International Conference on Computational Methods in Systems Biology

CMSB 2015: Computational Methods in Systems Biology pp 77-89

Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks

  • Sergiy Bogomolov
  • Thomas A. Henzinger
  • Andreas Podelski
  • Jakob Ruess
  • Christian Schilling
Conference paper

DOI: 10.1007/978-3-319-23401-4_8

Volume 9308 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Bogomolov S., Henzinger T.A., Podelski A., Ruess J., Schilling C. (2015) Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. In: Roux O., Bourdon J. (eds) Computational Methods in Systems Biology. CMSB 2015. Lecture Notes in Computer Science, vol 9308. Springer, Cham

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.

Keywords

Stochastic reaction networks Continuous-time markov chains Parameter inference Moment closure 

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sergiy Bogomolov
    • 1
  • Thomas A. Henzinger
    • 1
  • Andreas Podelski
    • 2
  • Jakob Ruess
    • 1
  • Christian Schilling
    • 2
  1. 1.IST AustriaKlosterneuburgAustria
  2. 2.University of FreiburgFreiburgGermany