PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
Kazeroonian, A. ; Theis, F.J. ; Hasenauer, J.

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

IFAC PapersOnline 19, 1729-1735 (2014)
Publ. Version/Full Text DOI
Open Access Gold
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.
Impact Factor
Scopus SNIP
Scopus
Cited By
Altmetric
0.000
0.170
8
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Editors Boje, E.* ; Xia, X.*
Keywords Chemical Master Equation ; Moment Equations ; Stochastic Modeling
Language english
Publication Year 2014
HGF-reported in Year 2015
ISSN (print) / ISBN 2405-8963
e-ISSN 1474-6670
Conference Title 19th World Congress of the International Federation of Automatic Control (IFAC)
Conference Date 24 - 29 August 2014
Conference Location Cape Town, South Africa
Journal IFAC-PapersOnLine
Quellenangaben Volume: 19, Issue: , Pages: 1729-1735 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Frankfurt ; München [u.a.]
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
DOI 10.3182/20140824-6-ZA-1003.02298
Scopus ID 84929714133
Erfassungsdatum 2015-06-03
[➜Report correction]