Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5518))

Included in the following conference series:

Abstract

In Systems Biology, there is a growing need for simulation and optimization tools for the prediction of the phenotypical behavior of microorganisms. In this paper, an open-source software platform is proposed to provide support for research in Metabolic Engineering, by implementing tools that enable the simulation and optimization of dynamic metabolic models using ordinary differential equations. Its main functionalities are related with (i) phenotype simulation of both wild type and mutant strains under given environmental conditions and (ii) strain optimization tackling tasks such as gene knockout selection or the definition of the optimal level of enzyme expression, given appropriate objective functions. The central carbon metabolism of E. coli was used as a case study, to illustrate the main features of the software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. AIBench, http://www.aibench.org/

  2. BioCham, http://contraintes.inria.fr/BIOCHAM/

  3. Bornstein, B., Keating, S.M., Jouraku, A., Hucka, M.: LibSBML: an API Library for SBML. Bioinformatics 24(6), 880–881 (2008)

    Article  Google Scholar 

  4. Chassagnole, C., Noisommit-Rizzi, N., Schmid, J.W., Mauch, K., Reuss, M.: Dynamic modeling of the central carbon metabolism of Escherichia coli. Biotechnology and Bioengineering 79(1), 53–73 (2002)

    Article  Google Scholar 

  5. Cohen, S., Hindmarsh, C.: Cvode, a stiff/nonstiff ode solver in c. Computers in Physics 10(2), 138–143 (1996)

    Article  Google Scholar 

  6. Evangelista, P., Rocha, M., Rocha, I., Ferreira, E.C.: Evolutionary approaches for strain optimization using dynamic models under a metabolic engineering perspective. In: Proc. of the EvoBio 2009 (to appear) (April 2009)

    Google Scholar 

  7. Gefflaut, T., Lemaire, M., Valentin, M., Bolte, J.: A novel efficient synthesis of dihydroxyacetone phosphate and bromoacetol phosphate for use in enzymatic aldol syntheses. The Journal of Organic Chemistry 62(17), 5920–5922 (1997)

    Article  Google Scholar 

  8. Hucka, M., Finney, A., et al.: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4), 524–531 (2003)

    Article  Google Scholar 

  9. JFreeChart, http://www.jfreechart.org/jfreechart

  10. Kitano, H.: Systems biology: a brief overview. Science 295(5560), 1662–1664 (2002)

    Article  Google Scholar 

  11. Lee, D.Y., Yun, C., Hou, B., Park, S., Lee, S.Y.: Webcell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks. Bioinformatics 22(9), 1150–1151 (2006)

    Article  Google Scholar 

  12. Rocha, M., Maia, P., Mendes, R., Ferreira, E.C., Patil, K., Nielsen, J., Rocha, I.: Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinformatics 9(499) (2008)

    Google Scholar 

  13. Nummela, J., Julstrom, B.A.: Evolving petri nets to represent metabolic pathways. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, pp. 2133–2139. ACM, New York (2005)

    Google Scholar 

  14. Patil, K., Akessin, M., Nielsen, J.: Use of genome-scale microbial models for metabolic engineering. Current Opinion Biotechnology 15(1) (2004)

    Google Scholar 

  15. Maia, P., Ferreira, E.C., Rocha, I., Rocha, M.: Evaluating evolutionary multiobjective algorithms for the in silico optimization of mutant strains. In: Proc. IEEE Intern. Conf. BioInformatics and BioEngineering (BIBE 2008), Athens (2008)

    Google Scholar 

  16. Sahle, S., Gauges, R., et al.: Simulation of biochemical networks using copasi: a complex pathway simulator. In: WSC 2006: Proceedings of the 37th conference on Winter simulation. Winter Simulation Conference, pp. 1698–1706 (2006)

    Google Scholar 

  17. Stephanopoulos, G.: Metabolic fluxes and metabolic engineering. Metabolic Engineering 1(1), 1–11 (1999)

    Article  Google Scholar 

  18. Yang, K., Ma, W., Liang, H., Ouyang, Q., Tang, C., Lai, L.: Dynamic simulations on the arachidonic acid metabolic network. PLoS Computational Biology, e55.eor+ (February 2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Evangelista, P., Rocha, I., Ferreira, E.C., Rocha, M. (2009). A Software Tool for the Simulation and Optimization of Dynamic Metabolic Models. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_162

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02481-8_162

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02480-1

  • Online ISBN: 978-3-642-02481-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics