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      Parameter estimation in biochemical pathways: a comparison of global optimization methods.

      Genome research

      Computational Biology, methods, statistics & numerical data, Computer Simulation, Evolution, Molecular, Genetic Engineering, HIV Protease, chemistry, genetics, metabolism, HIV Protease Inhibitors, Models, Chemical, Molecular Biology, Nonlinear Dynamics, Predictive Value of Tests, Software, Stochastic Processes, Algorithms

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          Abstract

          Here we address the problem of parameter estimation (inverse problem) of nonlinear dynamic biochemical pathways. This problem is stated as a nonlinear programming (NLP) problem subject to nonlinear differential-algebraic constraints. These problems are known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based) local optimization methods fail to arrive at satisfactory solutions. To surmount this limitation, the use of several state-of-the-art deterministic and stochastic global optimization methods is explored. A case study considering the estimation of 36 parameters of a nonlinear biochemical dynamic model is taken as a benchmark. Only a certain type of stochastic algorithm, evolution strategies (ES), is able to solve this problem successfully. Although these stochastic methods cannot guarantee global optimality with certainty, their robustness, plus the fact that in inverse problems they have a known lower bound for the cost function, make them the best available candidates.

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          Author and article information

          Journal
          10.1101/gr.1262503
          403766
          14559783

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