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

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 mult...

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Vydané v:Genome research Ročník 13; číslo 11; s. 2467
Hlavní autori: Moles, Carmen G, Mendes, Pedro, Banga, Julio R
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.11.2003
Predmet:
Algorithms
Computational Biology - methods
Computational Biology - statistics & numerical data
Computer Simulation
Evolution, Molecular
Genetic Engineering - methods
HIV Protease - chemistry
HIV Protease - genetics
HIV Protease - metabolism
HIV Protease Inhibitors - chemistry
Models, Chemical
Molecular Biology - methods
Molecular Biology - statistics & numerical data
Nonlinear Dynamics
Predictive Value of Tests
Software - statistics & numerical data
Stochastic Processes
ISSN:1088-9051
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Shrnutí: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.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1088-9051
DOI:10.1101/gr.1262503