Literature DB >> 17189866

Mathematical modeling as a tool for investigating cell cycle control networks.

Jill C Sible1, John J Tyson.   

Abstract

Although not a traditional experimental "method," mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.

Mesh:

Year:  2007        PMID: 17189866      PMCID: PMC1993813          DOI: 10.1016/j.ymeth.2006.08.003

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  36 in total

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5.  Parameter estimation for a mathematical model of the cell cycle in frog eggs.

Authors:  Jason W Zwolak; John J Tyson; Layne T Watson
Journal:  J Comput Biol       Date:  2005       Impact factor: 1.479

6.  Quantitative characterization of a mitotic cyclin threshold regulating exit from mitosis.

Authors:  Frederick R Cross; Lea Schroeder; Martin Kruse; Katherine C Chen
Journal:  Mol Biol Cell       Date:  2005-02-16       Impact factor: 4.138

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9.  Globally optimised parameters for a model of mitotic control in frog egg extracts.

Authors:  J W Zwolak; J J Tyson; L T Watson
Journal:  Syst Biol (Stevenage)       Date:  2005-06

Review 10.  Mechanisms and regulation of the degradation of cyclin B.

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  30 in total

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5.  Model composition for macromolecular regulatory networks.

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Review 6.  Introductory review of computational cell cycle modeling.

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Journal:  Methods Mol Biol       Date:  2014

Review 7.  Mapping the architecture of the HIV-1 Tat circuit: A decision-making circuit that lacks bistability and exploits stochastic noise.

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Journal:  Methods       Date:  2010-12-16       Impact factor: 3.608

8.  Mathematical modeling of fission yeast Schizosaccharomyces pombe cell cycle: exploring the role of multiple phosphatases.

Authors:  P Anbumathi; Sharad Bhartiya; K V Venkatesh
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Review 9.  Decoding the quantitative nature of TGF-beta/Smad signaling.

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10.  Deterministic and stochastic models of genetic regulatory networks.

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