Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0

The logical formalism is well adapted to model large cellular networks, for which detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step towards the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.

[1]  Aurélien Naldi,et al.  Decision Diagrams for the Representation and Analysis of Logical Models of Genetic Networks , 2007, CMSB.

[2]  Denis Thieffry,et al.  Segmenting the fly embryo: a logical analysis of the pair-rule cross-regulatory module. , 2003, Journal of theoretical biology.

[3]  Denis Thieffry,et al.  Logical Modeling and Dynamical Analysis of Cellular Networks , 2016, Front. Genet..

[4]  M. Oren,et al.  mdm2 expression is induced by wild type p53 activity. , 1993, The EMBO journal.

[5]  Aurélien Naldi,et al.  Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle , 2006, ISMB.

[6]  A. Levine,et al.  Surfing the p53 network , 2000, Nature.

[7]  Denis Thieffry,et al.  Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling , 2015, PLoS Comput. Biol..

[8]  Sarala M. Wimalaratne,et al.  The Systems Biology Graphical Notation , 2009, Nature Biotechnology.

[9]  J. Levine,et al.  Surfing the p53 network , 2000, Nature.

[10]  D. Thieffry,et al.  Dynamical Analysis of the Regulatory Network Defining the Dorsal–Ventral Boundary of the Drosophila Wing Imaginal Disc , 2006, Genetics.

[11]  Denis Thieffry,et al.  Dynamical roles of biological regulatory circuits , 2007, Briefings Bioinform..

[12]  Kwang-Hyun Cho,et al.  Attractor Landscape Analysis Reveals Feedback Loops in the p53 Network That Control the Cellular Response to DNA Damage , 2012, Science Signaling.

[13]  Wei Wang,et al.  Two-phase dynamics of p53 in the DNA damage response , 2011, Proceedings of the National Academy of Sciences.

[14]  Aurélien Naldi,et al.  Dynamically consistent reduction of logical regulatory graphs , 2011, Theor. Comput. Sci..

[15]  Luis Mendoza,et al.  A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity , 2015, PLoS Comput. Biol..

[16]  D. Thieffry,et al.  A logical analysis of the Drosophila gap-gene system. , 2001, Journal of theoretical biology.

[17]  Andrea Ciliberto,et al.  Steady States and Oscillations in the p53/Mdm2 Network , 2005, Cell cycle.

[18]  Eugenio Azpeitia,et al.  Gene regulatory network models for floral organ determination. , 2014, Methods in molecular biology.

[19]  Claudine Chaouiya,et al.  Quantification of reachable attractors in asynchronous discrete dynamics , 2014, ArXiv.

[20]  Aurélien Naldi,et al.  Diversity and Plasticity of Th Cell Types Predicted from Regulatory Network Modelling , 2010, PLoS Comput. Biol..

[21]  Claudine Chaouiya,et al.  A Discrete Model of Drosophila Eggshell Patterning Reveals Cell-Autonomous and Juxtacrine Effects , 2014, PLoS Comput. Biol..

[22]  Steffen Klamt,et al.  SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools , 2013, BMC Systems Biology.

[23]  Denis Thieffry,et al.  Logical modeling of lymphoid and myeloid cell specification and transdifferentiation , 2017, Proceedings of the National Academy of Sciences.

[24]  Denis Thieffry,et al.  Mathematical Modelling of Cell-Fate Decision in Response to Death Receptor Engagement , 2010, PLoS Comput. Biol..

[25]  L. Wiesmüller,et al.  p53 in recombination and repair , 2006, Cell Death and Differentiation.

[26]  R Thomas,et al.  Dynamical behaviour of biological regulatory networks--I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state. , 1995, Bulletin of mathematical biology.

[27]  Denis Thieffry,et al.  Genetic control of flower morphogenesis in Arabidopsis thaliana: a logical analysis , 1999, Bioinform..

[28]  Denis Thieffry,et al.  Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation , 2016, Bioinform..

[29]  Denis Thieffry,et al.  miR-9 controls the timing of neurogenesis through the direct inhibition of antagonistic factors. , 2012, Developmental cell.

[30]  E. R. de Arantes e Oliveira The Discrete Model , 1975 .

[31]  D. Thieffry,et al.  Modular logical modelling of the budding yeast cell cycle. , 2009, Molecular bioSystems.

[32]  E. Álvarez-Buylla,et al.  Dynamics of the genetic regulatory network for Arabidopsis thaliana flower morphogenesis. , 1998, Journal of theoretical biology.

[33]  Xinglai Ji,et al.  libSRES: a C library for stochastic ranking evolution strategy for parameter estimation , 2006, Bioinform..

[34]  L. Mayo,et al.  The PTEN, Mdm2, p53 tumor suppressor-oncoprotein network. , 2002, Trends in biochemical sciences.

[35]  Adrien Richard,et al.  On Circuit Functionality in Boolean Networks , 2013, Bulletin of mathematical biology.

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

[37]  Masahiro Okamoto,et al.  Stochasticity of Intranuclear Biochemical Reaction Processes Controls the Final Decision of Cell Fate Associated with DNA Damage , 2014, PloS one.

[38]  Pedro T. Monteiro,et al.  Dynamical modeling and analysis of large cellular regulatory networks. , 2013, Chaos.

[39]  Denis Thieffry,et al.  Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc , 2008, ECCB.

[40]  N. L. La Thangue,et al.  Mdm2 targets the p53 transcription cofactor JMY for degradation , 2007, EMBO reports.

[41]  R. Thomas,et al.  Dynamical behaviour of biological regulatory networks--II. Immunity control in bacteriophage lambda. , 1995, Bulletin of mathematical biology.

[42]  Luis Mendoza,et al.  A dynamical model of the regulatory network controlling lymphopoiesis , 2015, Biosyst..

[43]  Aurélien Naldi,et al.  MaBoSS 2.0: an environment for stochastic Boolean modeling , 2017, Bioinform..

[44]  Aurélien Naldi,et al.  Model Checking to Assess T-Helper Cell Plasticity , 2014, bioRxiv.

[45]  Bert Vogelstein,et al.  Oncoprotein MDM2 conceals the activation domain of tumour suppressor p53 , 1993, Nature.

[46]  D. Thieffry,et al.  Segmenting the fly embryo: logical analysis of the role of the segment polarity cross-regulatory module. , 2008, The International journal of developmental biology.

[47]  Elena R. Alvarez-Buylla,et al.  A minimal regulatory network of extrinsic and intrinsic factors recovers observed patterns of CD4+ T cell differentiation and plasticity , 2015 .

[48]  É. Remy,et al.  A Modeling Approach to Explain Mutually Exclusive and Co-Occurring Genetic Alterations in Bladder Tumorigenesis. , 2015, Cancer research.

[49]  Holger Fröhlich,et al.  Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance , 2009, BMC Systems Biology.

[50]  M. Kaufman,et al.  From structure to dynamics: frequency tuning in the p53-Mdm2 network I. Logical approach. , 2009, Journal of theoretical biology.

[51]  Denis Thieffry,et al.  Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes , 2016, PLoS Comput. Biol..

[52]  Wei Gu,et al.  p53 ubiquitination: Mdm2 and beyond. , 2006, Molecular cell.

[53]  Jun Cui,et al.  A plausible model for bimodal p53 switch in DNA damage response , 2014, FEBS letters.

[54]  Tomasz Lipniacki,et al.  Oscillations and bistability in the stochastic model of p53 regulation. , 2008, Journal of theoretical biology.

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

[56]  M. Kaufman,et al.  From structure to dynamics: frequency tuning in the p53-Mdm2 network. II Differential and stochastic approaches. , 2010, Journal of theoretical biology.

[57]  Aurélien Naldi,et al.  Cooperative development of logical modelling standards and tools with CoLoMoTo , 2014, bioRxiv.