Literature DB >> 16986262

Smooth bistable S-systems.

E O Voit1.   

Abstract

S-systems have been used as models of biochemical systems for over 30 years. One of their hallmarks is that, although they are highly non-linear, their steady states are characterised by linear equations. This allows streamlined analyses of stability, sensitivities and gains as well as objective, mathematically controlled comparisons of similar model designs. Regular S-systems have a unique steady state at which none of the system variables is zero. This makes it difficult to represent switching phenomena, as they occur, for instance, in the expression of genes, cell cycle phenomena and signal transduction. Previously, two strategies were proposed to account for switches. One was based on a technique called recasting, which permits the modelling of any differentiable non-linearities, including bistability, but typically does not allow steady-state analyses based on linear equations. The second strategy formulated the switching system in a piece-wise fashion, where each piece consisted of a regular S-system. A representation gleaned from a simplified form of recasting is proposed and it is possible to divide the characterisation of the steady states into two phases, the first of which is linear, whereas the other is non-linear, but easy to execute. The article discusses a representative pathway with two stable states and one unstable state. The pathway model exhibits strong separation between the stable states as well as hysteresis.

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Year:  2005        PMID: 16986262     DOI: 10.1049/ip-syb:20050063

Source DB:  PubMed          Journal:  Syst Biol (Stevenage)        ISSN: 1741-2471


  2 in total

Review 1.  Biological systems modeling and analysis: a biomolecular technique of the twenty-first century.

Authors:  Gautam Goel; I-Chun Chou; Eberhard O Voit
Journal:  J Biomol Tech       Date:  2006-09

2.  Polynomial superlevel set representation of the multistationarity region of chemical reaction networks.

Authors:  AmirHosein Sadeghimanesh; Matthew England
Journal:  BMC Bioinformatics       Date:  2022-09-27       Impact factor: 3.307

  2 in total

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