Predicting Changes of Reaction Networks with Partial Kinetic Information

Joachim Niehren 1, 2 Cristian Versari 2 Mathias John 2 François Coutte 3, 4 Philippe Jacques 3, 4
2 BioComputing
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : We wish to predict changes of reaction networks with partial kinetic information that lead to target changes of its steady states. The changes may be either influxes increases or decreases, reaction knockouts, or multiple changes of these two kinds. Our prime applications are knockout prediction tasks for metabolic and regulation networks.

In a first step, we propose a formal modeling language for reaction networks with partial kinetic information. The modeling language has a graphical syntax reminiscent to Petri nets. Each reaction in a model comes with a partial de- scription of its kinetics, that is based on a similarity relation on kinetic functions that we introduce. Such partial descriptions are able to model the regulation of existing metabolic networks, for which precise kinetic knowledge is usually not available.

In a second step, we develop prediction algorithms that can be applied to any reaction network modeled in our language. These algorithms perform qualitative reasoning based on abstract interpretation, by which the kinetic unknowns are abstracted away. Given a reaction network, abstract interpretation produces a finite domain constraint in a novel class. We show how to solve these finite domain constraints with an existing finite domain constraint solver, and how to interpret the solution sets as predictions of multiple reaction kockouts, that lead to a desired change of the steady states. We have implemented the prediction algorithm and integrated it into a prediction tool.

This journal article extends the two conference papers [1, 2] while adding a new prediction algorithm for multiple gene knockouts. An application to single gene knockout prediction for surfactin overproduction was presented in [3]. It illustrates the adequacy of the model-based predictions made by our algorithm in the wet lab.

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Article dans une revue
BioSystems, Elsevier, 2016, Special Issue of CMSB 2015, 149, pp.113-124
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Soumis le : mercredi 7 septembre 2016 - 17:05:57
Dernière modification le : mardi 3 juillet 2018 - 11:33:00

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  • HAL Id : hal-01239198, version 2

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Joachim Niehren, Cristian Versari, Mathias John, François Coutte, Philippe Jacques. Predicting Changes of Reaction Networks with Partial Kinetic Information . BioSystems, Elsevier, 2016, Special Issue of CMSB 2015, 149, pp.113-124. 〈hal-01239198v2〉

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