1. Predicting changes of reaction networks with partial kinetic information
- Author
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Cristian Versari, Joachim Niehren, Philippe Jacques, Mathias John, François Coutte, BioComputing, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Linking Dynamic Data (LINKS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Procédés Biologiques, Génie Enzymatique et Microbien - EA1026 (ProBioGEM), Université de Lille, Sciences et Technologies, Institut Charles Viollette (ICV) - EA 7394 (ICV), Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille, The authors would like to thank Emilie Allart and Debarun Dhali for proofreading. This research was supported by the European Union (Marie Curie ITN AMBER, 317338), the French National Research Agency (ICEBERG-ANR-10-BINF-06-01), and the University of Lille, through the BQR project 'Biologie synthétique pour la synthèse dirigée de peptides microbiens bioactifs'., European Project: 317338,EC:FP7:PEOPLE,FP7-PEOPLE-2012-ITN,AMBER(2013), Linking Dynamic Data [LINKS], Institut Charles Viollette (ICV) - EA 7394 [ICV], Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Ecole Centrale de Lille, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189 (CRIStAL), ProBioGEM, ANR-10-BINF-06-11,Iceberg,Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l’expression génique au niveau de la cellule unique(2011), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture, and Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Ecole Centrale de Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
- Subjects
0301 basic medicine ,Statistics and Probability ,Computer science ,Modeling language ,030106 microbiology ,Reaction networks ,Models, Biological ,General Biochemistry, Genetics and Molecular Biology ,Prime (order theory) ,Domain (software engineering) ,03 medical and health sciences ,Animals ,Humans ,abstract interpretation ,[INFO.INFO-BT]Computer Science [cs]/Biotechnology ,Constraint satisfaction problem ,genetic engineering ,Applied Mathematics ,model-based prediction ,systems biology ,General Medicine ,Petri net ,Abstract interpretation ,Constraint (information theory) ,Kinetics ,Qualitative reasoning ,030104 developmental biology ,constraint solving ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,Modeling and Simulation ,biotechnology ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,metabolic engineering ,Algorithm ,Metabolic Networks and Pathways ,Forecasting - Abstract
We wish to predict changes of reaction networks with partial kinetic information that lead to target changes of their steady states. The changes may be either increases or decreases of influxes, 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 description of its kinetics, 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 knockouts 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 John et al. (2013) and Niehren et al. (2015) while adding a new prediction algorithm for multiple gene knockouts. An application to single gene knockout prediction for surfactin overproduction was presented in Coutte et al. (2015). It illustrates the adequacy of the model-based predictions made by our algorithm in the wet lab.
- Published
- 2016