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Issue No. 04 - July-Aug. (2013 vol. 10)
ISSN: 1545-5963
pp: 1032-1044
Claudio Angione , Comput. Lab., Univ. of Cambridge, Cambridge, UK
Giovanni Carapezza , Dept. of Math. & Comput. Sci., Univ. of Catania, Catania, Italy
Jole Costanza , Dept. of Math. & Comput. Sci., Univ. of Catania, Catania, Italy
Pietro Lio , Comput. Lab., Univ. of Cambridge, Cambridge, UK
Giuseppe Nicosia , Dept. of Math. & Comput. Sci., Univ. of Catania, Catania, Italy
ABSTRACT
In low and high eukaryotes, energy is collected or transformed in compartments, the organelles. The rich variety of size, characteristics, and density of the organelles makes it difficult to build a general picture. In this paper, we make use of the Pareto-front analysis to investigate the optimization of energy metabolism in mitochondria and chloroplasts. Using the Pareto optimality principle, we compare models of organelle metabolism on the basis of single- and multiobjective optimization, approximation techniques (the Bayesian Automatic Relevance Determination), robustness, and pathway sensitivity analysis. Finally, we report the first analysis of the metabolic model for the hydrogenosome of Trichomonas vaginalis, which is found in several protozoan parasites. Our analysis has shown the importance of the Pareto optimality for such comparison and for insights into the evolution of the metabolism from cytoplasmic to organelle bound, involving a model order reduction. We report that Pareto fronts represent an asymptotic analysis useful to describe the metabolism of an organism aimed at maximizing concurrently two or more metabolite concentrations.
INDEX TERMS
Biochemistry, Optimization, Robustness, Sensitivity analysis, Mathematical model, Analytical models, Computational modeling,robustness analysis, Mitochondrion, chloroplast, hydrogenosome, sensitivity analysis, multiobjective optimization
CITATION
Claudio Angione, Giovanni Carapezza, Jole Costanza, Pietro Lio, Giuseppe Nicosia, "Pareto Optimality in Organelle Energy Metabolism Analysis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. , pp. 1032-1044, July-Aug. 2013, doi:10.1109/TCBB.2013.95
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