A model of yeast cell-cycle regulation based on a standard component modeling strategy for protein regulatory networks

Journal article

Authors/Editors

TEERAPHAN LAOMETTACHIT

Strategic Research Themes

No matching items found.

Publication Details

Author list: Laomettachit T., Chen K.C., Baumann W.T., Tyson J.J.

Publisher: Public Library of Science

Publication year: 2016

Journal: PLoS ONE (1932-6203)

Volume number: 11

Issue number: 5

ISSN: 1932-6203

eISSN: 1932-6203

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969249015&doi=10.1371%2fjournal&partnerID=40&md5=9821763f19dc466341b9ac4a2181dd51

Languages: English-Great Britain (EN-GB)

View in Web of Science | View on publisher site | View citing articles in Web of Science

Abstract

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a "standard component" modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbฤ clb5ฤ mutant strain. Our simulations show that mathematical modeling with "standard components" can capture in quantitative detail many essential properties of cell cycle control in budding yeast. ฉ 2016 Laomettachit et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords