Abstract
Motivation: Logic modeling is a useful tool to study signal transduction across multiple pathways. Logic models can be generated by training a network containing the prior knowledge to phospho-proteomics data. The training can be performed using stochastic optimization procedures, but these are unable to guarantee a global optima or to report the complete family of feasible models. This, however, is essential to provide precise insight in the mechanisms underlaying signal transduction and generate reliable predictions.Results: We propose the use of Answer Set Programming to explore exhaustively the space of feasible logic models. Toward this end, we have developed caspo, an open-source Python package that provides a powerful platform to learn and characterize logic models by leveraging the rich modeling language and solving technologies of Answer Set Programming. We illustrate the usefulness of caspo by revisiting a model of pro-growth and inflammatory pathways in liver cells. We show that, if experimental error is taken into account, there are thousands (11 700) of models compatible with the data. Despite the large number, we can extract structural features from the models, such as links that are always (or never) present or modules that appear in a mutual exclusive fashion. To further characterize this family of models, we investigate the input–output behavior of the models. We find 91 behaviors across the 11 700 models and we suggest new experiments to discriminate among them. Our results underscore the importance of characterizing in a global and exhaustive manner the family of feasible models, with important implications for experimental design.Availability: caspo is freely available for download (license GPLv3) and as a web service at http://caspo.genouest.org/.Supplementary information: Supplementary materials are available at Bioinformatics online.Contact: santiago.videla@irisa.fr
Highlights
Predictive models of biological networks are a main component of systems biology
Inspired by truth tables in propositional logics, we introduce the concept of Global Truth Tables (GTTs) as a way of describing the input–output behavior of a Boolean logic model
3 RESULTS To illustrate the use of caspo, we use a model of pro-growth and pro-inflammatory model in liver cells
Summary
Predictive models of biological networks are a main component of systems biology. For a certain system of interest, if enough information is available about the biomolecules that constitute it and their interactions, one can convert this prior knowledge into a mathematical model (e.g. a set of differential equations or logic rules) that can be simulated. One determines the model parameters (for example, kinetic constants in a biochemical model) to obtain the most plausible model given the data This is normally achieved by defining an objective function that describes the goodness of the model based on the data that is subsequently optimized (Banga, 2008). This training process is not a trivial task owing to factors including experimental error, limitations in the amount of data available, incompleteness of our prior knowledge and inherent mathematical properties of the models. The model is said to be non-identifiable (Kreutz and Timmer, 2009; Walter and Pronzato, 1996)
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