Graph Representations of Monotonic Boolean Model Pools

In the face of incomplete data on a system of interest, constraint-based Boolean modeling still allows for elucidating system characteristics by analyzing sets of models consistent with the available information. In this setting, methods not depending on consideration of every single model in the set are necessary for efficient analysis. Drawing from ideas developed in qualitative differential equation theory, we present an approach to analyze sets of monotonic Boolean models consistent with given signed interactions between systems components. We show that for each such model constraints on its behavior can be derived from a universally constructed state transition graph essentially capturing possible sign changes of the derivative. Reachability results of the modeled system, e.g., concerning trap or no-return sets, can then be derived without enumerating and analyzing all models in the set. The close correspondence of the graph to similar objects for differential equations furthermore opens up ways to relate Boolean and continuous models.

[1]  René Thomas On the Relation Between the Logical Structure of Systems and Their Ability to Generate Multiple Steady States or Sustained Oscillations , 1981 .

[2]  Julio Saez-Rodriguez,et al.  caspo: a toolbox for automated reasoning on the response of logical signaling networks families , 2016, Bioinform..

[3]  R. Thomas,et al.  Multistationarity, the basis of cell differentiation and memory. II. Logical analysis of regulatory networks in terms of feedback circuits. , 2001, Chaos.

[4]  H. D. Jong,et al.  Qualitative simulation of genetic regulatory networks using piecewise-linear models , 2004, Bulletin of mathematical biology.

[5]  Joachim Niehren,et al.  Knockout Prediction for Reaction Networks with Partial Kinetic Information , 2012, VMCAI.

[6]  Denis Thieffry,et al.  Formal derivation of qualitative dynamical models from biochemical networks , 2016, Biosyst..

[7]  Hidde de Jong,et al.  Qualitative Simulation of Genetic Regulatory Networks: Method and Application , 2001, IJCAI.

[8]  R. Thomas,et al.  Multistationarity, the basis of cell differentiation and memory. I. Structural conditions of multistationarity and other nontrivial behavior. , 2001, Chaos.

[9]  Kirsten Thobe,et al.  Data-driven optimizations for model checking of multi-valued regulatory networks , 2016, Biosyst..

[10]  Heike Siebert,et al.  PyBoolNet: a python package for the generation, analysis and visualization of boolean networks , 2016, Bioinform..

[11]  Adrien Richard,et al.  Asynchronous Simulation of Boolean Networks by Monotone Boolean Networks , 2016, ACRI.

[12]  Denis Thieffry,et al.  Graphic requirements for multistability and attractive cycles in a Boolean dynamical framework , 2008, Adv. Appl. Math..

[13]  Misa Keinänen Techniques for solving Boolean equation systems , 2006 .

[14]  Klaus Eisenack Model Ensembles for Natural Resource Management: Extensions of qualitative differential equations using graph theory andviability theory , 2006 .

[15]  Steffen Klamt,et al.  Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling , 2009, BMC Systems Biology.