141953, and U19AI135976 from NIAID and IIBR-2042948 from the National Science Foundation to N.S.B., B.P.G. was supported by T32 HL007627, and the work of J.N.W. was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health. J.P. received support from the Institut Pasteur (Bourse ROUX). This work was conducted by authors from the FDA working as official U.S. Government employees and for which no additional external funding was utilized.
Subjects: MESH: Clostridioides difficile, MESH: Amino Acids, colitis, Systems biology, MESH: Clostridium Infections, [SDV]Life Sciences [q-bio], gnotobiotic mouse, Clostridium sardiniense, Virulence, MESH: Butyrates, Biology, MESH: Virulence, medicine.disease_cause, Microbiology, MESH: Clostridiales, MESH: Fermentation, Clostridia, MESH: Gene Expression Profiling, conventional mouse, In vivo, Virology, MESH: Germ-Free Life, MESH: Severity of Illness Index, medicine, Paraclostridium bifermentans, MESH: Animals, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Colitis, Pathogen, MESH: Mice, MESH: Gene Regulatory Networks, MESH: Gene Expression Regulation, Bacterial, metatranscriptomics, MESH: Symbiosis, Toxin, Host (biology), Clostridioides difficile, MESH: Clostridium, MESH: Arginine, MESH: Cecum, biology.organism_classification, medicine.disease, metabolomics, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, MESH: Systems Biology, Parasitology, carbon source enrichment analysis, anaerobe metabolism
Abstract: International audience; Leveraging systems biology approaches, we illustrate how metabolically distinct species of Clostridia protect against or worsen Clostridioides difficile infection in mice by modulating the pathogen's colonization, growth, and virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Paraclostridium bifermentans survive infection with reduced disease severity, while mice colonized with the butyrate-producer, Clostridium sardiniense, succumb more rapidly. Systematic in vivo analyses revealed how each commensal alters the gut-nutrient environment to modulate the pathogen's metabolism, gene regulatory networks, and toxin production. Oral administration of P. bifermentans rescues conventional, clindamycin-treated mice from lethal C. difficile infection in a manner similar to that of monocolonized animals, thereby supporting the therapeutic potential of this commensal species. Our findings lay the foundation for mechanistically informed therapies to counter C. difficile disease using systems biology approaches to define host-commensal-pathogen interactions in vivo.
Published: 2021

2. Human Immunology through the Lens of Evolutionary Genetics

Author: Lluis Quintana-Murci, Génétique Evolutive Humaine - Human Evolutionary Genetics, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Research in the Quintana-Murci lab is funded by the Institut Pasteur, the Centre National de la Recherche Scientifique (CNRS), the Agence Nationale de la Recherche (ANR-14-CE14-0008-02, ANR-14-CE14-0007-02, ANR-14-CE02-0003-01), the French Government’s Investissement d’Avenir program, Laboratoires d’Excellence ‘‘Integrative Biology of Emerging Infectious Diseases’’ (ANR-10- LABX-62-IBEID) and ‘‘Milieu Intérieur’’ (ANR-10-LABX-69-01), and the Fondation pour la Recherche Médicale (Equipe FRMDEQ20180339214), ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-10-LABX-0069,MILIEU INTERIEUR,GENETIC & ENVIRONMENTAL CONTROL OF IMMUNE PHENOTYPE VARIANCE: ESTABLISHING A PATH TOWARDS PERSONALIZED MEDICINE(2010), ANR-14-CE14-0003,ORBiMP,Déjouer la résistance aux beta-lactamines des méningocoques et pneumocoques(2014), ANR-14-CE14-0007,TBPATHGEN,Dissection de la pathogenèse de la tuberculose par l'identification de défauts monogéniques de l'immunité dans les formes pédiatriques sévères de la maladie(2014), ANR-14-CE14-0008,IEIHSEER,L'encéphalite Herpétique de l'enfant résulte de déficits héréditaires d'immunité contre l'HSV-1: une exception ou une règle?(2014), and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: MESH: Selection, Genetic, MESH: Genetics, Population, Population genetics, MESH: Biological Evolution, Biology, General Biochemistry, Genetics and Molecular Biology, Evolution, Molecular, Immune System Phenomena, 03 medical and health sciences, 0302 clinical medicine, Genetic variation, Humans, MESH: Genetic Variation, Selection, Genetic, MESH: Evolution, Molecular, MESH: Immunity, Selection (genetic algorithm), MESH: Immune System Phenomena, 030304 developmental biology, 0303 health sciences, Genetic diversity, MESH: Humans, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Human evolutionary genetics, Systems Biology, Immunity, Genetic Variation, Adaptation, Physiological, Biological Evolution, MESH: Adaptation, Physiological, 3. Good health, Genetics, Population, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Human evolution, Evolutionary biology, MESH: Systems Biology, Human genome, Adaptation, 030217 neurology & neurosurgery
Abstract: International audience; Pathogen-imposed selection pressures have been paramount during human evolution. Detecting such selection signatures in ancient and modern human genomes can thus help us to identify genes of temporal and spatial immunological relevance. Admixture with ancient hominins and between human populations has been a source of genetic diversity open to selection by infections. Furthermore, cultural transitions, such as the advent of agriculture, have exposed humans to new microbial threats, with impacts on host defense mechanisms. The integration of population genetics and systems immunology holds great promise for the increased understanding of the factors driving immune response variation between individuals and populations.
Published: 2019

3. Special issue 'Formal Method for Biological Systems Modelling'

Author: Lhoussaine, Cédric, Feret, Jérôme, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Analyse Statique par Interprétation Abstraite (ANTIQUE), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris sciences et lettres (PSL), Cedric Lhoussaine, and Jérôme Feret
Subjects: MESH: Formal methods, MESH: Logic, MESH: Systems biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Static analysis
Abstract: International audience; The field of system biology poses many challenges to computer scientists. Regulatory networks, intra- and extra-cellular signalling pathways, metabolic networks and whole cells models suffer both from high conceptual difficulties and from a huge combinatorial complexity. From the description of these models, by organising and collecting facts based on knowledge representation databases, to the execution of these models, through the abstraction of their main properties, formal methods offer promising potential approaches. While existing formal frameworks can be adapted to cope with the specific kinds of problems, new formal methods emerge from the confrontation with this new field of application. Besides the intrinsic combinatorial complexity, biological systems are characterised by the fact that they have not been humanly designed. Their dynamics are driven by competition for shared resources, time- and concentration-scale separation, and non-linear feedback loops. Many components have antagonistic effects according to the context. It is impossible to understand from which mechanisms the collective behaviour of these models come from.Mainstream quantitative approaches (based on ODEs) suffer from various limitations. To cope with the combinatorial complexity, series of simplifications are usually required. This results in several drawbacks: not only does it usually restrict the context of the application of the models, but also, it is likely to skew the model in the direction of what is intended by the modeller. Moreover, their results are specific for particular sets of parameters and they are difficult to generalize. As a consequence, they usually offer an unsatisfying level of confidence.Formal methods provide various means to increase confidence in models and to compute their properties. Formal languages provide hierarchical constructions to collect information about mechanistic interactions, in a more goal agnostic manner. Formal methods also assist the modeller to gradually refine/abstract information about a given process by including more/fewer details from the literature. Symbolic static analyses not only enhance confidence in models, but also provide new perspectives on models by efficiently computing informative invariants. Causal analyses provide explanations for the potential scenarios for events of interest to occur. The behaviour of models can be captured while avoiding the exploration of the different interleaving of these massively distributed agent-centric models. Lastly, model reduction provides new systems while identifying the elements that mainly drive the dynamics of these models.This Special Issue is dedicated to formal methods developed for the analysis of biological systems and their application to current open questions in biology. Its scope covers the design of specific language and modelling tools, the design of static analyses, causal analyses, model reduction techniques. We are also interested in feedback on the usage of formal approaches in the design and the analysis of models to address biological issues.
Published: 2021

4. The Evolution of Gene-Specific Transcriptional Noise Is Driven by Selection at the Pathway Level

Author: Julien Y. Dutheil, Natasa Puzovic, Gustavo Valadares Barroso, Max Planck Institute for Evolutionary Biology, Max-Planck-Gesellschaft, Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), École pratique des hautes études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UR226
Subjects: MESH: Gene Ontology, 0301 basic medicine, Transcription, Genetic, MESH: Selection, Genetic, Gene regulatory network, Gene Expression, Mice, Gene expression, Protein Interaction Mapping, MESH: Animals, MESH: Models, Genetic, Protein Interaction Maps, MESH: Protein Interaction Maps, MESH: Evolution, Molecular, Regulation of gene expression, Genetics, Natural selection, MESH: Genomics, Systems Biology, food and beverages, MESH: Transcription Factors, Genomics, MESH: Gene Expression Regulation, biological networks, MESH: Systems Biology, expression noise, Single-Cell Analysis, Transcriptional noise, MESH: Computational Biology, Protein Binding, inorganic chemicals, Systems biology, Biology, Investigations, complex mixtures, Evolution, Molecular, MESH: Gene Expression Profiling, 03 medical and health sciences, medicine, MESH: Protein Binding, Mus musculus, Animals, Selection, Genetic, MESH: Mice, Gene, [SDV.GEN]Life Sciences [q-bio]/Genetics, Models, Genetic, MESH: Transcription, Genetic, MESH: Transcriptome, Gene Expression Profiling, MESH: Protein Interaction Mapping, Computational Biology, medicine.disease, equipment and supplies, Gene expression profiling, 030104 developmental biology, evolution of gene expression, Gene Ontology, Gene Expression Regulation, MESH: Genome-Wide Association Study, bacteria, Transcriptome, MESH: Single-Cell Analysis, Genome-Wide Association Study, Transcription Factors
Abstract: Gene expression is a noisy process: in constant environment and genotype, cell to cell variability occurs because of randomness of biochemical reactions..., Biochemical reactions within individual cells result from the interactions of molecules, typically in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generates noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, owing, in particular, to the advent of single-cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. Here, we take advantage of two recently published data sets for the single-cell transcriptome of the domestic mouse Mus musculus to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We show that noise levels in the mRNA distributions (also known as transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraints on the encoded protein, and gene age. However, the position of the encoded protein in a biological pathway is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with the mean expression level in functional and evolutionary studies of gene expression.
Published: 2017

5. Integrating multi-scale knowledge on cardiac development into a computational model of ventricular trabeculation

Author: Jean-François Le Garrec, Jan M. Ruijter, Bouke A. de Boer, Vincent M. Christoffels, Sigolène M. Meilhac, Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), Cellules Souches et Développement, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), BdB was supported by an ESF QuanTissue exchange grant (number 4185). Work in the Meilhac Lab was supported by the FRM and an ANR grant [11-JSV2-00601], which funded JFLG, SMM is an INSERM research scientist., ANR-11-JSV2-0006,cardiopol,Polarité des cellules du myocarde au cours de la formation des chambres cardiaques(2011), ACS - Amsterdam Cardiovascular Sciences, Medical Biology, AR&D - Amsterdam Reproduction & Development, and Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)
Subjects: MESH: Models, Cardiovascular, Scale (ratio), Process (engineering), Heart Ventricles, media_common.quotation_subject, Systems biology, Medicine (miscellaneous), MESH: Imaging, Three-Dimensional, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Biology, Bioinformatics, Biochemistry, Genetics and Molecular Biology (miscellaneous), Mice, Imaging, Three-Dimensional, MESH: Computer Simulation, Component (UML), [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], Animals, Humans, Computer Simulation, MESH: Animals, MESH: Mice, media_common, MESH: Humans, Systems Biology, Cellular Potts model, Models, Cardiovascular, Reproducibility of Results, Experimental data, [SDV.BDD.MOR]Life Sciences [q-bio]/Development Biology/Morphogenesis, Pipeline (software), MESH: Reproducibility of Results, MESH: Systems Biology, Conceptual model, MESH: Heart Ventricles, Biological system
Abstract: International audience; Insights into the mechanisms of development of the mammalian four-chambered heart are based on biological observations at organ, tissue, cell, and molecular levels, but the full integration of these experimental data awaits a systems biology approach. Such an approach can be employed to formulate and test conceptual models in a computational simulation. To illustrate how this can be applied to heart development, we used the process of trabeculation, which is the formation of muscular strands during chamber development. We selected this process because it is localized, involves a restricted number of cell types, and a range of experimental data is available. Trabeculation of the ventricles is based on the interplay between endocardial and myocardial cells and involves molecular pathways underlying cell-cell interactions and tissue-specific cell behavior. A cellular Potts model was used for the simulation of these multi-scale processes. With fairly simple inputs, of which the relative contributions are unknown, an iterative exploration achieved an outcome that resembles the trabeculation process and allows further investigation of contributing factors. The systems biology pipeline from biological observations and conceptual modeling to a mathematical model and computational algorithms is described and discussed. The multi-level biological observations provide the components and their connections of the conceptual model. However, the true strength of systems biology must be found in the biological test of the predictions that result from an experimental change in the computational model. These validated predictions will ultimately elucidate the functional role of a component or interaction in the process of heart development.
Published: 2014

6. The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli

Author: Morin, Manon, Ropers, Delphine, Letisse, Fabien, Laguerre, Sandrine, Portais, Jean-Charles, Cocaign-Bousquet, Muriel, Enjalbert, Brice, Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, Institut National de la Recherche Agronomique (Department 'Microbiology of the Food Chain') Institut National de Recherche en Informatique et en Automatique) Agence Nationale de la Recherche under project RESET ANR-11-BINF-0005, ANR-11-BINF-0005,RESET,Eteindre et rallumer la machinerie d'expression génique chez les bactéries: de modèles mathématiques aux applications biotechnologiques(2011), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDV]Life Sciences [q-bio], CENTRAL CARBON METABOLISM, MESH: Systems biology, Post-Transcriptional RNA Processing, CsrA protein, PRODUCT, Stress, Physiological, Escherichia coli, YEAST, GENE-EXPRESSION, ACCUMULATION, STABILITY, Escherichia coli Proteins, RNA-Binding Proteins, post-transcriptional RNA processing, GLYCOGEN BIOSYNTHESIS, Gene Expression Regulation, Bacterial, FLUX ANALYSIS, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Carbon, Repressor Proteins, CATABOLISM, Phosphofructokinases, Mutation, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Systems biology, MESSENGER-RNA, Glycolysis, Glycogen
Abstract: International audience; Metabolic control in Escherichia coli is a complex process involving multilevel regulatory systems but the involvement of post-transcriptional regulation is uncertain. The post-transcriptional factor CsrA is stated as being the only regulator essential for the use of glycolytic substrates. A dozen enzymes in the central carbon metabolism (CCM) have been reported as potentially controlled by CsrA, but its impact on the CCM functioning has not been demonstrated. Here, a multiscale analysis was performed in a wild-type strain and its isogenic mutant attenuated for CsrA (including growth parameters, gene expression levels, metabolite pools, abundance of enzymes and fluxes). Data integration and regulation analysis showed a coordinated control of the expression of glycolytic enzymes. This also revealed the imbalance of metabolite pools in the csrA mutant upper glycolysis, before the phosphofructokinase PfkA step. This imbalance is associated with a glucose-phosphate stress. Restoring PfkA activity in the csrA mutant strain suppressed this stress and increased the mutant growth rate on glucose. Thus, the carbon storage regulator system is essential for the effective functioning of the upper glycolysis mainly through its control of PfkA. This work demonstrates the pivotal role of post-transcriptional regulation to shape the carbon metabolism.
Published: 2016

7. Collaborative modelling: The future of computational neuroscience?

Author: Andrew P. Davison, Unité de Neurosciences Information et Complexité [Gif sur Yvette] (UNIC), Centre National de la Recherche Scientifique (CNRS), and Institut de Neurobiologie Alfred Fessard (INAF)
Subjects: Computer science, Models, Neurological, Neuroscience (miscellaneous), MESH: Information Dissemination, MESH: Software, 03 medical and health sciences, 0302 clinical medicine, MESH: Computer Simulation, MESH: Models, Neurological, Component (UML), MESH: Cooperative Behavior, Biological neural network, Animals, Humans, Computer Simulation, MESH: Animals, Cooperative Behavior, 030304 developmental biology, Network model, 0303 health sciences, MESH: Humans, Computational neuroscience, Quantitative Biology::Neurons and Cognition, Information Dissemination, business.industry, Systems Biology, Neurosciences, MESH: Neurosciences, Data science, MESH: Nerve Net, MESH: Systems Biology, MESH: Programming Languages, Programming Languages, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Artificial intelligence, Cooperative behavior, Nerve Net, business, Software, 030217 neurology & neurosurgery
Abstract: International audience; Given the complexity of biological neural circuits and of their component cells and synapses, building and simulating robust, well-validated, detailed models increasingly surpasses the resources of an individual researcher or small research group. In this article, I will briefly review possible solutions to this problem, argue for open, collaborative modelling as the optimal solution for advancing neuroscience knowledge, and identify potential bottlenecks and possible solutions.
Published: 2012

8. Quantifying domain-ligand affinities and specificities by high-throughput holdup assay

Author: Yves Nominé, Sebastian Charbonnier, Patricia Cassonnet, Anne Forster, Julie Abdat, Marilyne Blémont, Nicolas Wolff, Kevin Ricquier, Renaud Vincentelli, Katja Luck, Juline Poirson, Marie-Laure Straub, Jérôme Reboul, Jolanta Polanowska, Jeremy Turchetto, Gilles Travé, Francois, Jean-Paul Borg, Yves Jacob, Murielle Masson, Architecture et fonction des macromolécules biologiques (AFMB), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA), Biotechnologie et signalisation cellulaire (BSC), Université de Strasbourg (UNISTRA)-Institut de recherche de l'Ecole de biotechnologie de Strasbourg (IREBS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Résonance Magnétique Nucléaire des Biomolécules, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Equipe labellisée Ligue contre le Cancer, Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), This work was supported by institutional support from CNRS, Université de Strasbourg, Infrastructures Biologie Santé et Agronomie (Ibisa) and grants from the Association pour la Recherche contre le Cancer (ARC) (no. 3171), Agence Nationale de la Recherche (ANR-MIME-2007 EPI-HPV-3D), US National Institutes of Health (NIH grant R01CA134737), Ligue contre le Cancer and Alsace contre le Cancer. K.L. was supported by grants of Région Alsace and ARC. J. Poirson was supported by a grant of the Ligue Régionale contre le Cancer. J.-P.B. is supported in part by grants from La Ligue Contre le Cancer, Institut paoli-Calmettes, Cancéropôle PACA and SIRIC (INCa-DGOS-Inserm 6038). J.-P.B. is supported as a scholar of Institut Universitaire de France, SIRIC 6038United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) R01CA134737, We thank the CCSB (Harvard University) for kindly providing clones of the human ORFeome, and T. Rolland, M.A. Delsuc, D. Altschuh, B. Kieffer, A. Dejaegere and all members of the 'Oncoproteins' team for helpful discussions and advice., ANR-07-MIME-0009,EPI-HPV-3D,Vers la compréhension fonctionelle et structurale de l'interactome cellulaire des protéines précoces des Papilloma Virus Humains.(2007), Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Wolff, Nicolas, Microbiologie, immunologie et maladies émergentes - Vers la compréhension fonctionelle et structurale de l'interactome cellulaire des protéines précoces des Papilloma Virus Humains. - - EPI-HPV-3D2007 - ANR-07-MIME-0009 - MIME - VALID, Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut de recherche de l'Ecole de biotechnologie de Strasbourg (IREBS), and Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris]
Subjects: Proteome, Protein Conformation, MESH: PDZ Domains, MESH: DNA-Binding Proteins / chemistry, [SDV]Life Sciences [q-bio], Amino Acid Motifs, PDZ Domains, Ligands, Biochemistry, MESH: Amino Acid Motifs, Protein structure, MESH: Protein Conformation, Protein Interaction Mapping, MESH: Ligands, [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], ComputingMilieux_MISCELLANEOUS, MESH: Oncogene Proteins, Viral / chemistry, MESH: High-Throughput Screening Assays, 0303 health sciences, Chromatography, MESH: Kinetics, Systems Biology, 030302 biochemistry & molecular biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV] Life Sciences [q-bio], DNA-Binding Proteins, MESH: Proteome, [SDV.BBM.BS]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], MESH: Systems Biology, MESH: Proteins / chemistry, Biotechnology, MESH: Protein Interaction Mapping / methods, High-throughput screening, PDZ domain, Computational biology, Biology, MESH: Chromatography, DNA-binding protein, Article, Protein–protein interaction, 03 medical and health sciences, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Humans, Short linear motif, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], MESH: Repressor Proteins / chemistry, Molecular Biology, 030304 developmental biology, MESH: Humans, Proteins, Cell Biology, Oncogene Proteins, Viral, Affinities, Molecular biology, High-Throughput Screening Assays, Repressor Proteins, Kinetics
Abstract: Many protein interactions are mediated by small linear motifs interacting specifically with defined families of globular domains. Quantifying the specificity of a motif requires measuring and comparing its binding affinities to all its putative target domains. To this aim, we developed the high-throughput holdup assay, a chromatographic approach that can measure up to a thousand domain-motif equilibrium binding affinities per day. Extracts of overexpressed domains are incubated with peptide-coated resins and subjected to filtration. Binding affinities are deduced from microfluidic capillary electrophoresis of flow-throughs. After benchmarking the approach on 210 PDZ-peptide pairs with known affinities, we determined the affinities of two viral PDZ-binding motifs derived from Human Papillomavirus E6 oncoproteins for 209 PDZ domains covering 79% of the human PDZome. We obtained exquisite sequence-dependent binding profiles, describing quantitatively the PDZome recognition specificity of each motif. This approach, applicable to many categories of domain-ligand interactions, has a wide potential for quantifying the specificities of interactomes.
Published: 2015

9. Statistical analysis of molecule colocalization in bioimaging

Author: Thibault, Lagache, Nathalie, Sauvonnet, Lydia, Danglot, Jean-Christophe, Olivo-Marin, Analyse d'images biologiques - Biological Image Analysis (BIA), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Pathogénie microbienne moléculaire, Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Jacques Monod (IJM (UMR_7592)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Trafic Membranaire et Morphogenèse Neuronale & Epithéliale, Institut National de la Santé et de la Recherche Médicale (INSERM), Bourse Roux and PTR 387 from Institut Pasteur, Institut pour la Recherche sur la Moelle et l'Encéphale, ANR-10-LABX-0062,IBEID,Integrative Biology of Emerging Infectious Diseases(2010), ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Analyse d'images biologiques - BioImage Analysis (AIQ), ANR-10-LABX-62-IBEID,IBEID,Laboratoire d'Excellence 'Integrative Biology of Emerging Infectious Diseases'(2010), and ANR-10-INBS-04-01/10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010)
Subjects: Microscopy, Confocal, Systems Biology, quantitative measurements, Computational Biology, MESH: Algorithms, MESH: Microscopy, Fluorescence, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, MESH: Image Processing, Computer-Assisted, colocalization, spatial statistics, Microscopy, Fluorescence, MESH: Computer Simulation, MESH: Systems Biology, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, endocytosis, MESH: Microscopy, Confocal, Computer Simulation, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH: Image Interpretation, Computer-Assisted, Algorithms, light microscopy, MESH: Computational Biology
Abstract: International audience; The quantitative analysis of molecule interactions in bioimaging is key for understanding the molecular orchestration of cellular processes and is generally achieved through the study of the spatial colocalization between the different populations of molecules. Colocalization methods are traditionally divided into pixel-based methods that measure global correlation coefficients from the overlap between pixel intensities in different color channels, and object-based methods that first segment molecule spots and then analyze their spatial distributions with second-order statistics. Here, we present a review of such colocalization methods and give a quantitative comparison of their relative merits in different types of biological applications and contexts. We show on synthetic and biological images that object-based methods are more robust statistically than pixel-based methods, and allow moreover to quantify accurately the number of colocalized molecules.
Published: 2015

10. A systems biology approach to the hepatic role of the oxysterol receptor LXR in the regulation of lipogenesis highlights a cross-talk with PPARα

Author: Stefania Murzilli, Frédéric Lasserre, Jean-Marc A. Lobaccaro, Normand Podechard, Arnaud Polizzi, Justine Bertrand-Michel, Thierry Pineau, Chiara Di Lisio, Alexandra Montagner, Pascal G.P. Martin, Nicolas Loiseau, Hervé Guillou, Simon Ducheix, Simona D'Amore, Aurélien Pommier, ToxAlim (ToxAlim), Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT), Stress, membrane, signalisation, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Toxicologie Intégrative & Métabolisme (ToxAlim-TIM), Génétique, Reproduction et Développement (GReD), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Unité de Nutrition Humaine (UNH), Institut National de la Recherche Agronomique (INRA)-Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université, Laboratory of Lipid Metabolism and Cancer, Consorzio Mario NegriSud, MetaToul Lipidomics, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-MetaboHUB-MetaToul, MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-MetaboHUB-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Physiopathologie Toulouse Purpan (CPTP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), INRA-FORMAS France-Sweden cooperative program, Region Midi-Pyrenees, ANR project (Crisalis), Ministere de l'Enseignement Superieur et de la Recherche, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Université d'Angers (UA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Institut National de la Santé et de la Recherche Médicale (INSERM), Université d'Auvergne - Clermont-Ferrand I (UdA)-Clermont Université-Institut National de la Recherche Agronomique (INRA), Plateau MetaToul-LIPIDOMIQUE = MetaToul-Lipidomics, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-MetaToul-MetaboHUB, Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Génétique, Reproduction et Développement - Clermont Auvergne (GReD), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Clermont Auvergne (UCA)-Centre National de la Recherche Scientifique (CNRS), Clermont Université-Université d'Auvergne - Clermont-Ferrand I (UdA)-Institut National de la Recherche Agronomique (INRA), Plateau MetaToul-LIPIDOMIQUE, Institut National de la Santé et de la Recherche Médicale (INSERM)-MetaToul, Plateau de lipidomique, Centre de Physiopathologie Toulouse Purpan ex IFR 30 et IFR 150 (CPTP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Le Corre, Morgane, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut de recherche en santé, environnement et travail (Irset), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-MetaboHUB-MetaToul, Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université d'Angers (UA)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université d'Angers (UA)
Subjects: Male, Steatosis, Hydrocarbons, Fluorinated, MESH: Sulfonamides, Peroxisome proliferator-activated receptor, [SDV.GEN] Life Sciences [q-bio]/Genetics, MESH: Protein Isoforms, Ligands, Biochemistry, PPARα, Mice, 0302 clinical medicine, Cytochrome P-450 Enzyme System, Fenofibrate, MESH: Ligands, Protein Isoforms, MESH: Animals, MESH: PPAR alpha, Beta oxidation, Liver X Receptors, Oligonucleotide Array Sequence Analysis, chemistry.chemical_classification, 0303 health sciences, Sulfonamides, Systems Biology, Fatty Acids, General Medicine, Orphan Nuclear Receptors, MESH: Fatty Acids, Liver, MESH: Orphan Nuclear Receptors, 030220 oncology & carcinogenesis, MESH: Systems Biology, Lipogenesis, MESH: Cytochrome P-450 Enzyme System, lipids (amino acids, peptides, and proteins), LXR, Transcriptional Activation, medicine.medical_specialty, Oxysterol, MESH: Mice, Transgenic, MESH: Receptor Cross-Talk, Mice, Transgenic, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, Retinoid X receptor, 03 medical and health sciences, Internal medicine, medicine, Animals, PPAR alpha, Cytochrome P450 Family 4, Liver X receptor, [SDV.BC] Life Sciences [q-bio]/Cellular Biology, MESH: Mice, 030304 developmental biology, MESH: Lipogenesis, [SDV.GEN]Life Sciences [q-bio]/Genetics, Fatty acid, Receptor Cross-Talk, MESH: Fenofibrate, MESH: Male, MESH: Hydrocarbons, Fluorinated, Endocrinology, Nuclear receptor, chemistry, MESH: Oligonucleotide Array Sequence Analysis, MESH: Transcriptional Activation, MESH: Liver
Abstract: International audience; The Liver X Receptors (LXRs) α and β and the Peroxisome Proliferator-Activated Receptor α (PPARα) are transcription factors that belong to class II nuclear receptors. They drive the expression of genes involved in hepatic lipid homeostasis and therefore are important targets for the prevention and treatment of nonalcoholic fatty liver disease (NAFLD). LXRs and PPARα are regulated by endogenous ligands, oxysterols and fatty acid derived molecules, respectively. In the liver, pharmacological activation of LXRs leads to the over-expression of genes involved in de novo lipogenesis, while PPARα is critical for fatty acid catabolism in nutrient deprivation. Even if these two nuclear receptors seemed to play opposite parts, recent studies have highlighted that PPARα also influence the expression of genes involved in fatty acids synthesis. In this study, we used pharmacological approaches and genetically engineered mice to investigate the cross-talk between LXRs and PPARα in the regulation of genes responsible for lipogenesis. We first investigated the effect of T0901317 and fenofibrate, two synthetic agonists of LXRs and PPARα, respectively. As expected, T0901317 and fenofibrate induce expression of genes involved LXR-dependent and PPARα-dependent lipogenic responses. Considering such overlapping effect, we then tested whether LXR agonist may influence PPARα driven response and vice versa. We show that the lack of PPARα does not influence the effects of T0901317 on lipogenic genes expression. However, PPARα deficiency prevents the up-regulation of genes involved in ω-hydroxylation that are induced by the LXR agonist. In addition, over-expression of lipogenic genes in response to fenofibrate is decreased in LXR knockout mice as well as the expression of PPARα target genes involved in fatty acid oxidation. Altogether, our work provides in vivo evidence for a central interconnection between nuclear receptors that drive hepatic lipid metabolism in response to oxysterol and fatty acids.
Published: 2013

11. Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism

Author: Darren M. Wells, Benjamin Péret, Tom Beeckman, Ranjan Swarup, Karin Ljung, Tony P. Pridmore, Teva Vernoux, John R. King, Jonathan M. Garibaldi, Kris Vissenberg, Andrew P. French, Ilkka Sairanen, Mark Estelle, Géraldine Brunoud, Jianyong Sun, Michael Wilson, Ethel Mendocilla Sato, Malcolm J. Bennett, Alistair M. Middleton, Markus R. Owen, Geraint Parry, Leah R. Band, Antoine Larrieu, Marina Oliva, T. Charlie Hodgman, Centre for Plant Integrative Biology [Nothingham] (CPIB), University of Nottingham, UK (UON), Reproduction et développement des plantes (RDP), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Division of Biology [La Jolla], University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Umea Plant Science Center (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences (SLU)-Swedish University of Agricultural Sciences (SLU), Center for Plant Systems Biology (PSB Center), Vlaams Instituut voor Biotechnologie [Ghent, Belgique] (VIB), Biotechnology and Biological Sciences Research Council, Engineering and Physical Sciences Research Council, European Community [PIEF-GA-2008-220506], National Institutes of Health and Howard Hughes Medical Institute, Research Foundation Flanders, Belgian Scientific policy, Human Frontier Science Program Organization, Agence National de la Recherche, École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), University of California-University of California, Umea Plant Science Center, Swedish University of Agricultural Sciences (SLU)-Department of Forest Genetics and Plant Physiology, Department of Plant Systems Biology, State University of Ghent, Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Lyon (ENS Lyon)
Subjects: 0106 biological sciences, Root growth, MESH: Signal Transduction, Plant growth, Time Factors, MESH: Plant Roots, 01 natural sciences, Plant Roots, MESH: Dose-Response Relationship, Drug, Arabidopsis, heterocyclic compounds, MESH: Arabidopsis, MESH: Models, Theoretical, GENE-EXPRESSION, MESH: Gravitropism, chemistry.chemical_classification, 0303 health sciences, Multidisciplinary, biology, MESH: Kinetics, AUX/IAA PROTEINS, MESH: Plant Physiological Phenomena, food and beverages, systems biology, Biological Sciences, ARABIDOPSIS, MESH: Systems Biology, GROWTH, Engineering sciences. Technology, Signal Transduction, MESH: Indoleacetic Acids, Gravitropism, ENDOPLASMIC-RETICULUM, Growth regulator, Stimulus (physiology), Environment, BOX PROTEIN TIR1, Models, Biological, 03 medical and health sciences, GRAVITY, Auxin, Botany, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, MESH: Environment, Biology, Plant Physiological Phenomena, SEDIMENTATION KINETICS, 030304 developmental biology, Dose-Response Relationship, Drug, Indoleacetic Acids, fungi, MESH: Time Factors, MESH: Models, Biological, Models, Theoretical, biology.organism_classification, environmental sensing, TRANSPORT, Root gravitropism, Kinetics, chemistry, CELLS, Biophysics, 010606 plant biology & botany
Abstract: International audience; Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40° to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.
Published: 2012

12. A metabolomic and systems biology perspective on the brain of the Fragile X syndrome mouse model

Author: Vincent Navratil, Maria Vincenza Catania, Laetitia Davidovic, Carmela M. Bonaccorso, Barbara Bardoni, Marc-Emmanuel Dumas, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institut de pharmacologie moléculaire et cellulaire (IPMC), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences Analytiques (ISA), Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Pôle Rhône Alpes de Bioinformatique, Université de Lyon, Oasi Maria SS Institute for Research on Mental Retardation and Brain Aging, Oasi Maria SS Institute, Institute of Neurological Sciences (CNR), National Research Council [Italy] (CNR), Department of Surgery and Cancer, Imperial College London, Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ), Institut de pharmacologie moléculaire et cellulaire ( IPMC ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), Institut des Sciences Analytiques ( ISA ), École normale supérieure - Lyon ( ENS Lyon ) -Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique ( CNRS ), Institute of Neurological Sciences ( CNR ), and National Research Council [Italy] ( CNR )
Subjects: Method, MESH : Brain Chemistry, Interactome, MESH: Mice, Knockout, Fragile X Mental Retardation Protein, Mice, 0302 clinical medicine, Drug Delivery Systems, Intellectual disability, MESH: Animals, MESH: Brain Chemistry, Genetics (clinical), Genetics, Mice, Knockout, 0303 health sciences, Systems Biology, MESH : Metabolome, Brain, MESH : Fragile X Mental Retardation Protein, Fragile X syndrome, MESH : Fragile X Syndrome, MESH: Systems Biology, Metabolome, MESH : Drug Delivery Systems, MESH: Fragile X Syndrome, MESH: Metabolome, congenital, hereditary, and neonatal diseases and abnormalities, Systems biology, MESH: Drug Delivery Systems, Computational biology, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, Biology, MESH: Fragile X Mental Retardation Protein, 03 medical and health sciences, MESH: Brain, Metabolomics, Neurochemical, MESH : Mice, medicine, Gene silencing, Animals, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH: Mice, 030304 developmental biology, MESH : Systems Biology, Brain Chemistry, MESH: Humans, [ SDV.BC ] Life Sciences [q-bio]/Cellular Biology, MESH : Humans, MESH: Biological Markers, medicine.disease, MESH : Disease Models, Animal, MESH : Biological Markers, Disease Models, Animal, MESH : Brain, Fragile X Syndrome, MESH : Mice, Knockout, MESH : Animals, MESH: Disease Models, Animal, 030217 neurology & neurosurgery, Biomarkers
Abstract: Fragile X syndrome (FXS) is the first cause of inherited intellectual disability, due to the silencing of the X-linked Fragile X Mental Retardation 1 gene encoding the RNA-binding protein FMRP. While extensive studies have focused on the cellular and molecular basis of FXS, neither human Fragile X patients nor the mouse model of FXS—the Fmr1-null mouse—have been profiled systematically at the metabolic and neurochemical level to provide a complementary perspective on the current, yet scattered, knowledge of FXS. Using proton high-resolution magic angle spinning nuclear magnetic resonance (1H HR-MAS NMR)-based metabolic profiling, we have identified a metabolic signature and biomarkers associated with FXS in various brain regions of Fmr1-deficient mice. Our study highlights for the first time that Fmr1 gene inactivation has profound, albeit coordinated consequences in brain metabolism leading to alterations in: (1) neurotransmitter levels, (2) osmoregulation, (3) energy metabolism, and (4) oxidative stress response. To functionally connect Fmr1-deficiency to its metabolic biomarkers, we derived a functional interaction network based on the existing knowledge (literature and databases) and show that the FXS metabolic response is initiated by distinct mRNA targets and proteins interacting with FMRP, and then relayed by numerous regulatory proteins. This novel “integrated metabolome and interactome mapping” (iMIM) approach advantageously unifies novel metabolic findings with previously unrelated knowledge and highlights the contribution of novel cellular pathways to the pathophysiology of FXS. These metabolomic and integrative systems biology strategies will contribute to the development of potential drug targets and novel therapeutic interventions, which will eventually benefit FXS patients.
Published: 2011

13. Protention and retention in biological systems

Author: Maël Montévil, Giuseppe Longo, Laboratoire d'informatique de l'école normale supérieure (LIENS), Centre National de la Recherche Scientifique (CNRS) - École normale supérieure - Paris (ENS Paris), Ecole Doctorale Frontières du Vivant - Programme Bettencourt (FdV), Université Paris Descartes - Paris 5 (UPD5) - PRES Sorbonne Paris Cité, Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Centre de recherche en épistémologie appliquée (CREA), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Matière et Systèmes Complexes (MSC (UMR_7057)), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Paris 1 Panthéon-Sorbonne (UP1)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), La République des savoirs : Lettres, Sciences, Philosophie, Collège de France (CdF)-Centre National de la Recherche Scientifique (CNRS)-Département de Philosophie - ENS Paris, École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC)
Subjects: Statistics and Probability, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], retention, protention, MESH: Neurons, MESH: Cognition, Models, Biological, Relaxation phenomena, 03 medical and health sciences, 0302 clinical medicine, Cognition, Memory, MESH: Biology, Frame (artificial intelligence), Animals, Humans, MESH: Animals, MESH: Memory, MESH: Models, Theoretical, Biology, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Simple (philosophy), Cognitive science, Neurons, 0303 health sciences, MESH: Humans, Scope (project management), Memory and Cognition, [SDV.OT] Life Sciences [q-bio]/Other [q-bio.OT], Applied Mathematics, Systems Biology, biological time, Representation (systemics), MESH: Mathematics, MESH: Models, Biological, Models, Theoretical, Quantitative Biology - Other Quantitative Biology, Focus (linguistics), Anticipation (artificial intelligence), MESH: Systems Biology, 030217 neurology & neurosurgery, Mathematics
Abstract: This paper proposes an abstract mathematical frame for describing some features of cognitive and biological time. We focus here on the so called "extended present" as a result of protentional and retentional activities (memory and anticipation). Memory, as retention, is treated in some physical theories (relaxation phenomena, which will inspire our approach), while protention (or anticipation) seems outside the scope of physics. We then suggest a simple functional representation of biological protention. This allows us to introduce the abstract notion of "biological inertia"., Comment: This paper was made possible only as part of an extended collaboration with Francis Bailly (see references), a dear friend and "ma\^itre \'a penser", who contributed to the key ideas. Francis passed away in february 2008: we continue here our inspiring discussions and joint work
Published: 2011

14. A computational framework for gene regulatory network inference that combines multiple methods and datasets

Author: Philipp Antczak, Anna Stincone, Sarah Durant, Francesco Falciani, Andreas Bikfalvi, Rita Gupta, Roy Bicknell, School of Biosciences, University of Birmingham [Birmingham], Institute of Biomedical Research, Mecanismes Moleculaires de l'Angiogenese, Université Sciences et Technologies - Bordeaux 1-Institut National de la Santé et de la Recherche Médicale (INSERM), The work described in this paper was funded by the CRUK grant C8504/A9488 and partially funded by the BBSRC grant BBC5151041. AS is a recipient of a Darwin Trust PhD fellowship and PA is a recipient of a BBSRC PhD studentship., BMC, Ed., and Université Sciences et Technologies - Bordeaux 1 (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: MESH: Hydrogen-Ion Concentration, Time Factors, Gene regulatory network, Inference, Multiple methods, computer.software_genre, 0302 clinical medicine, Structural Biology, Gene regulatory network inference, Neoplasms, MESH: Gene Silencing, MESH: Neoplasms, Gene Regulatory Networks, MESH: Stress, Physiological, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], MESH: Gene Regulatory Networks, Genetics, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Escherichia coli, Applied Mathematics, Systems Biology, Methodology Article, Hydrogen-Ion Concentration, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Modeling and Simulation, MESH: Systems Biology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Reverse engineering, MESH: Cell Line, Tumor, Systems biology, Biology, Machine learning, Models, Biological, 03 medical and health sciences, Stress, Physiological, Modelling and Simulation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Cell Line, Tumor, Escherichia coli, Humans, Gene Silencing, Molecular Biology, 030304 developmental biology, MESH: Humans, business.industry, MESH: Time Factors, Ode, MESH: Models, Biological, lcsh:Biology (General), Time course, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, 030217 neurology & neurosurgery
Abstract: Background Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. Results This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies. Conclusions The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.
Published: 2011

15. Micropilot: automation of fluorescence microscopy-based imaging for systems biology

Author: Jan Ellenberg, Frank Sieckmann, Urban Liebel, Christian Conrad, Rainer Pepperkok, Arthur D. Edelstein, Tze Heng Tan, Annelie Wünsche, Jutta Bulkescher, Thomas Walter, Fatima Verissimo, Walter, Thomas, European Molecular Biology Laboratory [Heidelberg] (EMBL), and Leica Microsystems CMS GmbH
Subjects: Chromosomal Proteins, Non-Histone, Systems biology, Green Fluorescent Proteins, MESH: Microscopy, Fluorescence, Biology, Biochemistry, Article, 03 medical and health sciences, MESH: Software, 0302 clinical medicine, MESH: Green Fluorescent Proteins, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], MESH: Chromosomal Proteins, Non-Histone, Microscopy, Fluorescence microscope, Humans, Molecular Biology, 030304 developmental biology, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, MESH: Humans, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], business.industry, Systems Biology, Fluorescence recovery after photobleaching, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Cell Biology, Automation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Cell biology, Microscopy, Fluorescence, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Chromobox Protein Homolog 5, MESH: Systems Biology, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Quantitative Microscopy, MESH: HeLa Cells, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, Biological system, Software, 030217 neurology & neurosurgery, MESH: Fluorescence Recovery After Photobleaching, Fluorescence Recovery After Photobleaching, HeLa Cells, Biotechnology
Abstract: International audience; Quantitative microscopy relies on imaging of large cell numbers but is often hampered by time-consuming manual selection of specific cells. The 'Micropilot' software automatically detects cells of interest and launches complex imaging experiments including three-dimensional multicolor time-lapse or fluorescence recovery after photobleaching in live cells. In three independent experimental setups this allowed us to statistically analyze biological processes in detail and is thus a powerful tool for systems biology.
Published: 2011

16. Robustness analysis and behavior discrimination in enzymatic reaction networks

Author: Lucie M. Gattepaille, Eric Fanchon, Oded Maler, Philippe Tracqui, Alexandre Donzé, VERIMAG (VERIMAG - IMAG), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Université Joseph Fourier - Grenoble 1 (UJF), Biologie Computationnelle et Mathématique (TIMC-IMAG-BCM), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), and Dynamique Cellulaire et Tissulaire- Interdisciplinarité, Modèles & Microscopies (TIMC-IMAG-DyCTiM)
Subjects: Boundary detection, Time Factors, [SDV]Life Sciences [q-bio], Monte Carlo method, lcsh:Medicine, Model parameters, Parameter space, Bioinformatics, Biochemistry, MESH: Matrix Metalloproteinase 14, 0302 clinical medicine, Biochemical Simulations, lcsh:Science, MESH: Oscillometry, Bifurcation, 0303 health sciences, Numerical Analysis, Multidisciplinary, Calculus, Systems Biology, Enzymes, MESH: Systems Biology, Matrix Metalloproteinase 2, Biological system, Monte Carlo Method, Algorithms, Research Article, Differential equation, MESH: Cell Physiological Phenomena, Systems biology, High Level Languages, MESH: Enzymes, MESH: Algorithms, MESH: Monte Carlo Method, Models, Biological, Cell Physiological Phenomena, 03 medical and health sciences, Fuzzy Logic, Oscillometry, Differential Equations, Matrix Metalloproteinase 14, Temporal logic, Biology, 030304 developmental biology, Tissue Inhibitor of Metalloproteinase-2, Models, Statistical, MESH: Biochemistry, lcsh:R, MESH: Time Factors, MESH: Models, Biological, Computational Biology, Computing Methods, MESH: Matrix Metalloproteinase 2, MESH: Tissue Inhibitor of Metalloproteinase-2, Nonlinear Dynamics, Computer Science, lcsh:Q, Programming Languages, 030217 neurology & neurosurgery, Mathematics, MESH: Models, Statistical
Abstract: International audience; Characterizing the behavior and robustness of enzymatic networks with numerous variables and unknown parameter values is a major challenge in biology, especially when some enzymes have counter-intuitive properties or switch-like behavior between activation and inhibition. In this paper, we propose new methodological and tool-supported contributions, based on the intuitive formalism of temporal logic, to express in a rigorous manner arbitrarily complex dynamical properties. Our multi-step analysis allows efficient sampling of the parameter space in order to define feasible regions in which the model exhibits imposed or experimentally observed behaviors. In a first step, an algorithmic methodology involving sensitivity analysis is conducted to determine bifurcation thresholds for a limited number of model parameters or initial conditions. In a second step, this boundary detection is supplemented by a global robustness analysis, based on quasi-Monte Carlo approach that takes into account all model parameters. We apply this method to a well-documented enzymatic reaction network describing collagen proteolysis by matrix metalloproteinase MMP2 and membrane type 1 metalloproteinase (MT1-MMP) in the presence of tissue inhibitor of metalloproteinase TIMP2. For this model, our method provides an extended analysis and quantification of network robustness toward paradoxical TIMP2 switching activity between activation or inhibition of MMP2 production. Further implication of our approach is illustrated by demonstrating and analyzing the possible existence of oscillatory behaviors when considering an extended open configuration of the enzymatic network. Notably, we construct bifurcation diagrams that specify key parameters values controlling the co-existence of stable steady and non-steady oscillatory proteolytic dynamics.
Published: 2011

17. Systems biology of infectious diseases: a focus on fungal infections

Author: Michael Bromley, Philippe Pierre, Ivo Gut, Lisa Rizzetto, Rodrigo Santamaría, Wanseon Lee, Duccio Cavalieri, Brian Miller, Misha Kapushesky, Teresa Zelante, Luigina Romani, Manuel A. S. Santos, Paul Bowyer, University of Salamanca - Spain, University of Salamanca, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), University of Manchester [Manchester], Università degli Studi di Perugia (UNIPG), European Bioinforrmatics Institute (UK) (EBI), École de biologie industrielle (EBI), AlerGenetica (UK), AlerGenetica, Centre National de Génotypage (CNG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Universidade de Aveiro, Universidad de Salamanca, Università degli Studi di Firenze = University of Florence (UniFI), and Università degli Studi di Perugia = University of Perugia (UNIPG)
Subjects: Proteomics, Antifungal Agents, MESH: Th17 Cells, Disease, MESH: Drug Design, Bioinformatics, MESH: Receptors, Pattern Recognition, MESH: Molecular Targeted Therapy, Candida albicans, Immunology and Allergy, MESH: Animals, MESH: Immune Evasion, MESH: Genetic Variation, Molecular Targeted Therapy, Organism, MESH: Cytokines, 0303 health sciences, MESH: Proteomics, Systems Biology, Hematology, 3. Good health, Drug development, MESH: Systems Biology, Receptors, Pattern Recognition, MESH: Genome, Fungal, Host-Pathogen Interactions, MESH: Communicable Diseases, [SDV.IMM]Life Sciences [q-bio]/Immunology, Cytokines, MESH: Aspergillus fumigatus, Genome, Fungal, MESH: Immune Tolerance, Systems biology, Host–pathogen interaction, Immunology, Computational biology, Biology, Communicable Diseases, Underlying infection, 03 medical and health sciences, MESH: Mycoses, Immune Tolerance, Animals, Humans, Immune Evasion, 030304 developmental biology, MESH: Humans, 030306 microbiology, MESH: Candida albicans, Aspergillus fumigatus, MESH: Host-Pathogen Interactions, MESH: Biological Markers, Genetic Variation, Th1 Cells, MESH: Antifungal Agents, Microarray Analysis, MESH: Microarray Analysis, MESH: Th1 Cells, Mycoses, Infectious disease (medical specialty), Drug Design, Th17 Cells, Biomarkers
Abstract: International audience; The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.
Published: 2011

18. Systems biology and the origins of life? part II. Are biochemical networks possible ancestors of living systems? networks of catalysed chemical reactions: non-equilibrium, self-organization and evolution

Author: Jacques Ricard, Institut Jacques Monod (IJM (UMR_7592)), and Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Self-organization, Property (philosophy), Biochemical Phenomena, Systems biology, Origin of Life, Integration, MESH: Enzymes, MESH: Algorithms, MESH: Biological Evolution, Emergence, Biology, Non-equilibrium dynamic structure, 01 natural sciences, Catalysis, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Identity (mathematics), MESH: Biogenesis, Proteinoid, Abiogenesis, 0103 physical sciences, Enzyme reactions, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, 010306 general physics, 030304 developmental biology, 0303 health sciences, General Immunology and Microbiology, Systems Biology, Substrate (chemistry), General Medicine, MESH: Catalysis, Biological Evolution, Enzymes, Living systems, Biochemistry, MESH: Metabolic Networks and Pathways, MESH: Systems Biology, Thermodynamics, Networks, General Agricultural and Biological Sciences, Biological system, MESH: Biochemical Processes, Algorithms, Metabolic Networks and Pathways
Abstract: The present article discusses the possibility that catalysed chemical networks can evolve. Even simple enzyme-catalysed chemical reactions can display this property. The example studied is that of a two-substrate proteinoid, or enzyme, reaction displaying random binding of its substrates A and B. The fundamental property of such a system is to display either emergence or integration depending on the respective values of the probabilities that the enzyme has bound one of its substrate regardless it has bound the other substrate, or, specifically, after it has bound the other substrate. There is emergence of information if p(A)>p(AB) and p(B)>p(BA). Conversely, if p(A)
Published: 2010

19. A Computational Model of the Circulating Renin-Angiotensin System and Blood Pressure Regulation

Author: François Guillaud, Patrick Hannaert, Ischémie - Reperfusion en transplatation rénale, and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Poitiers
Subjects: MESH: Models, Cardiovascular, Sympathetic Nervous System, MESH: Renin, Blood Pressure, 030204 cardiovascular system & hematology, Kidney, Renin-Angiotensin System, 0302 clinical medicine, MESH: Renin-Angiotensin System, Renin, MESH: Animals, MESH: Sympathetic Nervous System, General Environmental Science, 0303 health sciences, Chemistry, Angiotensin II, Systems Biology, Applied Mathematics, Models, Cardiovascular, General Medicine, MESH: Blood Pressure, [SDV.SP]Life Sciences [q-bio]/Pharmaceutical sciences, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], medicine.anatomical_structure, MESH: Systems Biology, Circulatory system, [SDV.IB]Life Sciences [q-bio]/Bioengineering, MESH: Angiotensin II, General Agricultural and Biological Sciences, Perfusion, MESH: Juxtaglomerular Apparatus, medicine.medical_specialty, Models, Biological, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, MESH: Computer Simulation, Internal medicine, Renin–angiotensin system, medicine, Animals, Humans, Computer Simulation, 030304 developmental biology, MESH: Sodium, Dietary, MESH: Humans, MESH: Models, Biological, Sodium, Dietary, Juxtaglomerular apparatus, MESH: Kidney, Juxtaglomerular Apparatus, Philosophy, Endocrinology, Blood pressure, Macula densa
Abstract: The renin-angiotensin system (RAS) is critical in sodium and blood pressure (BP) regulation, and in cardiovascular-renal (CVR) diseases and therapeutics. As a contribution to SAPHIR project, we present a realistic computer model of renin production and circulating RAS, integrated into Guyton’s circulatory model (GCM). Juxtaglomerular apparatus, JGA, and Plasma modules were implemented in C ++/M2SL (Multi-formalism Multi-resolution Simulation Library) for fusion with GCM. Matlab© optimization toolboxes were used for parameter identification. In JGA, renin production and granular cells recruitment (GCR) are controlled by perfusion pressure (PP), macula densa (MD), angiotensin II (Ang II), and renal sympathetic activity (RSNA). In Plasma, renin and ACE (angiotensin-converting enzyme) activities are integrated to yield Ang I and II. Model vs. data deviation is given as normalized root mean squared error (nRMSE; n points). Identification: JGA and Plasma parameters were identified against selected experimental data. After fusion with GCM: (1) GCR parameters were identified against Laragh’s PRA-natriuresis nomogram; (2) Renin production parameters were identified against two sets of data ([renin] transients vs. ACE or renin inhibition). Finally, GCR parameters were re-identified vs. Laragh’s nomogram (nRMSE 8%, n = 9). Validation: (1) model BP, PRA and [Ang II] are within reported ranges, and respond physiologically to sodium intake; (2) short-term Ang II infusion induces reported rise in BP and PRA. The modeled circulating RAS, in interaction with an integrated CVR, exhibits a realistic response to BP control maneuvers. This construction will allow for modelling hypertensive and CVR patients, including salt-sensitivity, polymorphisms, and pharmacotherapeutics.
Published: 2010

20. A 2-dimensional Geometry for Biological Time

Author: Francis Bailly, Giuseppe Longo, Maël Montévil, CNRS-Meudon Bellevue, Centre National de la Recherche Scientifique (CNRS), La République des savoirs : Lettres, Sciences, Philosophie, Collège de France (CdF)-Centre National de la Recherche Scientifique (CNRS)-Département de Philosophie - ENS Paris, École normale supérieure - Paris (ENS Paris)-École normale supérieure - Paris (ENS Paris), Matière et Systèmes Complexes (MSC (UMR_7057)), Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut d'Histoire et de Philosophie des Sciences et des Techniques (IHPST), Université Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Centre de recherche en épistémologie appliquée (CREA), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Université Paris 1 Panthéon-Sorbonne (UP1)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'informatique de l'école normale supérieure (LIENS), Centre National de la Recherche Scientifique (CNRS) - École normale supérieure - Paris (ENS Paris), Ecole Doctorale Frontières du Vivant - Programme Bettencourt (FdV), and Université Paris Descartes - Paris 5 (UPD5) - PRES Sorbonne Paris Cité
Subjects: 0106 biological sciences, [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT], Theoretical computer science, Computer science, MESH: Biological Clocks, Quantitative Biology::Tissues and Organs, Biophysics, rate variability, MESH: Biological Evolution, Space (commercial competition), Models, Biological, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, Key point, Theoretical physics, Rhythm, Biological Clocks, allometry, Animals, Humans, Frame (artificial intelligence), MESH: Animals, MESH: Circadian Rhythm, Pulse, Molecular Biology, 030304 developmental biology, 0303 health sciences, MESH: Humans, [SDV.OT] Life Sciences [q-bio]/Other [q-bio.OT], Systems Biology, Representation (systemics), MESH: Mathematics, MESH: Models, Biological, Other Quantitative Biology (q-bio.OT), Biological Evolution, Manifold, Quantitative Biology - Other Quantitative Biology, Circadian Rhythm, MESH: Pulse, heartbeats, biological rhythms, circadian rhythms, FOS: Biological sciences, MESH: Systems Biology, Key (cryptography), Embedding, Mathematics
Abstract: This paper proposes an abstract mathematical frame for describing some features of biological time. The key point is that usual physical (linear) representation of time is insufficient, in our view, for the understanding key phenomena of life, such as rhythms, both physical (circadian, seasonal ...) and properly biological (heart beating, respiration, metabolic ...). In particular, the role of biological rhythms do not seem to have any counterpart in mathematical formalization of physical clocks, which are based on frequencies along the usual (possibly thermodynamical, thus oriented) time. We then suggest a functional representation of biological time by a 2-dimensional manifold as a mathematical frame for accommodating autonomous biological rhythms. The "visual" representation of rhythms so obtained, in particular heart beatings, will provide, by a few examples, hints towards possible applications of our approach to the understanding of interspecific differences or intraspecific pathologies. The 3-dimensional embedding space, needed for purely mathematical reasons, allows to introduce a suitable extra-dimension for "representation time", with a cognitive significance., Presented in an invited Lecture, conference "Biologie e selezioni naturali", Florence, December 4-8, 2009
Published: 2010

21. What makes cells move: requirements and obstacles for spontaneous cell motility

Author: Urszula Hibner, Fabien Binamé, Géraldine Pawlak, Pierre Roux, Institut de Génétique Moléculaire de Montpellier ( IGMM ), Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ), Centre de recherches de biochimie macromoléculaire ( CRBM ), Université Montpellier 1 ( UM1 ) -Université Montpellier 2 - Sciences et Techniques ( UM2 ) -IFR122-Centre National de la Recherche Scientifique ( CNRS ), Institut de Génétique Moléculaire de Montpellier (IGMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre de recherches de biochimie macromoléculaire (CRBM), Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-IFR122-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Association pour la recherche sur le cancer, La Ligue contre le cancer, Agence Nationale de Recherche sur le Sida et les hépatites virales et Institut National du Cancer, Centre National de la Recherche Scientifique ( CNRS ) -Université de Montpellier ( UM ), Laboratoire de Géophysique Interne et Tectonophysique ( LGIT ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Laboratoire Central des Ponts et Chaussées ( LCPC ) -Institut des Sciences de la Terre ( ISTerre ), Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers ( INSU - CNRS ) -Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux ( IFSTTAR ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Savoie Mont Blanc ( USMB [Université de Savoie] [Université de Chambéry] ) -PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux ( IFSTTAR ), Centre de recherche en Biologie Cellulaire (CRBM), and Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)
Subjects: MESH: Signal Transduction, MESH : Hepatocytes, MESH : Models, Biological, Mechanotransduction, Cellular, MESH : Mechanotransduction, Cellular, MESH: Hepatocytes, 0302 clinical medicine, Cell Movement, MESH : Cell Movement, MESH: Animals, Mechanotransduction, MESH: Cell Movement, 0303 health sciences, Amoeboid movement, Systems Biology, Cell Polarity, Cell migration, Molecular machine, Cell biology, 030220 oncology & carcinogenesis, MESH: Systems Biology, MESH : Imaging, Three-Dimensional, MESH: Cell Polarity, MESH : Cell Polarity, Biotechnology, Signal Transduction, Systems biology, Context (language use), MESH: Imaging, Three-Dimensional, Biology, In Vitro Techniques, Models, Biological, MESH: Cell Adhesion, 03 medical and health sciences, Imaging, Three-Dimensional, Cell Adhesion, Animals, Humans, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, Molecular Biology, [ SDV.BBM ] Life Sciences [q-bio]/Biochemistry, Molecular Biology, 030304 developmental biology, MESH : Systems Biology, MESH : Signal Transduction, MESH: Humans, [ SDU.STU.GM ] Sciences of the Universe [physics]/Earth Sciences/Geomorphology, MESH: Mechanotransduction, Cellular, Mesenchymal stem cell, MESH : Humans, MESH: Models, Biological, Multicellular organism, MESH : Cell Adhesion, Hepatocytes, MESH : Animals
Abstract: International audience; Movement of individual cells and of cellular cohorts, chains or sheets requires physical forces that are established through interactions of cells with their environment. In vivo, migration occurs extensively during embryonic development and in adults during wound healing and tumorigenesis. In order to identify the molecular events involved in cell movement, in vitro systems have been developed. These have contributed to the definition of a number of molecular pathways put into play in the course of migratory behaviours, such as mesenchymal and amoeboid movement. More recently, our knowledge of migratory modes has been enriched by analyses of cells exploring and moving through three-dimensional (3D) matrices. While the cells' morphologies differ in 2D and 3D environments, the basic mechanisms that put a cellular body into motion are remarkably similar. Thus, in both 2D and 3D, the polarity of the migrating cell is initially defined by a specific subcellular localization of signalling molecules and components of molecular machines required for motion. While the polarization can be initiated either in response to extracellular signalling or be a chance occurrence, it is reinforced and sustained by positive feedback loops of signalling molecules. Second, adhesion to a substratum is necessary to generate forces that will propel the cell engaged in either mesenchymal or ameboid migration. For collective cell movement, intercellular coordination constitutes an additional requirement: a cell cohort remains stationary if individual cells pull in opposite directions. Finally, the availability of space to move into is a general requirement to set cells into motion. Lack of free space is probably the main obstacle for migration of most healthy cells in an adult multicellular organism. Thus, the requirements for cell movement are both intrinsic to the cell, involving coordinated signalling and interactions with molecular machines, and extrinsic, imposed by the physicochemical nature of the environment. In particular, the geometry and stiffness of the support act on a range of signalling pathways that induce specific cell migratory responses. These issues are discussed in the present review in the context of published work and our own data on collective migration of hepatocyte cohorts.
Published: 2010

22. Model of cap-dependent translation initiation in sea urchin: a step towards the eukaryotic translation regulation network

Author: Bellé, Robert, Prigent, Sylvain, Siegel, Anne, Cormier, Patrick, Mer et santé (MS), Station biologique de Roscoff [Roscoff] (SBR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Inverse problems in earth monitoring (ARIANA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Signal, Images et Systèmes (Laboratoire I3S - SIS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique
Subjects: RNA Caps, MESH: Peptide Chain Initiation, Translational, Models, Genetic, Systems Biology, Reproducibility of Results, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Sea Urchins, MESH: Reproducibility of Results, MESH: RNA Caps, MESH: Computer Simulation, Fertilization, Sea Urchins, MESH: Systems Biology, Animals, MESH: Protein Binding, Computer Simulation, MESH: Animals, MESH: Fertilization, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, RNA, Messenger, MESH: Models, Genetic, Peptide Chain Initiation, Translational, Protein Binding, MESH: RNA, Messenger
Abstract: International audience; The large and rapid increase in the rate of protein synthesis following fertilization of the sea urchin egg has long been a paradigm of translational control, an important component of the regulation of gene expression in cells. This translational up-regulation is linked to physiological changes that occur upon fertilization and is necessary for entry into first cell division cycle. Accumulated knowledge on cap-dependent initiation of translation makes it suited and timely to start integrating the data into a system view of biological functions. Using a programming environment for system biology coupled with model validation (named Biocham), we have built an integrative model for cap-dependent initiation of translation. The model is described by abstract rules. It contains 51 reactions involved in 74 molecular complexes. The model proved to be coherent with existing knowledge by using queries based on computational tree logic (CTL) as well as Boolean simulations. The model could simulate the change in translation occurring at fertilization in the sea urchin model. It could also be coupled with an existing model designed for cell-cycle control. Therefore, the cap-dependent translation initiation model can be considered a first step towards the eukaryotic translation regulation network.
Published: 2010

23. Evolution of metabolic network organization

Author: Benno Schwikowski, Danail Bonchev, Gregory A. Buck, Aurélien J. Mazurie, Center for the Study of Biological Complexity, Virginia Commonwealth University (VCU), Biologie systémique - Systems Biology, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics and Applied Mathematics, Department of Microbiology and Immunology, This work was supported by grants R01AI050196 and R01AI055347 from the National Institutes of Health (Buck, PI), and grant LSHG-CT-2006-037469 fromthe European Union (Schwikowski, PI)., and Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Gene regulatory network, Metabolic network, Structural Biology, Cell Movement, Protein Interaction Mapping, Cluster coefficient, Gene Regulatory Networks, MESH: Cell Movement, lcsh:QH301-705.5, MESH: Evolution, Molecular, MESH: Gene Regulatory Networks, 0303 health sciences, Applied Mathematics, Systems Biology, 030302 biochemistry & molecular biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Modeling and Simulation, MESH: Systems Biology, Algorithms, Metabolic Networks and Pathways, MESH: Computational Biology, Systems biology, MESH: Algorithms, Computational biology, Biology, Models, Biological, Evolution, Molecular, 03 medical and health sciences, Betweenness centrality, Artificial Intelligence, Modelling and Simulation, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Research article, MESH: Artificial Intelligence, Humans, Set (psychology), Molecular Biology, 030304 developmental biology, MESH: Humans, Bacteria, MESH: Protein Interaction Mapping, MESH: Models, Biological, Computational Biology, Cell movement, MESH: Bacteria, lcsh:Biology (General), MESH: Metabolic Networks and Pathways, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Centrality
Abstract: Background Comparison of metabolic networks across species is a key to understanding how evolutionary pressures shape these networks. By selecting taxa representative of different lineages or lifestyles and using a comprehensive set of descriptors of the structure and complexity of their metabolic networks, one can highlight both qualitative and quantitative differences in the metabolic organization of species subject to distinct evolutionary paths or environmental constraints. Results We used a novel representation of metabolic networks, termed network of interacting pathways or NIP, to focus on the modular, high-level organization of the metabolic capabilities of the cell. Using machine learning techniques we identified the most relevant aspects of cellular organization that change under evolutionary pressures. We considered the transitions from prokarya to eukarya (with a focus on the transitions among the archaea, bacteria and eukarya), from unicellular to multicellular eukarya, from free living to host-associated bacteria, from anaerobic to aerobic, as well as the acquisition of cell motility or growth in an environment of various levels of salinity or temperature. Intuitively, we expect organisms with more complex lifestyles to have more complex and robust metabolic networks. Here we demonstrate for the first time that such organisms are not only characterized by larger, denser networks of metabolic pathways but also have more efficiently organized cross communications, as revealed by subtle changes in network topology. These changes are unevenly distributed among metabolic pathways, with specific categories of pathways being promoted to more central locations as an answer to environmental constraints. Conclusions Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell. This approach allows the identification and quantification of those changes, and provides an overview of the evolution of intracellular systems.
Published: 2009

24. Knowledge based identification of essential signaling from genome-scale siRNA experiments

Author: Carol A. Rohl, Nolwenn LeMeur, Chester Ni, Armand Bankhead, Iliana Sach, Robert Gentleman, Marc Ferrer, Mark J Kruger, Rosetta Inpharmatics LLC, Merck & Co. Inc, Biological systems and models, bioinformatics and sequences (SYMBIOSE), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Automated Biotechnology, Computational Biology Program, Fred Hutchinson Cancer Research Center [Seattle] (FHCRC), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and BMC, Ed.
Subjects: MESH: Signal Transduction, Gene Expression, Interactome, 0302 clinical medicine, RNA interference, Structural Biology, MESH: RNA, Small Interfering, MESH: Proteins, RNA, Small Interfering, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Genetics, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Effector, Applied Mathematics, Systems Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, 030220 oncology & carcinogenesis, Modeling and Simulation, MESH: Systems Biology, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Algorithms, Protein Binding, Signal Transduction, Research Article, MESH: Gene Expression, Systems biology, MESH: Algorithms, Biology, Protein–protein interaction, Cell Line, 03 medical and health sciences, Human interactome, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Modelling and Simulation, MESH: Protein Binding, Humans, Molecular Biology, MESH: Genome, Human, 030304 developmental biology, MESH: Humans, Genome, Human, Robustness (evolution), Proteins, MESH: Cell Line, lcsh:Biology (General), Human genome, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract: Background A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits. Results We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction. Conclusion PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.
Published: 2009

25. Modelling methodology in physiopathology

Author: Jean-Pierre Boissel, Guillemette Chapuisat, Benjamin Ribba, Marie-Aimée Dronne, Emmanuel Grenier, Evaluation et modélisation des effets thérapeutiques, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Numerical Medicine (NUMED), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Unité de Mathématiques Pures et Appliquées (UMPA-ENSL), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS), Unité de Mathématiques Pures et Appliquées (UMPA-ENSL), École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques et de Leurs Applications (CMLA), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and École normale supérieure de Lyon (ENS de Lyon)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Process (engineering), Biophysics, Complex system, computer.software_genre, Models, Biological, 03 medical and health sciences, 0302 clinical medicine, MESH: Computer Simulation, Order (exchange), MESH: Disease, Animals, Humans, Computer Simulation, Disease, Model development, MESH: Animals, Representation (mathematics), Molecular Biology, 030304 developmental biology, 0303 health sciences, MESH: Humans, Systems Biology, MESH: Models, Biological, Evidence-based medicine, Missing data, Industrial engineering, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Range (mathematics), MESH: Systems Biology, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer, 030217 neurology & neurosurgery
Abstract: International audience; Diseases are complex systems. Modelling them, i.e. systems physiopathology, is a quite demanding, complicated, multidimensional, multiscale process. As such, in order to achieve the goal of the model and further to optimise a rather-time and resource-consuming process, a relevant and easy to practice methodology is required. It includes guidance for validation. Also, the model development should be managed as a complicated process, along a strategy which has been elaborated in the beginning. It should be flexible enough to meet every case. A model is a representation of the available knowledge. All available knowledge does not have the same level of evidence and, further, there is a large variability of the values of all parameters (e.g. affinity constant or ionic current) across the literature. In addition, in a complex biological system there are always values lacking for a few or sometimes many parameters. All these three aspects are sources of uncertainty on the range of validity of the models and raise unsolved problems for designing a relevant model. Tools and techniques for integrating the parameter range of experimental values, level of evidence and missing data are needed.
Published: 2008

26. SAPHIR: a physiome core model of body fluid homeostasis and blood pressure regulation

Author: Pierre Mazière, Fariza Tahi, Alfredo Hernandez, Patrick Hannaert, Pierre Baconnier, Julie Fontecave, Ronald J. White, François Guillaud, Jean-Pierre Françoise, S. Randall Thomas, Virginie Le Rolle, Informatique, Biologie Intégrative et Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Physiologie cardio-Respiratoire Expérimentale Théorique et Appliquée (TIMC-IMAG-PRETA), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Jacques-Louis Lions (LJLL), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Ischémie - Reperfusion en transplatation rénale, Université de Poitiers-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), USRA Division of Space Life Sciences, Universities Space Research Association (USRA), ANR-06-BYOS-0007,SAPHIR,:a Systems Approach for PHysiological Integration of Renal, cardiac and respiratory function(2006), Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM), Davesne, Frédéric, and Biologie systémique (BIOSYS) - :a Systems Approach for PHysiological Integration of Renal, cardiac and respiratory function - - SAPHIR2006 - ANR-06-BYOS-0007 - BIOSYS - VALID
Subjects: MESH: Models, Cardiovascular, [SDV.MHEP.PHY] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Computer science, Physiology, Knowledge Bases, General Physics and Astronomy, Blood Pressure, 030204 cardiovascular system & hematology, Ontology (information science), [SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology, MESH: Body Fluids, 0302 clinical medicine, [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], Homeostasis, Respiratory function, multi-scale modelling, Acid-Base Equilibrium, 0303 health sciences, [INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT], Systems Biology, General Engineering, Models, Cardiovascular, Tissue level, blood pressure regulation, MESH: Blood Pressure, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], biomedical toolbox, Body Fluids, MESH: Homeostasis, MESH: Systems Biology, Systems engineering, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Core model, MESH: Knowledge Bases, General Mathematics, Systems biology, [SCCO.COMP]Cognitive science/Computer science, Models, Biological, 03 medical and health sciences, MESH: Computer Simulation, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Humans, Computer Simulation, 030304 developmental biology, [SDV.IB] Life Sciences [q-bio]/Bioengineering, MESH: Physiology, MESH: Humans, physiome, MESH: Models, Biological, Human physiology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, body fluid homeostasis, MESH: Acid-Base Equilibrium, Physiome, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], mathematical model
Abstract: We present the current state of the development of the SAPHIR project (a Systems Approach for PHysiological Integration of Renal, cardiac and respiratory function). The aim is to provide an open-source multi-resolution modelling environment that will permit, at a practical level, a plug-and-play construction of integrated systems models using lumped-parameter components at the organ/tissue level while also allowing focus on cellular- or molecular-level detailed sub-models embedded in the larger core model. Thus, an in silico exploration of gene-to-organ-to-organism scenarios will be possible, while keeping computation time manageable. As a first prototype implementation in this environment, we describe a core model of human physiology targeting the short- and long-term regulation of blood pressure, body fluids and homeostasis of the major solutes. In tandem with the development of the core models, the project involves database implementation and ontology development.
Published: 2008

27. The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

Author: Michael Primig, Frédéric Chalmel, Groupe d'Etude de la Reproduction Chez l'Homme et les Mammiferes (GERHM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Forgeron, Christine
Subjects: MESH: Signal Transduction, Proteome, Data management, Biochemistry, User-Computer Interface, 0302 clinical medicine, Structural Biology, Protein Interaction Mapping, lcsh:QH301-705.5, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], Oligonucleotide Array Sequence Analysis, 0303 health sciences, Biological data, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Systems Biology, Applied Mathematics, Chromosome Mapping, Genome project, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, MESH: Proteome, 030220 oncology & carcinogenesis, MESH: Systems Biology, lcsh:R858-859.7, User interface, DNA microarray, MESH: Computer Graphics, Algorithms, Signal Transduction, Systems biology, MESH: Algorithms, Computational biology, Biology, lcsh:Computer applications to medicine. Medical informatics, Data type, 03 medical and health sciences, Annotation, MESH: Gene Expression Profiling, MESH: Software, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Computer Graphics, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Molecular Biology, 030304 developmental biology, MESH: User-Computer Interface, Information retrieval, business.industry, Gene Expression Profiling, MESH: Molecular Biology, MESH: Protein Interaction Mapping, Systems Integration, lcsh:Biology (General), MESH: Oligonucleotide Array Sequence Analysis, MESH: Systems Integration, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, MESH: Chromosome Mapping, Software
Abstract: Background High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently. Results We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human. Conclusion AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.
Published: 2008

28. Developmental vitamin D deficiency alters the expression of genes encoding mitochondrial, cytoskeletal and synaptic proteins in the adult rat brain

Author: Alan Mackay-Sim, Lionel Almeras, Philippe Benech, John J. McGrath, Angela Patatian, Francois Feron, Darryl W. Eyles, Neurobiologie des interactions cellulaires et neurophysiopathologie - NICN (NICN), Centre National de la Recherche Scientifique (CNRS)-Université de la Méditerranée - Aix-Marseille 2, Technologies avancées pour le génôme et la clinique (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Microélectronique, Electromagnétisme et Photonique (IMEP), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Université Joseph Fourier - Grenoble 1 (UJF), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), Khrestchatisky, Michel, and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Nervous system, Male, MESH: Signal Transduction, Aging, Time Factors, Transcription, Genetic, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, MESH: Neurons, MESH: Rats, Sprague-Dawley, Mitochondrion, Biochemistry, Rats, Sprague-Dawley, 0302 clinical medicine, Endocrinology, MESH: Aging, MESH: Animals, MESH: Proteins, Cytoskeleton, Oligonucleotide Array Sequence Analysis, 2. Zero hunger, Neurons, 0303 health sciences, Systems Biology, Brain, MESH: Gene Expression Regulation, Mitochondria, medicine.anatomical_structure, MESH: Systems Biology, MESH: Vitamin D Deficiency, Molecular Medicine, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Signal Transduction, medicine.medical_specialty, MESH: Rats, Offspring, MESH: Mitochondria, Central nervous system, Biology, vitamin D deficiency, 03 medical and health sciences, MESH: Brain, Internal medicine, medicine, Vitamin D and neurology, MESH: Cytoskeleton, Animals, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Molecular Biology, 030304 developmental biology, Multiple sclerosis, MESH: Transcription, Genetic, MESH: Time Factors, Proteins, Cell Biology, medicine.disease, Vitamin D Deficiency, MESH: Male, Rats, Gene Expression Regulation, Synaptic plasticity, MESH: Oligonucleotide Array Sequence Analysis, MESH: Female, 030217 neurology & neurosurgery
Abstract: Epidemiology has highlighted the links between season of birth, latitude and the prevalence of brain disorders such as multiple sclerosis and schizophrenia. In line with these data, we have hypothesized that "imprinting" with low prenatal vitamin D could contribute to the risk of these two brain disorders. Previously, we have shown that transient developmental hypovitaminosis D induces permanent changes in adult nervous system. The aim of this study was to examine the impact of prenatal hypovitaminosis D on gene expression in the adult rat brain. Vitamin D deficient female rats were mated with undeprived males and the offspring were fed with a control diet after birth. At Week 10, gene expression in the progeny's brain was compared with control animals using Affymetrix gene microarrays. Prenatal hypovitaminosis D causes a dramatic dysregulation of several biological pathways including oxidative phosphorylation, redox balance, cytoskeleton maintenance, calcium homeostasis, chaperoning, post-translational modifications, synaptic plasticity and neurotransmission. A computational analysis of these data suggests that impaired synaptic network may be a consequence of mitochondrial dysfunction. Since disruptions of mitochondrial metabolism have been associated with both multiple sclerosis and schizophrenia, developmental vitamin D deficiency may be a heuristic animal model for the study of these two brain diseases.
Published: 2007

29. [Immunology: towards the end of the amateurs?]

Author: Bernard, Malissen, Centre d'Immunologie de Marseille - Luminy (CIML), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)
Subjects: Allergy and Immunology, Systems Biology, MESH: Systems Biology, MESH: Molecular Biology, [SDV.IMM]Life Sciences [q-bio]/Immunology, MESH: Allergy and Immunology, Molecular Biology
Published: 2007

30. A new pairwise kernel for biological network inference with support vector machines

Author: William Stafford Noble, Jian Qiu, Jean-Philippe Vert, Centre de Bioinformatique (CBIO), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Department of Genome Sciences [Seattle] (GS), and University of Washington [Seattle]
Subjects: MESH: Signal Transduction, Saccharomyces cerevisiae Proteins, Inference, MESH: Algorithms, Saccharomyces cerevisiae, 02 engineering and technology, Biology, Indirect Inference, Machine learning, computer.software_genre, Biochemistry, 03 medical and health sciences, MESH: Gene Expression Profiling, MESH: Saccharomyces cerevisiae Proteins, Structural Biology, 0202 electrical engineering, electronic engineering, information engineering, Molecular Biology, 030304 developmental biology, 0303 health sciences, Models, Statistical, business.industry, Gene Expression Profiling, Systems Biology, Applied Mathematics, Biological network inference, Pattern recognition, MESH: Saccharomyces cerevisiae, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Support vector machine, Proceedings, ComputingMethodologies_PATTERNRECOGNITION, MESH: Metabolic Networks and Pathways, Kernel (statistics), MESH: Systems Biology, Metric (mathematics), 020201 artificial intelligence & image processing, Pairwise comparison, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer, Algorithms, Metabolic Networks and Pathways, Biological network, MESH: Models, Statistical, Signal Transduction
Abstract: International audience; BACKGROUND: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). RESULTS: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. CONCLUSION: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.
Published: 2007

31. Validation of qualitative models of genetic regulatory networks by model checking: analysis of the nutritional stress response in Escherichia coli

Author: Johannes Geiselmann, Gregory Batt, Hidde de Jong, Dominique Schneider, Delphine Ropers, Radu Mateescu, Michel Page, Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF), Computer science and genomics (HELIX), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)
Subjects: Statistics and Probability, Model checking, 0209 industrial biotechnology, Time Factors, Computer science, 02 engineering and technology, computer.software_genre, Bacterial Physiological Phenomena, Biochemistry, Models, Biological, Modeling and simulation, Fight-or-flight response, 03 medical and health sciences, MESH: Software, 020901 industrial engineering & automation, MESH: Computer Simulation, Escherichia coli, Computer Simulation, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, MESH: Models, Genetic, MESH: Bacterial Physiology, Molecular Biology, 030304 developmental biology, 0303 health sciences, Models, Genetic, MESH: Escherichia coli, Systems Biology, MESH: Time Factors, MESH: Models, Biological, Experimental data, Computational Biology, Reproducibility of Results, Computer Science Applications, MESH: Reproducibility of Results, Computational Mathematics, Computational Theory and Mathematics, Genes, Bacterial, MESH: Systems Biology, Data mining, MESH: Genes, Bacterial, computer, Software, MESH: Computational Biology
Abstract: Motivation: The modeling and simulation of genetic regulatory networks have created the need for tools for model validation. The main challenges of model validation are the achievement of a match between the precision of model predictions and experimental data, as well as the efficient and reliable comparison of the predictions and observations. Results: We present an approach towards the validation of models of genetic regulatory networks addressing the above challenges. It combines a method for qualitative modeling and simulation with techniques for model checking, and is supported by a new version of the computer tool Genetic Network Analyzer (GNA). The model-validation approach has been applied to the analysis of the network controlling the nutritional stress response in Escherichia coli. Availability: GNA and the model of the stress response network are available at http://www-helix.inrialpes.fr/gna Contact: Hidde.de-Jong@inrialpes.fr
Published: 2005

32. Mouse phenogenomics: the fast track to 'systems metabolism'

Author: Carmen Argmann, Johan Auwerx, Pierre Chambon, Institut de génétique et biologie moléculaire et cellulaire (IGBMC), Université Louis Pasteur - Strasbourg I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Louis Pasteur - Strasbourg I
Subjects: Physiology, Systems biology, MESH: Pharmacogenetics, Human metabolism, Genomics, Model system, Computational biology, Biology, MESH: Phenotype, Bioinformatics, Genome, Models, Biological, Chromosomes, 03 medical and health sciences, Mice, 0302 clinical medicine, MESH: Computer Simulation, Animals, Humans, MESH: Animals, MESH: Genome, Computer Simulation, MESH: Models, Genetic, MESH: Mice, Molecular Biology, Crosses, Genetic, 030304 developmental biology, 0303 health sciences, MESH: Humans, Models, Genetic, Extramural, MESH: Genomics, Systems Biology, MESH: Models, Biological, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, Cell Biology, MESH: Crosses, Genetic, Phenotype, Pharmacogenetics, MESH: Systems Biology, MESH: Genetic Engineering, MESH: Chromosomes, Genetic Engineering, 030217 neurology & neurosurgery
Abstract: With the completion of the many genomes, genetics is positioned to meet physiology. In this review, we summarize the coming of "systems metabolism" in mammals through the use of the mouse, as a model system to study metabolism. Building on mouse genetics with increasingly sophisticated clinical and molecular phenotyping strategies has enabled scientists to now tackle complex biomedical questions, such as those related to the pathogenesis of the common metabolic disorders. The ultimate goal of such strategies will be to mimic human metabolism with the click of a mouse.
Published: 2005

33. Event-driven simulation of neural population synchronization facilitated by electrical coupling

Author: Boris Barbour, Olivier J. M. D. Coenen, Richard R. Carrillo, Christian Boucheny, Eduardo Ros, Laboratoire de Neurobiologie (UMR 8544) (NEURO), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physiologie de la Perception et de l'Action (LPPA), and Collège de France (CdF (institution))-Centre National de la Recherche Scientifique (CNRS)
Subjects: Statistics and Probability, Scheme (programming language), Physical neural network, Theoretical computer science, Computer science, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Computation, Models, Neurological, Action Potentials, 02 engineering and technology, Synaptic Transmission, General Biochemistry, Genetics and Molecular Biology, Random neural network, Network simulation, MESH: Neural Networks (Computer), 03 medical and health sciences, 0302 clinical medicine, MESH: Models, Neurological, Synchronization (computer science), MESH: Synaptic Transmission, 0202 electrical engineering, electronic engineering, information engineering, Evoked Potentials, MESH: Action Potentials, computer.programming_language, Spiking neural network, Quantitative Biology::Neurons and Cognition, Applied Mathematics, Systems Biology, General Medicine, MESH: Evoked Potentials, MESH: Nerve Net, MESH: Systems Biology, Modeling and Simulation, Table (database), 020201 artificial intelligence & image processing, Neural Networks, Computer, Nerve Net, computer, Algorithm, 030217 neurology & neurosurgery
Abstract: Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.
Published: 2005

34. Proceeding of the Third International Conference of the French-Speaking Society for Theoretical Biology

Author: Slimane Ben Miled, Université de Tunis El Manar (UTM), and SBM and SFBT wish to thank the Tunisian Ministry of Higher Education and Scientific Research, TWAS, IRD and AUF for their financial support of the event.
Subjects: Societies, Scientific, [SDV]Life Sciences [q-bio], Systems biology, MathematicsofComputing_GENERAL, 050905 science studies, GeneralLiterature_MISCELLANEOUS, General Biochemistry, Genetics and Molecular Biology, InformationSystems_GENERAL, 03 medical and health sciences, Multidisciplinary approach, MESH: Models, Theoretical, Complex systems biology, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, General Environmental Science, Mathematics, MESH: Systems Biology, 0303 health sciences, Mathematical and theoretical biology, Systems Biology, Applied Mathematics, 05 social sciences, General Medicine, Models, Theoretical, Rotation formalisms in three dimensions, Epistemology, MESH: France, Philosophy, Philosophy of biology, Action (philosophy), Identification (biology), France, MESH: Societies, Scientific, 0509 other social sciences, General Agricultural and Biological Sciences
Abstract: The definition of theoretical biology may be controversial. Indeed, the basic definition of theoretical biology classifies it as biomathematics. It assumes that theoretical biology is divided into, at least, four subfields: Biological mathematical modeling, Relational biology/Complex systems biology, Bioinformatics and Biomodeling Computation. In epistemology, theoretical biology is defined as the action of ‘‘theorizing’’ life sciences. It aims to introduce laws; it highlights some biological phenomena and makes possible the identification of new biological principles. The latter definition does not imply a direct relationship with mathematics. In both cases, theoretical biology is a strongly multidisciplinary science, for the tools imported from other science and for the shared ideas. This controversy was one of the main concerns of the French-speaking Society for Theoretical Biology (SFBT). In 1985, it succeeded the Association for the development of Theoretical Methods for Biology (AMTB). The initial purpose of AMTB was to organize Schools; rapidly it took a more general objective: ‘‘the development of methods and theoretical formalisms useful for fundamental biological research and its practical applications’’. The origin of SFBT Conferences comes from 1975, when Jean-Henri Maresquelle, used to invite colleagues with different scientific backgrounds to round tables, twice a year. These meetings lead to scientifically fruitful multidisciplinary
Published: 2012

35. Genetdes: automatic design of transcriptional networks

Author: Guillermo Rodrigo, Javier Carrera, Alfonso Jaramillo, Dep. de Matematica Aplicada, Universitat Politècnica de València (UPV), Laboratoire de Biochimie de l'Ecole polytechnique (BIOC), and Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)
Subjects: Statistics and Probability, Theoretical computer science, Transcription, Genetic, Computer science, 0206 medical engineering, MESH: Algorithms, 02 engineering and technology, Models, Biological, Biochemistry, MESH: Software, Automation, 03 medical and health sciences, MESH: Computer Simulation, MESH: Automation, Transcription (biology), Oscillometry, MESH: Promoter Regions, Genetic, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Computer Simulation, SBML, MESH: Oscillometry, Promoter Regions, Genetic, Molecular Biology, Gene, 030304 developmental biology, 0303 health sciences, Models, Statistical, MESH: Kinetics, MESH: Transcription, Genetic, Systems Biology, Transcriptional Networks, MESH: Models, Biological, Rational design, Computational Biology, Computer Science Applications, Kinetics, Computational Mathematics, Computational Theory and Mathematics, MESH: Systems Biology, Simulated annealing, MESH: Models, Statistical, Algorithms, Software, 020602 bioinformatics, Biological network, MESH: Computational Biology
Abstract: Motivation: The rational design of biological networks with prescribed functions is limited to gene circuits of a few genes. Larger networks involve complex interactions with many parameters and the use of automated computational tools can be very valuable. We propose a new tool to design transcriptional networks with targeted behavior that could be used to better understand the design principles of genetic circuits.Results: We have implemented a Simulated Annealing optimization algorithm that explores throughout the space of transcription networks to obtain a specific behavior. The software outputs a transcriptional network with all the corresponding kinetic parameters in SBML format. We provide examples of transcriptional circuits with logical and oscillatory behaviors. Our tool can also be applied to design networks with multiple external input and output genes.Availability: The software, a tutorial manual, parameter sets and examples are freely available at http://synth-bio.yi.org/genetdes.htmlContact: Alfonso.Jaramillo@polytechnique.edu
Published: 2007

36. Pooling in systems biology becomes smart

Author: Nicolas Thierry-Mieg, Laboratoire Logiciels Systèmes Réseaux (LSR - IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Systems biology, 0206 medical engineering, Pooling, 02 engineering and technology, MESH: Two-Hybrid System Techniques, Machine learning, computer.software_genre, Bioinformatics, Biochemistry, 03 medical and health sciences, Molecular Biology, MESH: False Negative Reactions, 030304 developmental biology, Simple (philosophy), 0303 health sciences, MESH: False Positive Reactions, MESH: Protein Array Analysis, business.industry, Cell Biology, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], MESH: Sensitivity and Specificity, MESH: Systems Biology, MESH: Drug Resistance, Artificial intelligence, business, computer, 020602 bioinformatics, MESH: Computational Biology, Biotechnology
Abstract: A simple 'smart-pooling' screening strategy for large-scale systems biology experiments promises to provide considerable improvement in experimental efficiency, while simultaneously allowing improved accuracy and coverage.
Published: 2006

37. Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling

Author: Denis Thieffry, Aurélien Naldi, Claudine Chaouiya, Jorge Carneiro, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Technologies avancées pour le génôme et la clinique (TAGC), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Constraint programming (CONTRAINTES), Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work has been supported by the French National Agency (projects ANR-06-BYOS-0006 and ANR-08-SYSC-003), the Belgian Science Policy Office (IAP BioMaGNet), and the Calouste Gulbenkian Foundation, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Autard, Delphine, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Subjects: MESH: Signal Transduction, Cellular differentiation, T cell receptors, Lymphocyte Activation, Immunology/Leukocyte Signaling and Gene Expression, Mice, 0302 clinical medicine, Cell differentiation, MESH: Animals, Logical data model, Biology (General), 0303 health sciences, Computational Biology/Systems Biology, Ecology, Systems Biology, Cell Differentiation, T-Lymphocytes, Helper-Inducer, Regulatory T cells, MESH: Transcription Factors, Cell biology, Computational Theory and Mathematics, MESH: Systems Biology, Modeling and Simulation, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Cytokines, Signal transduction, Immunology/Leukocyte Development, Graphs, Research Article, Signal Transduction, MESH: Cell Differentiation, Cell type, MESH: Interleukins, Logical analysis, QH301-705.5, In silico, Systems biology, T cells, Computational Biology/Transcriptional Regulation, Computational biology, Biology, MESH: Models, Immunological, T helper cells, 03 medical and health sciences, Cellular and Molecular Neuroscience, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Transcription factors, Genetics, Animals, Humans, MESH: T-Lymphocytes, Helper-Inducer, MESH: Lymphocyte Activation, MESH: Mice, Molecular Biology, Transcription factor, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, MESH: Humans, Interleukins, Models, Immunological, Computational Biology/Signaling Networks, Immunology/Immune Response, 030217 neurology & neurosurgery, Transcription Factors
Abstract: Alternative cell differentiation pathways are believed to arise from the concerted action of signalling pathways and transcriptional regulatory networks. However, the prediction of mammalian cell differentiation from the knowledge of the presence of specific signals and transcriptional factors is still a daunting challenge. In this respect, the vertebrate hematopoietic system, with its many branching differentiation pathways and cell types, is a compelling case study. In this paper, we propose an integrated, comprehensive model of the regulatory network and signalling pathways controlling Th cell differentiation. As most available data are qualitative, we rely on a logical formalism to perform extensive dynamical analyses. To cope with the size and complexity of the resulting network, we use an original model reduction approach together with a stable state identification algorithm. To assess the effects of heterogeneous environments on Th cell differentiation, we have performed a systematic series of simulations considering various prototypic environments. Consequently, we have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but these were found to coexist with other transient hybrid cell types that co-express combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. In the process, our logical analysis highlights the nature of these cell types and their relationships with canonical Th subtypes. Finally, our logical model can be used to explore novel differentiation pathways in silico., Author Summary T lymphocytes play a key role in the regulation of the immune response in mammals. Various T-helper subtypes (Th1, Th2, Th17, Treg,…) have been identified over the years, characterised by the expression of specific transcription factors and cytokines, which have a critical influence on the selection of different immune responses, driving pro-inflammatory or allergic responses, promoting alternative antibody classes, or preventing (auto)immunity by inhibiting the activation and proliferation of other cells. To gain insight into the heterogeneity and the plasticity of late T-helper lineages, we have built an integrated model of the regulatory network and signalling pathways controlling Th cell differentiation. Relying on a logical modelling framework, we have performed a systematic series of simulations to assess the effects of heterogeneous environments on Th cell differentiation. We have identified stable states corresponding to canonical Th1, Th2, Th17 and Treg subtypes, but also to hybrid cell types co-expressing combinations of Th1, Th2, Treg and Th17 markers in an environment-dependent fashion. Our analysis highlights the nature of these cell types and their relationships with canonical Th subtypes.
Published: 2010

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