Model Integration and Crosstalk Analysis of Logical Regulatory Networks

Methods for model integration have become increasingly popular for understanding of the interplay between biological processes. In this work, we introduce an approach for coupling models taking uncertainties concerning the crosstalk into account. Using constraintbased modeling and formal verification techniques, a pool of possible integrated models is generated in agreement with previously validated behavior of the isolated models as well as additional experimental observations. Correlation- and causality-based analysis allows us to uncover the importance of particular crosstalk connections for specific functionalities leading to new biological insights and starting points for experimental design. We illustrate our approach studying crosstalk between the MAPK and mTor signaling pathways.

[1]  Laurence Calzone,et al.  Correction: Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision , 2013, PLoS Computational Biology.

[2]  David Safránek,et al.  Parameter Identification and Model Ranking of Thomas Networks , 2012, CMSB.

[3]  Christel Baier,et al.  Principles of model checking , 2008 .

[4]  S. Kimball,et al.  ERK and Akt signaling pathways function through parallel mechanisms to promote mTORC1 signaling. , 2011, American journal of physiology. Cell physiology.

[5]  Claudine Chaouiya,et al.  Composition and abstraction of logical regulatory modules: application to multicellular systems , 2013, Bioinform..

[6]  J. Bown,et al.  Feedforward and feedback regulation of the MAPK and PI3K oscillatory circuit in breast cancer. , 2013, Cellular signalling.

[7]  Pascale Le Gall,et al.  An LTL Model Checking Approach for Biological Parameter Inference , 2014, ICFEM.

[8]  B. Kholodenko,et al.  Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. , 2000, European journal of biochemistry.

[9]  Denis Thieffry,et al.  Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision , 2013, PLoS Comput. Biol..

[10]  Ian Stark,et al.  The Continuous pi-Calculus: A Process Algebra for Biochemical Modelling , 2008, CMSB.

[11]  Adrien Richard,et al.  Application of formal methods to biological regulatory networks: extending Thomas' asynchronous logical approach with temporal logic. , 2004, Journal of theoretical biology.

[12]  S. Chandarlapaty,et al.  Rapid induction of apoptosis by PI3K inhibitors is dependent upon their transient inhibition of RAS-ERK signaling. , 2014, Cancer discovery.

[13]  Julio Saez-Rodriguez,et al.  Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming , 2013, Bioinform..

[14]  H. Siebert.  Analysis of Discrete Bioregulatory Networks Using Symbolic Steady States , 2011, Bulletin of mathematical biology.

[15]  J. Engelman,et al.  The PI3K pathway as drug target in human cancer. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  Rui-Sheng Wang,et al.  Boolean modeling in systems biology: an overview of methodology and applications , 2012, Physical biology.

[17]  René Thomas.  Regulatory networks seen as asynchronous automata: A logical description , 1991 .

[18]  Steffen Klamt,et al.  The Logic of EGFR/ErbB Signaling: Theoretical Properties and Analysis of High-Throughput Data , 2009, PLoS Comput. Biol..

[19]  Heike Siebert,et al.  Esther: Introducing an Online Platform for ParameterIdentification of Boolean Networks , 2013 .

[20]  J. Blenis,et al.  The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation. , 2011, Trends in biochemical sciences.