Conference Paper

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

DOI: 10.1007/978-3-540-85861-4_24 Conference: 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics, IWPACBB 2008, Salamanca, Spain, 22th-24th October 2008
Source: DBLP

ABSTRACT 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.

1 Follower
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Constructing biological circuits in a bottom-up modular fashion using design methodologies similar to those used in electronics has gained tremendous attention in the past decade. The end goal, however, is engineering biological systems and not only individual components in the context of pursuing applications useful in improving human health or enhancing the environment. This article reviews the basics of biological system design rooted in Metabolic Engineering and Systems Biology and outlines current system-level modeling, analysis, optimization, and synthesis with emphasis on some current bottlenecks in establishing more rigorous design tools and methodologies for engineering biological systems.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Constraint-based metabolic models and flux balance analysis (FBA) have been extensively used in the last years to investigate the behavior of cells and also as basis for different industrial applications. In this context, this work provides a validation of a small-sized FBA model of the yeast Pichia pastoris. Our main objective is testing how accurate is the hypothesis of maximum growth to predict the behavior of P. pastoris in a range of experimental environments.ResultsA constraint-based model of P. pastoris was previously validated using metabolic flux analysis (MFA). In this paper we have verified the model ability to predict the cells behavior in different conditions without introducing measurements, experimental parameters, or any additional constraint, just by assuming that cells will make the best use of the available resources to maximize its growth. In particular, we have tested FBA model ability to: (a) predict growth yields over single substrates (glucose, glycerol, and methanol); (b) predict growth rate, substrate uptakes, respiration rates, and by-product formation in scenarios where different substrates are available (glucose, glycerol, methanol, or mixes of methanol and glycerol); (c) predict the different behaviors of P. pastoris cultures in aerobic and hypoxic conditions for each single substrate. In every case, experimental data from literature are used as validation.Conclusions We conclude that our predictions based on growth maximisation are reasonably accurate, but still far from perfect. The deviations are significant in scenarios where P. pastoris grows on methanol, suggesting that the hypothesis of maximum growth could be not dominating in these situations. However, predictions are much better when glycerol or glucose are used as substrates. In these scenarios, even if our FBA model is small and imposes a strong assumption regarding how cells will regulate their metabolic fluxes, it provides reasonably good predictions in terms of growth, substrate preference, product formation, and respiration rates.
    BMC Systems Biology 12/2014; 8(1):4. DOI:10.1186/s12918-014-0142-y · 2.85 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: MOTIVATION: Metabolic engineering algorithms provide means to optimise a biological process leading to the improvement of a biotechnological interesting molecule. Therefore, it is important to understand how to act in a metabolic pathway in order to have the best results in terms of productions. In this work, we present a computational framework that searches for optimal and robust microbial strains that are able to produce target molecules. Our framework performs three tasks: it evaluates the parameter sensitivity of the microbial model, searches for the optimal genetic or fluxes design, and finally calculates the robustness of the microbial strains. We are capable to combine the exploration of species, reactions, pathways and knockout parameter spaces with the Pareto optimality principle. RESULTS: Our framework provides also theoretical and practical guidelines for design automation. The statistical cross comparison of our new optimisation procedures, carried out with respect to currently widely used algorithms for bacteria (e.g. Escherichia coli) over different multiple functions, reveals good performances over a variety of biotechnological products. CONTACT:, and SUPPLEMENTARY INFORMATION:
    Bioinformatics 10/2012; 28(23). DOI:10.1093/bioinformatics/bts590 · 4.62 Impact Factor

Full-text (2 Sources)

Available from
May 21, 2014