TAILIEUCHUNG - Báo cáo khoa học: Systems biology: parameter estimation for biochemical models

Experimental design has a long tradition in statistics, engineering and life sciences, dating back to the beginning of the last century when optimal designs for industrial and agricultural trials were considered. In cell biol-ogy, the use of mathematical modeling approaches raises new demands on experimental planning. | MINIREVIEW Systems biology parameter estimation for biochemical models Maksat Ashyraliyev1 Yves Fomekong-Nanfack2 Jaap A. Kaandorp2 and Joke G. Blom1 1 Centrum voor Wiskunde en Informatica Amsterdam The Netherlands 2 Section ComputationalScience University of Amsterdam The Netherlands Keywords a prioiri and a posteriori identifiability local and global optimization parameter estimation Correspondence J. G. Blom Centrum voor Wiskunde en Informatica Science Park 123 1098 XG Amsterdam The Netherlands Fax 31 20 5924199 Tel 31 20 5924263 E-mail Mathematical models of biological processes have various applications to assist in understanding the functioning of a system to simulate experiments before actually performing them to study situations that cannot be dealt with experimentally etc. Some parameters in the model can be directly obtained from experiments or from the literature. Others have to be inferred by comparing model results to experiments. In this minireview we discuss the identifiability of models both intrinsic to the model and taking into account the available data. Furthermore we give an overview of the most frequently used approaches to search the parameter space. Received 8 April2008 revised 21 October 2008 accepted 28 November 2008 doi Introduction Parameter estimation in systems biology is usually part of an iterative process to develop data-driven models for biological systems that should have predictive value. In this minireview we discuss how to obtain parameters for mathematical models by data fitting. We restrict ourselves to the case where a deterministic model in the form of a mathematical function-based model is available such as a system of differential and algebraic equations. For example in the case of a biochemical process hypotheses based on the knowledge of the underlying network structure of a pathway are translated into a system of kinetic equations parameters are obtained from literature

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