Network modelling
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With the great advances in molecular biology, genomics and functional genomics, regulatory and signalling networks are being uncovered, opening the way for a better understanding of how cellular processes are controlled. Molecular pathways interplay and operate at diverse levels (transcription and translation of the genetic material, protein modifications, etc.). The complexity of these networks call for the development of dedicated mathematical and computational models. In this context, our goal is to develop generic tools to model and analyse large signalling and regulatory networks. For this purpose, we mainly rely on a qualitative modelling framework combining the generalised logical formalism, cellular automata and Petri nets. Our research goes mainly along three lines. First, we devise new methods to efficiently analyse properties of biological relevance in discrete models. Second, methodological advances are made available in the form of novel computational tools or as new functionalities of GINsim, our software tool for logical modelling (http://ginsim.org). Finally, in a close collaboration with biologists, methodological developments are motivated by and challenged with real case applications, specifying and analysing models of networks involved in processes of interest (cell proliferation, differentiation, etc.)
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- Diversity and plasticity of Th cell types predicted from regulatory network modellingPublication . Naldi, Aurélien; Carneiro, Jorge; Chaouiya, Claudine; Thieffry, DenisAlternative 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.
- Dynamical modeling and analysis of large cellular regulatory networksPublication . Bérenguier, D.; Chaouiya, C.; Monteiro, P. T.; Naldi, A.; Remy, E.; Thieffry, D.; Tichit, L.The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.
- Majority rules with random tie-breaking in Boolean gene regulatory networksPublication . Chaouiya, Claudine; Ourrad, Ouerdia; Lima, RicardoWe consider threshold boolean gene regulatory networks, where the update function of each gene is described as a majority rule evaluated among the regulators of that gene: it is turned ON when the sum of its regulator contributions is positive (activators contribute positively whereas repressors contribute negatively) and turned OFF when this sum is negative. In case of a tie (when contributions cancel each other out), it is often assumed that the gene keeps it current state. This framework has been successfully used to model cell cycle control in yeast. Moreover, several studies consider stochastic extensions to assess the robustness of such a model. Here, we introduce a novel, natural stochastic extension of the majority rule. It consists in randomly choosing the next value of a gene only in case of a tie. Hence, the resulting model includes deterministic and probabilistic updates. We present variants of the majority rule, including alternate treatments of the tie situation. Impact of these variants on the corresponding dynamical behaviours is discussed. After a thorough study of a class of two-node networks, we illustrate the interest of our stochastic extension using a published cell cycle model. In particular, we demonstrate that steady state analysis can be rigorously performed and can lead to effective predictions; these relate for example to the identification of interactions whose addition would ensure that a specific state is absorbing.
- Path2Models: large-scale generation of computational models from biochemical pathway mapsPublication . Büchel, Finja; Rodriguez, Nicolas; Swainston, Neil; Wrzodek, Clemens; Czauderna, Tobias; Keller, Roland; Mittag, Florian; Schubert, Michael; Glont, Mihai; Golebiewski, Martin; van Iersel, Martijn; Keating, Sarah; Rall, Matthias; Wybrow, Michael; Hermjakob, Henning; Hucka, Michael; Kell, Douglas B; Müller, Wolfgang; Mendes, Pedro; Zell, Andreas; Chaouiya, Claudine; Saez-Rodriguez, Julio; Schreiber, Falk; Laibe, Camille; Dräger, Andreas; Le Novère, NicolasSystems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data.
- Bringing Dicynodonts Back to Life: Paleobiology and Anatomy of a New Emydopoid Genus from the Upper Permian of MozambiquePublication . Castanhinha, Rui; Araújo, Ricardo; Júnior, Luís C.; Angielczyk, Kenneth D.; Martins, Gabriel G.; Martins, Rui M. S.; Chaouiya, Claudine; Beckmann, Felix; Wilde, FabianDicynodontia represent the most diverse tetrapod group during the Late Permian. They survived the Permo-Triassic extinction and are central to understanding Permo-Triassic terrestrial ecosystems. Although extensively studied, several aspects of dicynodont paleobiology such as, neuroanatomy, inner ear morphology and internal cranial anatomy remain obscure. Here we describe a new dicynodont (Therapsida, Anomodontia) from northern Mozambique: Niassodon mfumukasi gen. et sp. nov. The holotype ML1620 was collected from the Late Permian K5 formation, Metangula Graben, Niassa Province northern Mozambique, an almost completely unexplored basin and country for vertebrate paleontology. Synchrotron radiation based micro-computed tomography (SRµCT), combined with a phylogenetic analysis, demonstrates a set of characters shared with Emydopoidea. All individual bones were digitally segmented allowing a 3D visualization of each element. In addition, we reconstructed the osseous labyrinth, endocast, cranial nerves and vasculature. The brain is narrow and the cerebellum is broader than the forebrain, resembling the conservative, "reptilian-grade" morphology of other non-mammalian therapsids, but the enlarged paraflocculi occupy the same relative volume as in birds. The orientation of the horizontal semicircular canals indicates a slightly more dorsally tilted head posture than previously assumed in other dicynodonts. In addition, synchrotron data shows a secondary center of ossification in the femur. Thus ML1620 represents, to our knowledge, the oldest fossil evidence of a secondary center of ossification, pushing back the evolutionary origins of this feature. The fact that the specimen represents a new species indicates that the Late Permian tetrapod fauna of east Africa is still incompletely known.
- SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and toolsPublication . Chaouiya, Claudine; Bérenguier, Duncan; Keating, Sarah M; Naldi, Aurélien; van Iersel, Martijn P; Rodriguez, Nicolas; Dräger, Andreas; Büchel, Finja; Cokelaer, Thomas; Kowal, Bryan; Wicks, Benjamin; Gonçalves, Emanuel; Dorier, Julien; Page, Michel; Monteiro, Pedro T; von Kamp, Axel; Xenarios, Ioannis; de Jong, Hidde; Hucka, Michael; Klamt, Steffen; Thieffry, Denis; Le Novère, Nicolas; Saez-Rodriguez, Julio; Helikar, TomášQualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.
- A Discrete Model of Drosophila Eggshell Patterning Reveals Cell-Autonomous and Juxtacrine EffectsPublication . Fauré, Adrien; Vreede, Barbara M. I.; Sucena, Élio; Chaouiya, ClaudineThe Drosophila eggshell constitutes a remarkable system for the study of epithelial patterning, both experimentally and through computational modeling. Dorsal eggshell appendages arise from specific regions in the anterior follicular epithelium that covers the oocyte: two groups of cells expressing broad (roof cells) bordered by rhomboid expressing cells (floor cells). Despite the large number of genes known to participate in defining these domains and the important modeling efforts put into this developmental system, key patterning events still lack a proper mechanistic understanding and/or genetic basis, and the literature appears to conflict on some crucial points. We tackle these issues with an original, discrete framework that considers single-cell models that are integrated to construct epithelial models. We first build a phenomenological model that reproduces wild type follicular epithelial patterns, confirming EGF and BMP signaling input as sufficient to establish the major features of this patterning system within the anterior domain. Importantly, this simple model predicts an instructive juxtacrine signal linking the roof and floor domains. To explore this prediction, we define a mechanistic model that integrates the combined effects of cellular genetic networks, cell communication and network adjustment through developmental events. Moreover, we focus on the anterior competence region, and postulate that early BMP signaling participates with early EGF signaling in its specification. This model accurately simulates wild type pattern formation and is able to reproduce, with unprecedented level of precision and completeness, various published gain-of-function and loss-of-function experiments, including perturbations of the BMP pathway previously seen as conflicting results. The result is a coherent model built upon rules that may be generalized to other epithelia and developmental systems.
- Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)Publication . Waltemath, Dagmar; Bergmann, Frank T.; Chaouiya, Claudine; Czauderna, Tobias; Gleeson, Padraig; Goble, Carole; Golebiewski, Martin; Hucka, Michael; Juty, Nick; Krebs, Olga; Le Novère, Nicolas; Mi, Huaiyu; Moraru, Ion I.; Myers, Chris J.; Nickerson, David; Olivier, Brett G.; Rodriguez, Nicolas; Schreiber, Falk; Smith, Lucian; Zhang, Fengkai; Bonnet, EricThe Computational Modeling in Biology Network (COMBINE) is an initiative to coordinate the development of community standards and formats in computational systems biology and related fields. This report summarizes the topics and activities of the fourth edition of the annual COMBINE meeting, held in Paris during September 16-20 2013, and attended by a total of 96 people. This edition pioneered a first day devoted to modeling approaches in biology, which attracted a broad audience of scientists thanks to a panel of renowned speakers. During subsequent days, discussions were held on many subjects including the introduction of new features in the various COMBINE standards, new software tools that use the standards, and outreach efforts. Significant emphasis went into work on extensions of the SBML format, and also into community-building. This year’s edition once again demonstrated that the COMBINE community is thriving, and still manages to help coordinate activities between different standards in computational systems biology.
- Modelling the onset of senescence at the G1/S cell cycle checkpointPublication . Mombach, José CM; Bugs, Cristhian A; Chaouiya, ClaudineDNA damage (single or double-strand breaks) triggers adapted cellular responses. These responses are elicited through signalling pathways, which activate cell cycle checkpoints and basically lead to three cellular fates: cycle arrest promoting DNA repair, senescence (permanent arrest) or cell death. Cellular senescence is known for having a tumour-suppressive function and its regulation arouses a growing scientific interest. Here, we advance a qualitative model covering DNA damage response pathways, focusing on G1/S checkpoint enforcement, supposedly more sensitive to arrest than G2/M checkpoint.
- Model Checking to Assess T-Helper Cell PlasticityPublication . Abou-Jaoudé, Wassim; Monteiro, Pedro T.; Naldi, Aurélien; Grandclaudon, Maximilien; Soumelis, Vassili; Chaouiya, Claudine; Thieffry, DenisComputational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.