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      COPASI--a COmplex PAthway SImulator

      Author(s): , , , , , , , ,   ,
      Publication date Created:
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press (OUP)

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          Abstract

          Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.

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

          P Mendes, D Kell (1998)
          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. 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. 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. prm@aber.ac.uk
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            Basic Linear Algebra Subprograms for Fortran Usage

            C Lawson, R. Hanson, D. Kincaid … (1979)
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              Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering.

              S. Schuster (1999)
              Rational metabolic engineering requires powerful theoretical methods such as pathway analysis, in which the topology of metabolic networks is considered. All metabolic capabilities in steady states are composed of elementary flux modes, which are minimal sets of enzymes that can each generate valid steady states. The modes of the fructose-2,6-bisphosphate cycle, the combined tricarboxylic-acid-glyoxylate-shunt system and tryptophan synthesis are used here for illustration. This approach can be used for many biotechnological applications such as increasing the yield of a product, channelling a product into desired pathways and in functional reconstruction from genomic data.
              Article 0 comments Cited 136 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Bioinformatics
                Abbreviated Title: Bioinformatics
                Publisher: Oxford University Press (OUP)
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1460-2059
                Publication date Created: December 01 2006
                Publication date Created: December 15 2006
                Publication date (Electronic): October 10 2006
                Publication date (Print): December 15 2006
                Volume: 22
                Issue: 24
                Pages: 3067-3074
                Article
                DOI: 10.1093/bioinformatics/btl485
                PubMed ID: 17032683
                SO-VID: 0f6d1e47-bc0e-40ec-8868-1a8f0e92ed37
                Copyright © © 2006
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