Robustness analysis of biochemical network models

J. Kim, D. G. Bates, I. Postlethwaite, L. Ma, P. A. Iglesias

Research output: Contribution to journalArticle

62 Scopus citations

Abstract

Biological systems that have been experimentally verified to be robust to significant changes in their environments require mathematical models that are themselves robust. In this context, a necessary condition for model robustness is that the model dynamics should not be sensitive to small variations in the model's parameters. Robustness analysis problems of this type have been extensively studied in the field of robust control theory and have been found to be very difficult to solve in general. The authors describe how some tools from robust control theory and nonlinear optimisation can be used to analyse the robustness of a recently proposed model of the molecular network underlying adenosine 3′,5′-cyclic monophosphate (cAMP) oscillations observed in fields of chemotactic Dictyostelium cells. The network model, which consists of a system of seven coupled nonlinear differential equations, accurately reproduces the spontaneous oscillations in cAMP observed during the early development of D. discoideum. The analysis by the authors reveals, however, that very small variations in the model parameters can effectively destroy the required oscillatory dynamics. A biological interpretation of the analysis results is that correct functioning of a particular positive feedback loop in the proposed model is crucial to maintaining the required oscillatory dynamics.

Original languageEnglish (US)
Pages (from-to)96-104
Number of pages9
JournalIEE Proceedings: Systems Biology
Volume153
Issue number3
DOIs
StatePublished - May 1 2006

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Keywords

  • A0230
  • A8710
  • A8715P
  • A8725F

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Cell Biology

Cite this

Kim, J., Bates, D. G., Postlethwaite, I., Ma, L., & Iglesias, P. A. (2006). Robustness analysis of biochemical network models. IEE Proceedings: Systems Biology, 153(3), 96-104. https://doi.org/10.1049/ip-syb:20050024