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4 results on '"Feedback circuits"'

1. The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks

Author

Nathan Weinstein, Daniel González-Tokman, Aurélien Naldi, Luis Mendoza, David A. Rosenblueth, Elena R. Alvarez-Buylla, Eugenio Azpeitia, Stalin Muñoz, Mariana Esther Martinez-Sanchez, Modeling plant morphogenesis at different scales, from genes to phenotype (VIRTUAL PLANTS), 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)-Institut National de la Recherche Agronomique (INRA)-Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas [México] (IIMAS), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Consejo Nacional de Ciencia y Tecnología [Mexico] (CONACYT), Programa de Doctorado en Ciencias Biomédicas [México], Instituto de Ecologia, Laboratorio de Matemática Aplicada y Cómputo de Alto Rendimiento del Departamento de Matemáticas (ABACUS), Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), Dynamique des interactions membranaires normales et pathologiques (DIMNP), Université Montpellier 1 (UM1)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM), Centro de Ciencias de la Complejidad (c3), Instituto de Investigaciones Biomedicas, PASPA-DGAPA-UNAM : 261225, UNAM-DGAPA-PAPIIT : IN 208517, IN 205517, N 211516, IN 204217, SEP-CONACyT : 221341, CONACyT : 240180, 180380, 2015-01-687, 251420, EDOMEX-2011-C01-165873, 400554, PASPA-DGAPA-UNAM, ABACUS, Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), Universidad Nacional Autónoma de México (UNAM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
0301 basic medicine, Computer science, Feedback circuits, Epistasis and functional genomics, Machine learning, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Attractor, [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], Combinatoire, applied mathematics, Statistiques (Mathématiques), automation, Multidisciplinary, business.industry, statistique informatique, mathématiques appliquées, 030104 developmental biology, Combinatorics, automatisation, Artificial intelligence, Erratum, Biological system, business, computer, 030217 neurology & neurosurgery
Abstract

Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

Published
2017
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2. How to Knock Out Feedback Circuits in Gene Networks?

Author

Adrien Richard, Christophe Soulé, H. Gruber, knowledgepark [München], knowledgepark, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe BIOINFO, Modèles Discrets pour les Systèmes Complexes (Laboratoire I3S - MDSC), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), 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), 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), Institut des Hautes Etudes Scientifiques (IHES), and IHES

Subjects
Genetics, media_common.quotation_subject, Feedback circuits, 010102 general mathematics, Gene regulatory network, Computational biology, Biology, 01 natural sciences, 010101 applied mathematics, 0101 mathematics, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Function (engineering), Gene, media_common
Abstract

International audience; The development of living organisms involve the successive activation of several gene networks. It is often known if activation or inhibition of a given gene occurs in these networks, but their global dynamic remains poorly understood. It is thus important to understand the function of motives contained in such a network, and for this one has to be able to perform knock out experiments.

Published
2013

3. From structure to dynamics: Frequency tuning in the p53-Mdm2 network. II. Differential and stochastic approaches

Author

Ouattara, Djomangan A., Abou-Jaoudé, Wassim, Kaufman, Marcelle, Faculté des Sciences, and Université libre de Bruxelles (ULB)

Subjects
Stochastic simulations, Oscillations, Mathematical modelling, Bifurcation analysis, Feedback circuits, P53/Mdm2
Abstract

International audience; In Part I of this work, we carried out a logical analysis of a simple model describing the interplay between protein p53, its main negative regulator Mdm2 and DNA damage, and briefly discussed the corresponding di_erential model (Abou-Jaoudé et al., 2009). This analysis allowed us to reproduce several qualitative features of the kinetics of the p53 response to damage and provided an interpretation of the short and long characteristic periods of oscillation reported by Geva-Zatorsky et al. (2006) depending on the irradiation dose. Starting from this analysis, we focus here on more quantitative aspects of the dynamics of our network and combine the differential description of our system with stochastic simulations which take molecular fluctuations into account. We find that the amplitude of the p53 and Mdm2 oscillations is highly variable (to a degree that depends, however, on the bifurcation properties of the system). In contrast, peak width and timing remain more regular, consistent with the experimental data. Our simulations also show that noise can induce repeated pulses of p53 and Mdm2 that, at low damage, resemble the slow irregular fluctuations observed experimentally. Adding the stochastic dimension in our modeling further allowed us to account for an increase of the fraction of cells oscillating with a high frequency when the irradiation dose increases, as observed by Geva-Zatorsky et al. (2006).

Published
2010
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4. A description of dynamical graphs associated to elementary regulatory circuits

Author

Claudine Chaouiya, Denis Thieffry, Elisabeth Remy, Brigitte Mossé, Institut de Biologie du Développement de Marseille (IBDM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut de mathématiques de Luminy (IML), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), Laboratoire de génétique et physiologie du développement (LGPD), and Université de la Méditerranée - Aix-Marseille 2-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, Proteome, Feedback circuits, 0102 computer and information sciences, [SDV.BC]Life Sciences [q-bio]/Cellular Biology, 92B05, Topology, Models, Biological, 01 natural sciences, Biochemistry, 03 medical and health sciences, [MATH.MATH-CO]Mathematics [math]/Combinatorics [math.CO], Computer Simulation, Molecular Biology, 030304 developmental biology, Mathematics, Electronic circuit, Structure (mathematical logic), Discrete mathematics, 0303 health sciences, Gene Expression Profiling, Quantitative Biology::Molecular Networks, Logical approach, Parity (physics), Computer Science Applications, Computational Mathematics, Gene Expression Regulation, Computational Theory and Mathematics, 010201 computation theory & mathematics, Asynchronous communication, Regulatory circuit, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithms, Signal Transduction, Sign (mathematics)
Abstract

The biological and dynamical importance of feedback circuits in regulatory graphs has often been emphasized. The work presented here aims at completly describing the dynamics of isolated elementary regulatory circuits. Our analytical approach is based on a discrete formal framework, built upon the logical approach of R. Thomas. Given a regulatory circuit, we show that the structure of synchronous and asynchronous dynamical graphs depends only on the length of the circuit (number of genes) and on its sign (which depends on the parity of the number of negative interactions). This work constitutes a first step towards the analytical characterisation of discrete dynamical graphs for more complex regulatory networks in terms of contributions corresponding to their embedded elementary circuits. Contact: remy@iml.univ-mrs.fr *To whom correspondence should be addressed.

Published
2003
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