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

By Carmen G. Moles, Pedro Mendes, Julio R. Banga. Genome Research, 2003

965 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f85cf309e6f438637c00fe91d3bc8405cf619779

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Citations in the corpus

Stochastic modelling for quantitative description of heterogeneous biological systems (2009, methodology): “Researchers have been fitting deterministic models to time course data for decades, and many simple approaches are non-statistica”
Parameter Estimation and Model Selection in Computational Biology (2010, methodology): “The latter is suggested as the method of choice for large parameter estimation problems [7].”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “problem (NLP) subject to nonlinear differential-algebraic constraints [172].”
Quantitative and logic modelling of gene and molecular networks (2015, methodology): “Logic models are characterized by the assignment of new values to model variables on the basis of the result of logical statements (107).”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, neutral): “[Moles et al., 2003] Moles, C., Mendes, P., and Banga, J. (2003).”
Systems biology informed deep learning for inferring parameters and hidden dynamics (2019, neutral): “In particular, extensive research has been conducted on the applications of different optimization techniques, such as linear and nonlinear least-squares fitting [4], genetic algorithms [5], evolutionary computation [6], and more [7].”
Automated refinement and inference of analytical models for metabolic networks (2011, methodology): “Cybernetic modeling [47] Nonlinear Dynamic Biochemical systems theory (S-Systems approach) [45, 46] Nonlinear Dynamic Localized”