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Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation.

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Mendes, P, Kell, D·1998·Bioinformatics

Abstract

MOTIVATION: The simulation of biochemical kinetic systems is a powerful approach that can be used for: (i) checking the consistency of a postulated model with a set of experimental measurements, (ii) answering 'what if?' questions and (iii) exploring possible behaviours of a model. Here we describe a generic approach to combine numerical optimization methods with biochemical kinetic simulations, which is suitable for use in the rational design of improved metabolic pathways with industrial significance (metabolic engineering) and for solving the inverse problem of metabolic pathways, i.e. the estimation of parameters from measured variables. RESULTS: We discuss the suitability of various optimization methods, focusing especially on their ability or otherwise to find global optima. We recommend that a suite of diverse optimization methods should be available in simulation software as no single one performs best for all problems. We describe how we have implemented such a simulation-optimization strategy in the biochemical kinetics simulator Gepasi and present examples of its application. AVAILABILITY: The new version of Gepasi (3.20), incorporating the methodology described here, is available on the Internet at http://gepasi.dbs.aber.ac.uk/softw/Gepasi. html. CONTACT: prm@aber.ac.uk

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Metadata

Authors
Mendes, P., & Kell, D.
Venue
Bioinformatics
Published
1998-01-01
Volume
14
Issue
10
Pages
869-883
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W1982258483
Cited by
676
References
0
FWCI
3.26
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. https://app.cochat.ai/papers/10.1093/bioinformatics/14.10.869
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Mendes, P., & Kell, D. (1998). Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics. https://doi.org/10.1093/bioinformatics/14.10.869

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