Motivation: Quantitative models are increasingly used in systems biology. Usually, these quantitative models involve many molecular species and their associated reactions. When simulating a tissue with thousands of cells, using these large models becomes computationally and time limiting.
Results: In this paper, we propose to construct abstractions using information theory notions. Entropy is used to discretize the state space and mutual information is used to select a subset of all original variables and their mutual dependencies. We apply our method to an hybrid model of TRAIL-induced apoptosis in HeLa cell. Our abstraction, represented as a Dynamic Bayesian Network (DBN), reduces the number of variables from 92 to 10, and accelerates numerical simulation by an order of magnitude, yet preserving essential features of cell death time distributions.
Availability and implementation: This approach is implemented in the tool DBNizer, freely available at http://perso.crans.org/genest/DBNizer .
Contact: gregory.batt@inria.fr or bgenest@irisa.fr.
Supplementary information: Supplementary data are available at Bioinformatics online.
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