Modeling of stochastic biological processes with non-polynomial propensities using non-central conditional moment equation

Atefeh Kazeroonian, Fabian J. Theis, Jan Hasenauer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Biological processes exhibiting stochastic fluctuations are mainly modeled using the Chemical Master Equation (CME). As a direct simulation of the CME is often computationally intractable, we recently introduced the Method of Conditional Moments (MCM). The MCM is a hybrid approach to approximate the statistics of the CME solution. In this work, we provide a more comprehensive formulation of the MCM by using non-central conditional moments instead of central conditional moments. The modified formulation allows for additional insight into the model structure and for extensions to higher-order reactions and non-polynomial propensity functions. The properties of the non-central MCM are analyzed using a model for the regulation of pili formation on the surface of bacteria, which possesses rational propensity functions.

Original languageEnglish
Title of host publication19th IFAC World Congress IFAC 2014, Proceedings
EditorsEdward Boje, Xiaohua Xia
PublisherIFAC Secretariat
Pages1729-1735
Number of pages7
ISBN (Electronic)9783902823625
DOIs
StatePublished - 2014
Event19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume19
ISSN (Print)1474-6670

Conference

Conference19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014
Country/TerritorySouth Africa
CityCape Town
Period24/08/1429/08/14

Keywords

  • Chemical master equation
  • Moment equations
  • Stochastic modeling

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