title: Adaptive moment closure for parameter inference of biochemical reaction networks.
abstract: Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space.
authors.affiliation: University of Freiburg, 79110 Freiburg, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Schilling
authors.affiliation: IST Austria, 3400 Klosterneuburg, Austria.
authors.forename: Sergiy
authors.initials: S
authors.lastname: Bogomolov
authors.affiliation: IST Austria, 3400 Klosterneuburg, Austria.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Henzinger
authors.affiliation: University of Freiburg, 79110 Freiburg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Podelski
authors.affiliation: IST Austria, 3400 Klosterneuburg, Austria. Electronic address: jakob.ruess@ist.ac.at.
authors.forename: Jakob
authors.initials: J
authors.lastname: Ruess
mesh_terms: D000818:Animals; D001669:Biochemical Phenomena / Q000502:physiology; D053263:Gene Regulatory Networks; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D013269:Stochastic Processes
keywords: Continuous-time Markov chains; Moment closure; Parameter inference; Stochastic reaction networks