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Graph clustering is one of the most important research topics in graph mining and network analysis. Given the abundance of data in many real-world applications , graph nodes and edges could be annotated with multiple sets of attributes that could be derived from heterogeneous data sources. The consideration of these attributes during graph clustering would facilitate the generation of graph clusters with balanced and cohesive intra-cluster structures and nodes with homogeneous properties. In this paper, we propose a graph clustering approach for mining skyline clusters over large attributed graphs based on the dominance relationship. Each skyline solution is optimized simultaneously for multiple fitness functions, each function is defined over the graph topology or over a particular set of attributes derived from multiple data sources. We evaluate our approach experimentally with a large protein-protein interaction network of the human interactome enriched with large sets of heterogeneous cancer-associated attributes. The results demonstrate the efficiency of our approach and show how integrating node attributes from multiple data sources can result in a more robust graph clustering than the consideration of the graph topology alone.

International audience; Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients’ data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.

In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy is based on the comparison between tumoral and healthy cells. First, we use gene expression data from healthy cells to infer a reference gene regulatory network. Then, we compare it with gene expression levels in tumor samples to detect deregulated target genes. We finally measure the ability of each transcription factor to explain these deregulations. We apply our method on a public bladder cancer data set derived from The Cancer Genome Atlas project and confirm that it captures hallmarks of cancer subtypes. We also show that it enables the discovery of new potential biomarkers.

MotivationRheumatoid arthritis (RA) is a multifactorial autoimmune disease that causes chronic inflammation of the joints. RA is considered a complex disease as it involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this approach, we integrate three different biological layers (gene expression, signalling cascades, mutations), obtained by bottom-up and top-down methods to build an integrative, disease-specific network. The goal behind this endeavour is to see if we can unravel mechanisms governing the regulation of key genes identified as mutation carriers in RA and derive patient-specific models to gain more insights into the disease heterogeneity. ResultsIn this work, we combine biological data relevant to Rheumatoid Arthritis, in the form of a global, integrative network. We first make use of publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using prior knowledge encoded in a state-of-the-art, RA-specific molecular map. Lastly, a list of RA specific variants highlights key genes associated with known disease mutations. AvailabilityDatasets used for the analysis are publicly available. All scripts used to generate results and the Shiny app will be freely accessible after peer-reviewed publication.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that affects the synovial joints of the body. Rheumatoid arthritis fibroblast-like synoviocytes (RA FLS) are central players in the disease pathogenesis, as they are involved in the secretion of cytokines and proteolytic enzymes, exhibit invasive traits, high rate of self-proliferation and an apoptosis-resistant phenotype. We aim at characterizing transcription factors (TFs) that are master regulators in RA FLS and could potentially explain phenotypic traits. We make use of differentially expressed genes in synovial tissue from patients suffering from RA and osteoarthritis (OA) to infer a TF co-regulatory network, using dedicated software. The co-regulatory network serves as a reference to analyze microarray and single-cell RNA-seq data from isolated RA FLS. We identified five master regulators specific to RA FLS, namely BATF, POU2AF1, STAT1, LEF1 and IRF4. TF activity of the identified master regulators was also estimated with the use of two additional, independent software. The identified TFs contribute to the regulation of inflammation, proliferation and apoptosis, as indicated by the comparison of their differentially expressed target genes with hallmark molecular signatures derived from the Molecular Signatures Database (MSigDB). Our results show that TFs influence could be used to identify putative master regulators of phenotypic traits and suggest novel, druggable targets for experimental validation.

Significance Systems biology involves the development of large computational models of biological systems. The radical improvement of systems biology models will necessarily involve the automation of model improvement cycles. We present here a general approach to automating systems biology model improvement. Humans are eukaryotic organisms, and the yeast Saccharomyces cerevisiae is widely used in biology as a “model” for eukaryotic cells. The yeast diauxic shift is the most studied cellular transformation. We combined multiple software tools with integrated laboratory robotics to execute three semiautomated cycles of diauxic shift model improvement. All the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for execution. The resulting improved model is relevant to understanding cancer, the immune system, and aging., One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast (Saccharomyces cerevisiae) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles.

GREAT (Genome REgulatory Architecture Tools) is a novel web portal for tools designed to generate user-friendly and biologically useful analysis of genome architecture and regulation. The online tools of GREAT are freely accessible and compatible with essentially any operating system which runs a modern browser. GREAT is based on the analysis of genome layout -defined as the respective positioning of co-functional genes- and its relation with chromosome architecture and gene expression. GREAT tools allow users to systematically detect regular patterns along co-functional genomic features in an automatic way consisting of three individual steps and respective interactive visualizations. In addition to the complete analysis of regularities, GREAT tools enable the use of periodicity and position information for improving the prediction of transcription factor binding sites using a multi-view machine learning approach. The outcome of this integrative approach features a multivariate analysis of the interplay between the location of a gene and its regulatory sequence. GREAT results are plotted in web interactive graphs and are available for download either as individual plots, self-contained interactive pages or as machine readable tables for downstream analysis. The GREAT portal can be reached at the following URL https://absynth.issb.genopole.fr/GREAT and each individual GREAT tool is available for downloading.

Metabolic disorder contributes to the increase in the mortality rate of patients on hemodialysis (HD). The aim of this study was to estimate the prevalence of metabolic syndrome (MS) and malnutrition in patients on maintenance HD and to evaluate their influence on cardiovascular and all-cause mortality during the follow-up. We carried out a prospective cross- sectional study in which we enrolled 100 patients from a single center who had been followed up for three years. Collected data included demographic characteristics, detailed medical history, clinical variables, MS variables, nutritional status, and laboratory findings. The outcomes were the occurrence of a cardiovascular event and cardiovascular or all-cause mortality during the follow-up period. The Statistical Package for the Social Sciences software was used for statistical analysis. Whereas 50% of patients had MS, 23% showed evidence of malnutrition. Patients with MS were older and had more preexisting cardiovascular diseases (CVDs). All patients were followed for 36 months. During this time, 19 patients with MS and 14 patients without MS died (38% vs. 28%; P = 0.19), most frequently of CVD. Mean survival time was 71.52 ± 42.1 months for MS group versus 92.06 ± 65 months for non-MS group, but the difference was not significant. MS was related with a higher cardiovascular mortality, while malnutrition was significantly associated with all-cause mortality. Our data showed that MS was not related to cardiovascular or all-cause mortality in HD patients and did not influence survival. The independent risk factors for all-cause mortality were older age, preexisting CVD, and malnutrition.

International audience; Cancer diagnosis is currently undergoing a paradigm shift with the incorporation of molecular biomarkers as part of routine diagnostic panel. This breakthrough discovery directs researches to examine the role of microRNA in cancer, since its deregulation is often associated with almost all human tumors. Such differences frequently recur in tumor-specific microRNA signatures, which are helpful to diagnose tissue of origin and tumor subtypes. Nonetheless, the resulting classification problem is far from trivial, as there are hundreds of microRNA types, and tumors are non-linearly correlated to the presence of several overexpressions. In this paper, we propose to apply an evolutionary optimized convolutional neural network classifier to this complex task. The presented approach is compared against 21 state-of-the-art classifiers, on a real-world dataset featuring 8129 patients, for 29 different classes of tumors, using 1046 different biomarkers. As a result of the comparison, we also present a meta-analysis on the dataset, identifying the classes on which the collective performance of the considered classifiers is less effective, and thus possibly singling out types of tumors for which biomarker tests might be less reliable.

PMID: 29322933; International audience; Background: Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype. Results: We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods. Conclusions: Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.

CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study CoRegNet was used to identify driver regulators of bladder cancer. Availability: CoRegNet is available at http://bioconductor.org/packages/CoRegNet Contact: remy.nicolle@issb.genopole.fr or mohamed.elati@issb.genopole.fr Supplementary inform a tion: Supplementary data are available at Bioinformatics online.

Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may match large numbers of sites and produce an unreliable list of target genes. To improve the prediction of binding sites, we propose to additionally use the unrelated knowledge of the genome layout. Indeed, it has been shown that co-regulated genes tend to be either neighbors or periodically spaced along the whole chromosome. This study demonstrates that respective gene positioning carries significant information. This novel type of information is combined with traditional sequence information by a machine learning algorithm called PreCisIon. To optimize this combination, PreCisIon builds a strong gene target classifier by adaptively combining weak classifiers based on either local binding sequence or global gene position. This strategy generically paves the way to the optimized incorporation of any future advances in gene target prediction based on local sequence, genome layout or on novel criteria. With the current state of the art, PreCisIon consistently improves methods based on sequence information only. This is shown by implementing a cross-validation analysis of the 20 major TFs from two phylogenetically remote model organisms. For Bacillus subtilis and Escherichia coli, respectively, PreCisIon achieves on average an area under the receiver operating characteristic curve of 70 and 60%, a sensitivity of 80 and 70% and a specificity of 60 and 56%. The newly predicted gene targets are demonstrated to be functionally consistent with previously known targets, as assessed by analysis of Gene Ontology enrichment or of the relevant literature and databases.

The annotation of protein provides a considerable knowledge for the biologists in order to understand life at the molecular level. The computational annotation of protein function has therefore emerged as an important alternative given that the biological experiments are extremely laborious. A number of methods have been developed to computationally annotate proteins using standardized nomenclatures such as Gene Ontology. These methods are based on various independency assumptions for modeling the annotation problem. However, the recent network analysis reveals that the same protein with different interactions may perform different functions. In this paper, we take into account the topology of the protein-protein interaction network in order to propose a new representation of functions' ontology. We use the Bayesian network in order to model and to alter the structure of this ontology so as to create the new context specific ontology. We use this newly proposed structure for predicting the functions of the unlabeled proteins. We evaluate our method, called Context-Specific Ontology by the use of the Bayesian Network (ConSOn-BN), on the Saccharomyces cerevisiae protein-protein interaction network and we find that ConSOn-BN has enhanced results as compared to some known methods.

Background Genome layout and gene regulation appear to be interdependent. Understanding this interdependence is key to exploring the dynamic nature of chromosome conformation and to engineering functional genomes. Evidence for non-random genome layout, defined as the relative positioning of either co-functional or co-regulated genes, stems from two main approaches. Firstly, the analysis of contiguous genome segments across species, has highlighted the conservation of gene arrangement (synteny) along chromosomal regions. Secondly, the study of long-range interactions along a chromosome has emphasised regularities in the positioning of microbial genes that are co-regulated, co-expressed or evolutionarily correlated. While one-dimensional pattern analysis is a mature field, it is often powerless on biological datasets which tend to be incomplete, and partly incorrect. Moreover, there is a lack of comprehensive, user-friendly tools to systematically analyse, visualise, integrate and exploit regularities along genomes. Results Here we present the Genome REgulatory and Architecture Tools SCAN (GREAT:SCAN) software for the systematic study of the interplay between genome layout and gene expression regulation. GREAT:SCAN is a collection of related and interconnected applications currently able to perform systematic analyses of genome regularities as well as to improve transcription factor binding sites (TFBS) and gene regulatory network predictions based on gene positional information. Conclusions We demonstrate the capabilities of these tools by studying on one hand the regular patterns of genome layout in the major regulons of the bacterium Escherichia coli. On the other hand, we demonstrate the capabilities to improve TFBS prediction in microbes. Finally, we highlight, by visualisation of multivariate techniques, the interplay between position and sequence information for effective transcription regulation. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1047-0) contains supplementary material, which is available to authorized users.

International audience; In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the subtypes. To answer this question, we propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be deregulated. We derive an EM algorithm where the hidden variables correspond to the status (under/over/normally expressed) of the genes and where the E-step is solved thanks to a message passing algorithm. Our procedure provides posterior probabilities of deregulation in a given sample for each gene. We assess the performance of our method by numerical experiments on simulations and on a bladder cancer data set.

One of the most promising but challenging tasks in the post-genomic era is to reconstruct transcriptional regulatory networks from gene expression data. We propose an alternative heuristic solution to those implemented in Bayesian approaches for inferring transcription regulation topology from RNA microarray data. This method relies on classical data mining techniques, namely frequent itemset mining, in order to reduce efficiently the space of candidate transcriptional regulation relations consistent with expression data. We have experimented our method on real human bladder cancer data, and results are quite encouraging.

Reconstruction of large scale gene regulatory networks (GRNs in the following) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality—large number of genes but a small number of samples, overfitting, heavy computation time and low interpretability. We have previously proposed an original Data Mining algorithm Licorn, that infers cooperative regulation network from expression datasets. In this work, we present an extension of Licorn to a hybrid inference method h-Licorn that uses search in both discrete and real valued spaces. Licorn's algorithm, using the discrete space to find cooperative regulation relationships fitting the target gene expression, has been shown to be powerful in identifying cooperative regulation relationships that are out of the scope of most GRN inference methods. Still, as many of related GRN inference techniques, Licorn suffers from a large number of false positives. We propose here an extension of Licorn with a numerical selection step, expressed as a linear regression problem, that effectively complements the discrete search of Licorn. We evaluate a bootstrapped version of h-Licorn on the in silico Dream5 dataset and show that h-Licorn has significantly higher performance than Licorn, and is competitive or outperforms state of the art GRN inference algorithms, especially when operating on small data sets. We also applied h-Licorn on a real dataset of human bladder cancer and show that it performs better than other methods in finding candidate regulatory interactions. In particular, solely based on gene expression data, h-Licorn is able to identify experimentally validated regulator cooperative relationships involved in cancer.

Reconstruction of gene regulatory networks (GRNs) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality - large number of genes but a small number of samples -, over fitting, and heavy computation time. In this work 1, we present a novel method, namely SETNET, to improve the stability and accuracy of GRN inference using ensemble techniques. For a given target gene, SETNET extract an ensemble of regulation networks from discretized expression data instead of a single one. Inferred networks are then assessed by ranking individual regulation relationships using a regression based technique and continuous expression data. Evaluation on DREAM5 data demonstrates that SETNET is efficient, specially when operating on a small data set.

In recent years, graph clustering methods have rapidly emerged to mine latent knowledge and functions in networks. Most sub-graphs extracting methods that have been introduced fall into graph clustering. In this paper, a novel trend of relevant sub-graphs extraction problem was considered as multi-objective optimization. Genetic Algorithms GAs and Simulated Annealing SA were then used to solve the problem applied to biological networks. Comparisons between GAs, SA and Markov Cluster Algorithm MCL were carried out and the results showed that the proposed approach is superior.

Classical approaches to analyze transcriptomic data usually produce average classification models that have very low reproducibility. In this work, genome wide gene expression is considered through the activity of large regulatory networks. We introduce a new measure of regulatory influence based on the variations of expression of genes in a large inferred regulatory network. This methodology can be used to transform transcriptomic data into a smaller influence data set on which feature selection and classification models show similar predictive performance and increased stability and reproducibility, especially when comparing models trained on different datasets. The methodology was tested on two distinct bladder cancer data sets.

Understanding transcriptional regulation requires a reliable identification of the DNA binding sites that are recognized by each transcription factor (TF). Building an accurate bioinformatic model of TF-DNA binding is an essential step to differentiate true binding targets from spurious ones. Conventional approches of binding site prediction are based on the notion of consensus sequences. They are formalized by the so-called position-specific weight matrices (PWM) and rely on the statistical analysis of DNA sequence of known binding sites. To improve these techniques, we propose to use genome organization knowledge about the optimal positioning of co-regulated genes along the whole chromosome. For this purpose, we use learning machine approaches to optimally combine sequence information with positioning information. We present a new learning algorithm called PreCisIon, which relies on a TF binding classifier that optimally combines a set of PWMs and chrommosal position based classifiers. This non-linear binding decision rule drastically reduces the rate of false positives so that PRECISION consistently outperforms sequence-based methods. This is shown by implementing a cross-validation analysis in two model organisms: Escherichia coli and Bacillus Subtilis. The analysis is based on the identification of binding sites for 24 TFs; PRECISION achieved on average an AUC (aera under the curve) of 70% and 60%, a sensitivity of 80% and 70%, and a specificity of 60% and 56% for B. subtilis and E. coli, respectively.

In the organism, each organ, each cell, and each protein has a defined role to fulfill so that life is maintained. When one player deviates from its predefined scenario, the consequences are generally not even noticeable. However, sometimes, the consequences are very important, and the whole organism might not survive it. The only way to fight against these disorders is to understand fully the functioning of the whole ensemble, and how each deviation affects it. This is what systems biology is about [2, 14]. At the cellular level, this requires understanding how the different components interact, and how these interactions result in functional networks, often called pathways. Regulation of gene expression at the transcriptional level is a fundamental mechanism that is evolutionarily conserved in all the cellular systems. This form of regulation is typically mediated by transcription factors (TFs) that bind to short DNA sequence motifs, also called binding sites, in promoter regions of transcriptional units and either activate or repress the expression of nearby genes.

International audience; BACKGROUND: Identifying gene functional modules is an important step towards elucidating gene functions at a global scale. Clustering algorithms mostly rely on co-expression of genes, that is group together genes having similar expression profiles. RESULTS: We propose to cluster genes by co-regulation rather than by co-expression. We therefore present an inference algorithm for detecting co-regulated groups from gene expression data and introduce a method to cluster genes given that inferred regulatory structure. Finally, we propose to validate the clustering through a score based on the GO enrichment of the obtained groups of genes. CONCLUSION: We evaluate the methods on the stress response of S. Cerevisiae data and obtain better scores than clustering obtained directly from gene expression.

Motivation: One of the most challenging tasks in the post-genomic era is the reconstruction of transcriptional regulation networks. The goal is to identify, for each gene expressed in a particular cellular context, the regulators affecting its transcription, and the co-ordination of several regulators in specific types of regulation. DNA microarrays can be used to investigate relationships between regulators and their target genes, through simultaneous observations of their RNA levels.Results: We propose a data mining system for inferring transcriptional regulation relationships from RNA expression values. This system is particularly suitable for the detection of cooperative transcriptional regulation. We model regulatory relationships as labelled two-layer gene regulatory networks, and describe a method for the efficient learning of these bipartite networks from discretized expression data sets. We also evaluate the statistical significance of such inferred networks and validate our methods on two public yeast expression data sets.Availability: http://www.lri.fr/~elati/licorn.htmlContact: mohamed.elati@curie.frSupplementary information: Supplementary data are available at Bioinformatics online.

Objective Understanding the molecular and cellular mechanisms of ciliopathies is vital for dissecting their pathogenesis, identifying appropriate therapeutic targets and designing effective treatments. Recent advances in DNA sequencing technology have provided a torrent of genetic data that can now be used to elucidate the genetic basis of human diseases. A consensus has emerged among biologists that to fully exploit the available data, they have to be correlated with additional research. This is especially important for rare-disease genetics, because of the small number of available patients.

International audience; In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy is based on the comparison between tumoral and healthy cells. First, we use gene expression data from healthy cells to infer a reference gene regulatory network. Then, we compare it with gene expression levels in tumor samples to detect deregulated target genes. We finally measure the ability of each transcription factor to explain these deregulations. We apply our method on a public bladder cancer data set derived from The Cancer Genome Atlas project and confirm that it captures hallmarks of cancer subtypes. We also show that it enables the discovery of new potential biomarkers.

Comparison between tumoral and healthy cells may reveal abnormal regulation behaviors between a transcription factor and the genes it regulates, without exhibiting differential expression of the former genes. We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells. This strategy is based on the inference of a reference gene regulatory network that connects transcription factors to their downstream targets using gene expression data. Gene expression levels in tumor samples are then carefully compared to this reference network to detect deregulated target genes. A linear model is finally used to measure the ability of each transcription factor to explain these deregulations. We assess the performance of our method by numerical experiments on a public bladder cancer data set derived from the Cancer Genome Atlas project. We identify genes known for their implication in the development of specific bladder cancer subtypes as well as new potential biomarkers.

Metabolism defines the set of biochemical reactions within an organism, allowing it to survive and adapt to different environments. Regulating these reactions requires complex processes involving many effectors interacting together at different scales.Developing models of these regulatory networks is therefore an essential step in better understanding the precise mechanisms governing living systems and ultimately enabling the design of synthetic, self-regulating and adaptive systems at the genome level. As part of this interdisciplinary work, we propose to use an iterative network inference and interrogation approach to guide the engineering of the metabolism of the yeast of industrial interest Yarrowia lipolytica.Based on transcriptomic data, the first network for the regulation of adaptation to nitrogen limitation and lipid production in this yeast was inferred.The interrogation of this network has then allowed to to highlight and experimentally validate the impact of several regulators on lipid accumulation. In order to further explore the relationships between regulation and metabolism, a new method, CoRegFlux, has been proposed for the prediction of metabolic phenotype based on the influence profiles of regulators in the studied conditions. This R package, available on the Bioconductor platform, was then used to better understand adaptation to nitrogen limitation and to identify phenotypes of interest for strain engineering, particularly for the production of lipids and amino acid derivatives such as violacein.Thus, through an iterative approach, this work provides new insights into the interactions between regulation and metabolism in Y. lipolytica, conserved regulatory module in this yeast and contributes to the development of innovative integrative methods for computer-assisted strain design.; Le métabolisme définit l’ensemble des réactions biochimiques au sein d’un organisme, lui permettant de survivre et de s’adapter dans différents environnements. La régulation de ces réactions requiert un processus complexe impliquant de nombreux effecteurs interagissant ensemble à différentes échelles.Développer des modèles de ces réseaux de régulation est ainsi une étape indispensable pour mieux comprendre les mécanismes précis régissant les systèmes vivants et permettre, à terme, la conception de systèmes synthétiques, autorégulés et adaptatifs, à l'échelle du génome. Dans le cadre de ces travaux interdisciplinaires, nous proposons d’utiliser une approche itérative d’inférence de réseau et d’interrogation afin de guider l’ingénierie du métabolisme de la levure d’intérêt industriel Yarrowia lipolytica.À partir de données transcriptomiques, le premier réseau de régulation de l’adaptation à la limitation en azote et de la production de lipides a été inféré pour cette levure. L’interrogation de ce réseau a ensuite permis de mettre en avant et valider expérimentalement l’impact de régulateurs sur l'accumulation lipidique.Afin d’explorer davantage les liens entre régulation et métabolisme, une nouvelle méthode, CoRegFlux, a été proposée pour la prédiction de phénotype métabolique à partir des profils d’activités des régulateurs dans les conditions étudiées.Ce package R, disponible sur la plateforme Bioconductor, a ensuite été utilisé pour mieux comprendre l’adaptation à la limitation en azote et identifier des phénotypes d’intérêts en vue de l’ingénierie de cette levure, notamment pour la production de lipides et de violacéine.Ainsi, par une approche itérative, ces travaux apportent de nouvelles connaissances sur les interactions entre la régulation et le métabolisme chez Y. lipolytica, l’identification de motifs de régulation chez cette levure et contribue au développement de méthodes intégratives pour la conception de souches assistée par ordinateur.

Metabolism defines the set of biochemical reactions within an organism, allowing it to survive and adapt to different environments. Regulating these reactions requires complex processes involving many effectors interacting together at different scales.Developing models of these regulatory networks is therefore an essential step in better understanding the precise mechanisms governing living systems and ultimately enabling the design of synthetic, self-regulating and adaptive systems at the genome level. As part of this interdisciplinary work, we propose to use an iterative network inference and interrogation approach to guide the engineering of the metabolism of the yeast of industrial interest Yarrowia lipolytica.Based on transcriptomic data, the first network for the regulation of adaptation to nitrogen limitation and lipid production in this yeast was inferred.The interrogation of this network has then allowed to to highlight and experimentally validate the impact of several regulators on lipid accumulation. In order to further explore the relationships between regulation and metabolism, a new method, CoRegFlux, has been proposed for the prediction of metabolic phenotype based on the influence profiles of regulators in the studied conditions. This R package, available on the Bioconductor platform, was then used to better understand adaptation to nitrogen limitation and to identify phenotypes of interest for strain engineering, particularly for the production of lipids and amino acid derivatives such as violacein.Thus, through an iterative approach, this work provides new insights into the interactions between regulation and metabolism in Y. lipolytica, conserved regulatory module in this yeast and contributes to the development of innovative integrative methods for computer-assisted strain design.; Le métabolisme définit l’ensemble des réactions biochimiques au sein d’un organisme, lui permettant de survivre et de s’adapter dans différents environnements. La régulation de ces réactions requiert un processus complexe impliquant de nombreux effecteurs interagissant ensemble à différentes échelles.Développer des modèles de ces réseaux de régulation est ainsi une étape indispensable pour mieux comprendre les mécanismes précis régissant les systèmes vivants et permettre, à terme, la conception de systèmes synthétiques, autorégulés et adaptatifs, à l'échelle du génome. Dans le cadre de ces travaux interdisciplinaires, nous proposons d’utiliser une approche itérative d’inférence de réseau et d’interrogation afin de guider l’ingénierie du métabolisme de la levure d’intérêt industriel Yarrowia lipolytica.À partir de données transcriptomiques, le premier réseau de régulation de l’adaptation à la limitation en azote et de la production de lipides a été inféré pour cette levure. L’interrogation de ce réseau a ensuite permis de mettre en avant et valider expérimentalement l’impact de régulateurs sur l'accumulation lipidique.Afin d’explorer davantage les liens entre régulation et métabolisme, une nouvelle méthode, CoRegFlux, a été proposée pour la prédiction de phénotype métabolique à partir des profils d’activités des régulateurs dans les conditions étudiées.Ce package R, disponible sur la plateforme Bioconductor, a ensuite été utilisé pour mieux comprendre l’adaptation à la limitation en azote et identifier des phénotypes d’intérêts en vue de l’ingénierie de cette levure, notamment pour la production de lipides et de violacéine.Ainsi, par une approche itérative, ces travaux apportent de nouvelles connaissances sur les interactions entre la régulation et le métabolisme chez Y. lipolytica, l’identification de motifs de régulation chez cette levure et contribue au développement de méthodes intégratives pour la conception de souches assistée par ordinateur.