Multi-Objective Optimization of Biological Networks for Prediction of Intracellular Fluxes

  • José-Oscar H. Sendín
  • Antonio A. Alonso
  • Julio R. Banga
Part of the Advances in Soft Computing book series (AINSC, volume 49)


In this contribution, we face the problem of predicting intracellular fluxes using a multi-criteria optimization approach, i.e. the simultaneous optimization of two or more cellular functions. Based on Flux Balance Analysis, we calculate the Pareto set of optimal flux distributions in E. coli for three objectives: maximization of biomass and ATP, and minimization of intracellular fluxes. These solutions are able to predict flux distributions for different environmental conditions without requiring specific constraints, and improve previous published results. We thus illustrate the usefulness of multi-objective optimization for a better understanding of complex biological networks.


Multi-objective optimization Pareto front Flux Balance Analysis 


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  1. 1.
    Bonarios, H.P.J., Hatzimanikatis, V., Meesters, K.P.H., de Gooijer, C.D., Schmid, G., Tramper, J.: Metabolic flux analysis of hybridoma cells in different culture media using mass balances. Biotechnology Bioengineering 50, 299–318 (1996)CrossRefGoogle Scholar
  2. 2.
    Das, I., Dennis, J.E.: Normal Boundary Intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optimization 8, 631–657 (1998)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Dauner, M., Sauer, U.: Stoichiometric growth model for riboflavin-producing Bacillus subtilis. Biotechnology Bioengineering 76, 132–143 (2001)CrossRefGoogle Scholar
  4. 4.
    Edwards, J.S., Ibarra, R.U., Palsson, B.O.: In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnology 19(2), 125–130 (2001)CrossRefGoogle Scholar
  5. 5.
    Forster, J., Famili, I., Fu, P., Palsson, B.O., Nielssen, J.: Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Research 13(2), 244–253 (2003)CrossRefGoogle Scholar
  6. 6.
    Handl, J., Kell, D.B., Knowles, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE-ACM Transactions on Computational Biology and Bioinformatics 4(2), 279–292 (2007)CrossRefGoogle Scholar
  7. 7.
    Nielsen, J.: Principles of optimal metabolic network operation. Molecular Systems Biology 3, 2 (2007)CrossRefGoogle Scholar
  8. 8.
    Schuetz, R., Kuepfer, L., Sauer, U.: Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology 3, 119 (2007)CrossRefGoogle Scholar
  9. 9.
    Sendín, J.O.H., Vera, J., Torres, N.V., Banga, J.R.: Model-based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies. Mathematical and Computer Modelling of Dynamical Systems 12(5), 469–487 (2006)MATHCrossRefGoogle Scholar
  10. 10.
    Sendín, J.O.H., Banga, J.R., Csendes, T.: Extensions of a multistart clustering algorithm for constrained global optimization problems. Industrial & Engineering Chemistry Research (accepted for publication, 2008)Google Scholar
  11. 11.
    van Gulik, W.M., Heijnen, J.J.: A metabolic network stoichiometry analysis of microbial growth and product formation. Biotechnology Bioengineering 48, 681–698 (1995)CrossRefGoogle Scholar
  12. 12.
    Varma, A., Palsson, B.O.: Metabolic flux balancing: Basic concepts, scientific and practical us. Bio/Technology 12(10), 994–998 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José-Oscar H. Sendín
    • 1
  • Antonio A. Alonso
    • 1
  • Julio R. Banga
    • 1
  1. 1.Process Engineering GroupInstituto de Investigaciones Marinas (CSIC)VigoSpain

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