Revisiting the Training of Logic Models of Protein Signaling Networks with ASP

A fundamental question in systems biology is the construction and training to data of mathematical models. Logic formalisms have become very popular to model signaling networks because their simplicity allows us to model large systems encompassing hundreds of proteins. An approach to train (Boolean) logic models to high-throughput phospho-proteomics data was recently introduced and solved using optimization heuristics based on stochastic methods. Here we demonstrate how this problem can be solved using Answer Set Programming (ASP), a declarative problem solving paradigm, in which a problem is encoded as a logical program such that its answer sets represent solutions to the problem. ASP has significant improvements over heuristic methods in terms of efficiency and scalability, it guarantees global optimality of solutions as well as provides a complete set of solutions. We illustrate the application of ASP with in silico cases based on realistic networks and data.

[1]  Emmanuel Barillot,et al.  BiNoM: a Cytoscape plugin for manipulating and analyzing biological networks , 2008, Bioinform..

[2]  Steffen Klamt,et al.  A Logical Model Provides Insights into T Cell Receptor Signaling , 2007, PLoS Comput. Biol..

[3]  Florian Dittmann,et al.  Automatic generation of causal networks linking growth factor stimuli to functional cell state changes , 2012, The FEBS journal.

[4]  Martin Gebser,et al.  On the Input Language of ASP Grounder Gringo , 2009, LPNMR.

[5]  Wolfgang Faber,et al.  Logic Programming and Nonmonotonic Reasoning , 2011, Lecture Notes in Computer Science.

[6]  Julio Saez-Rodriguez,et al.  Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge , 2011, Science Signaling.

[7]  Klaus Truemper,et al.  Logic Integer Programming Models for Signaling Networks , 2008, J. Comput. Biol..

[8]  Julio Saez-Rodriguez,et al.  Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli , 2011, PLoS Comput. Biol..

[9]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[10]  Alex M. Andrew,et al.  Knowledge Representation, Reasoning and Declarative Problem Solving , 2004 .

[11]  D. di Bernardo,et al.  How to infer gene networks from expression profiles , 2007, Molecular systems biology.

[12]  Gary D. Bader,et al.  Pathway Commons, a web resource for biological pathway data , 2010, Nucleic Acids Res..

[13]  Jens Nielsen,et al.  Reconstruction and logical modeling of glucose repression signaling pathways in Saccharomyces cerevisiae , 2009, BMC Systems Biology.

[14]  Steffen Klamt,et al.  A methodology for the structural and functional analysis of signaling and regulatory networks , 2006, BMC Bioinformatics.

[15]  Julio Saez-Rodriguez,et al.  Modeling signaling networks using high-throughput phospho-proteomics. , 2012, Advances in experimental medicine and biology.

[16]  D. Lauffenburger,et al.  Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction , 2009, Molecular systems biology.

[17]  Steffen Klamt,et al.  Hypergraphs and Cellular Networks , 2009, PLoS Comput. Biol..

[18]  T. Thingholm,et al.  Strategies for quantitation of phosphoproteomic data , 2010, Expert review of proteomics.

[19]  Michael Hecker,et al.  Gene regulatory network inference: Data integration in dynamic models - A review , 2009, Biosyst..

[20]  Kenneth H. Buetow,et al.  PID: the Pathway Interaction Database , 2008, Nucleic Acids Res..

[21]  Martin Gebser,et al.  Conflict-Driven Answer Set Solving , 2007, IJCAI.

[22]  Julio Saez-Rodriguez,et al.  Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data , 2009, PLoS Comput. Biol..