Claudine Chaouiya

Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance

BackgroundIn breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.ResultsFirst, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.ConclusionIn this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.

Logical modelling of regulatory networks with GINsim 2.3

Many important problems in cell biology require the consideration of dense nonlinear interactions between functional modules. The requirement of computer simulation for the understanding of cellular processes is now widely accepted, and a variety of modelling frameworks have been designed to meet this need. Here, we present a novel public release of the Gene Interaction Network simulation suite (GINsim), a software designed for the qualitative modelling and analysis of regulatory networks. The main functionalities of GINsim are illustrated through the analysis of a logical model for the core network controlling the fission yeast cell cycle. The last public release of GINsim (version 2.3), as well as development versions, can be downloaded from the dedicated website (http://gin.univ-mrs.fr/GINsim/), which further includes a model library, along with detailed tutorial and user manual.

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

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.

Path2Models: large-scale generation of computational models from biochemical pathway maps

BackgroundSystems 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.ResultsTo increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps.ConclusionsTo date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized.

Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E.Coli

MOTIVATION The integrated dynamical modelling of mixed metabolic/genetic networks constitutes one of the challenges of systems biology. Furthermore, as most of available data about genetic and metabolic regulations are qualitative, there is a pressing need for rigorous qualitative mathematical approaches. RESULTS On the basis of two established formalisms, the logical modelling of genetic regulatory networks and the Petri net modelling of metabolic networks, we propose a systematic approach for the modelling of regulated metabolic networks. This approach leans on previous work defining a systematic procedure to translate logical regulatory graphs into standard (discrete) Petri nets (PNs). This approach is illustrated by the qualitative modelling of the biosynthesis of tryptophan (Trp) in Escherichia coli, taking into account two types of regulatory feedbacks: the direct inhibition of the first enzyme of the pathway by the final product of the pathway, and the transcriptional inhibition of the Trp operon by the Trp-repressor complex. On the basis of this integrated PN model, we further indicate how available dynamical analysis tools can be applied to obtain significant insights in the behaviour of the system. AVAILABILITY The software GINsim for the logical modelling of genetic regulatory networks together with the PN model of the regulated Trp biosynthesis pathway are available at: http://gin.univ-mrs.fr/GINsim.

Decision diagrams for the representation and analysis of logical models of genetic networks

The complexity of biological regulatory networks calls for the development of proper mathematical methods to model their structures and to obtain insight in their dynamical behaviours. One qualitative approach consists in modelling regulatory networks in terms of logical equations (using either Boolean or multi-valued discretisation). In this paper, we propose a novel implementation of the generalised logical formalism by means of Multi-valued Decision Diagrams. We show that the use of this representation enables the development of efficient algorithms for the analysis of specific dynamical properties of the regulatory graphs. In particular, we address the question of determining conditions insuring the functionality of feedback circuits, as well as the identification of stable states. Finally, we apply these algorithms to logical models of T cell activation and differentiation.

Segmenting the fly embryo: logical analysis of the role of the segment polarity cross-regulatory module.

Initially activated by the pair-rule genes, the expression patterns of the segment polarity genes engrailed and wingless become consolidated through inter-cellular interactions between juxtaposed cells. We delineate a logical model focusing on a dozen molecular components at the core of the regulatory network controlling this process. Our model leads to the following conclusions: (1) the pair-rule signals, which activate engrailed and wingless genes independently of each other, need to be operative until the inter-cellular circuit involving these two genes is functional. This implies that the pair-rule pattern is instrumental both in determining the activation of the genes engrailed and wingless in rows of adjacent cells, and in consolidating these expression patterns; (2) the consolidation of engrailed and wingless expression patterns requires the simultaneous activation of both autocrine and paracrine Wingless-pathways, and the Hedgehog pathway; (3) protein kinase A plays at least two roles through the phosphorylation of Cubitus interruptus, the effector molecule of the Hedgehog signalling pathway and (4) the roles of Sloppy-paired and Naked in the delineation of the engrailed and wingless expression domains are emphasized as being important for segmental boundary formation. Moreover, the application of an original computational method leads to the delineation of a subset of crucial regulatory circuits enabling the coexistence of specific expression states at the cellular level, as well as specific combination of cellular states inter-connected through Wingless and Hedgehog signalling. Finally, the simulation of altered expressions of segment polarity genes leads to results consistent with the published data.

Petri net modelling of biological regulatory networks

The complexity of biological regulatory networks often defies the intuition of the biologist and calls for the development of proper mathematical methods to model their structures and to delineate their dynamical properties. One qualitative approach consists in modelling regulatory networks in terms of logical equations (using either Boolean or multi-level discretisations). The Petri Net (PN) formalism offers a complementary framework to analyse the dynamical behaviour of large systems, either from a qualitative or from a quantitative point of view. Our proposal consists in articulating the logical approach with the PN formalism. In a previous work, we have already defined a systematic re-writing of Boolean regulatory models into a standard PN formalism. In this paper, we propose a rigorous and systematic mapping of multi-level logical regulatory models into specific standard Petri nets, called Multi-level Regulatory Petri Nets (MRPNs). We further propose some reduction strategies. Consequently, the resulting models become amenable to the algebraic and computational analyses used by the PN community. To illustrate our approach, we apply it to a multi-level logical model of the genetic switch controlling the lysis-lysogeny decision in the lambda bacteriophage.

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools

BackgroundQualitative 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.ResultsWe present the Systems Biology Markup Language (SBML) Qualitative Models Package (“qual”), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.ConclusionsSBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

Qualitative modelling of genetic networks: From logical regulatory graphs to standard Petri nets

In this paper, a systematic rewriting of logical genetic regulatory graphs in terms of standard Petri net models is proposed. We show that, in the Boolean case, the combination of the logical approach with the standard Petri net framework enables the analysis of isolated regulatory circuits, confirming their most fundamental dynamical properties. Furthermore, two more realistic applications are also presented, the first dealing with the control of the early cell cycles in the developing fly, the second dealing with flower morphogenesis.