Robustness analysis of biochemical network models

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

Abstract

Biological systems which have been experimentally verified to be robust to significant changes in their environments require mathematical models which 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. This paper describes 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 in this paper 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.

• Item Type: Articles
• Keywords: Robustness, Dictyostelium discoideum, cAMP oscillation.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kim, Dr Jongrae
• Authors: Kim, J., Bates, D.G., Postlethwaite, I., Ma, L., and Iglesias, P.A.
• Subjects: Q Science > QH Natural history > QH301 Biology; Q Science > QH Natural history > QH345 Biochemistry
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: Systems Biology
• ISSN: 1751-8849