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Issue No.04 - July-Aug. (2013 vol.10)
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
cellular biophysics, Bayes methods, protozoan parasites, pareto optimality, organelle energy metabolism analysis, eukaryotes, organelle size, organelle characteristics, organelle density, Pareto-front analysis, mitochondria, chloroplasts, single objective optimization, multi objective optimization, Bayesian Automatic Relevance Determination, 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. 4, pp. 1032-1044, July-Aug. 2013, doi:10.1109/TCBB.2013.95
REFERENCES
[1] S. Raha et al., "Mitochondria, Oxygen Free Radicals, Disease and Ageing," Trends in Biochemical Sciences, vol. 25, no. 10, pp. 502-508, 2000.
[2] B. Alberts et al., "Energy Conversion: Mitochondria and Chloroplasts," Molecular Biology of the Cell, Garland Science, 2002.
[3] P. Lió and N. Goldman, "Modeling Mitochondrial Protein Evolution Using Structural Information," J. Molecular Evolution, vol. 54, no. 4, pp. 519-529, 2002.
[4] T. Kuroiwa, H. Kuroiwa, A. Sakai, H. Takahashi, K. Toda, and R. Itoh, "The Division Apparatus of Plastids and Mitochondria," Int'l Rev. of Cytology, vol. 181, pp. 1-41, 1998.
[5] A. Shiflett and P.J. Johnson, "Mitochondrion-Related Organelles in Parasitic Eukaryotes," Ann. Rev. of Microbiology, vol. 64, pp. 409-429, 2010.
[6] J.I. Macrae, E. Maréchal, C. Biot, and C.Y. Botté, "The Apicoplast: A Key Target to Cure Malaria," Current Pharmaceutical Design, vol. 18, pp. 3490-3504, 2012.
[7] M.V.D. Giezen, "Hydrogenosomes and Mitosomes: Conservation and Evolution of Functions," J. Eukaryotic Microbiology, vol. 56, no. 3, pp. 221-231, 2009.
[8] W. de Souza, M. Attias, and J.C.F Rodrigues, "Particularities of Mitochondrial Structure in Parasitic Protists (Apicomplexa and Kinetoplastida)," Int'l J. Biochemistry & Cell Biology, vol. 41, no. 10, pp. 2069-2080, 2009.
[9] W.B.M. Paula, J.F. Allen, and M. Giezen, "Mitochondria, Hydrogenosomes and Mitosomes in Relation to the CoRR Hypothesis for Genome Function and Evolution," Organelle Genetics, pp. 105-119, Springer, 2012.
[10] J. Handl, D.B. Kell, and J. Knowles, "Multiobjective Optimization in Bioinformatics and Computational Biology," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 4, no. 2, pp. 279-292, Apr.-June 2007.
[11] J.O.H Sendin, O. Exler, and J.R. Banga, "Multi-Objective Mixed Integer Strategy for the Optimisation of Biological Networks," IET Systems Biology, vol. 4, no. 3, pp. 236-248, May 2010.
[12] X.-G. Zhu, E. de Sturler, and S.P. Long, "Optimizing the Distribution of Resources between Enzymes of Carbon Metabolism Can Dramatically Increase Photosynthetic Rate: A Numerical Simulation Using an Evolutionary Algorithm," Plant Physiology, vol. 145, pp. 513-526, 2007.
[13] J.N. Bazil, G.T. Buzzard, and A.E. Rundell, "Modeling Mitochondrial Bioenergetics with Integrated Volume Dynamics," PLoS Computational Biology, vol. 6, no. 1,article e1000632, 2010.
[14] G. Stracquadanio and G. Nicosia, "Computational Energy-Based Redesign of Robust Proteins," Computers & Chemical Eng., vol. 35, no. 3, pp. 464-473, 2011.
[15] C.M. Agapakis, H. Niederholtmeyer, R.R. Noche, T.D. Lieberman, S.G. Megason, J.C. Way, and P.A. Silver, "Towards a Synthetic Chloroplast," PLoS One, vol. 6, no. 4,article e18877, 2011.
[16] J.D. Orth, I. Thiele, and B.Ø. Palsson, "What Is Flux Balance Analysis?" Nature Biotechnology, vol. 28, no. 3, pp. 245-248, 2010.
[17] H. Kitano, "Towards a Theory of Biological Robustness," Molecular Systems Biology, vol. 3, no. 1, 2007.
[18] J. Gunawardena, "Models in Systems Biology: The Parameter Problem and the Meanings of Robustness," Elements of Computational Systems Biology, pp. 19-47, John Wiley & Sons, 2009.
[19] D. Gorissen, I. Couckuyt, P. Demeester, T. Dhaene, and K. Crombecq, "A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design," J. Machine Learning Research, vol. 11, pp. 2051-2055, 2010.
[20] P. Lió., "Phylogenetic and Structural Analysis of Mitochondrial Complex i Proteins," Gene, vol. 345, no. 1, pp. 55-64, 2005.
[21] M.D. Morris, "Factorial Sampling Plans for Preliminary Computational Experiments," Technometrics, vol. 33, no. 2, pp. 161-174, 1991.
[22] R. Umeton, G. Stracquadanio, A. Papini, J. Costanza, P. Liò, and G. Nicosia, "Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism," Advances in Systems Biology, vol. 736, pp. 441-459, 2012.
[23] G. Stracquadanio, R. Umeton, A. Papini, P. Liò, and G. Nicosia, "Analysis and Optimization of C3 Photosynthetic Carbon Metabolism," Proc. IEEE 10th Int'l Conf. Bioinformatics and Bioeng. (BIBE '10), pp. 44-51, 2010.
[24] M. Rosvall and C.T. Bergstrom, "An Information-Theoretic Framework for Resolving Community Structure in Complex Networks," Proc. Nat'l Academy of Sciences USA, vol. 104, no. 18, pp. 7327-7331, 2007.
[25] F. Wu, F. Yang, K.C. Vinnakota, and D.A. Beard, "Computer Modeling of Mitochondrial Tricarboxylic Acid Cycle, Oxidative Phosphorylation, Metabolite Transport, and Electrophysiology," J. Biological Chemistry, vol. 282, no. 34, pp. 24525-24537, 2007.
[26] J.O. Ramsay, G. Hooker, D. Campbell, and J. Cao, "Parameter Estimation for Differential Equations: A Generalized Smoothing Approach," J. the Royal Statistical Soc.: Series B (Statistical Methodology), vol. 69, no. 5, pp. 741-796, 2007.
[27] J.A.K. Suykens and J. Vandewalle, "Least Squares Support Vector Machine Classifiers," Neural Processing Letters, vol. 9, no. 3, pp. 293-300, 1999.
[28] T.V. Gestel, J.A.K. Suykens, B.D. Moor, and J. Vandewalle, "Automatic Relevance Determination for Least Squares Support Vector Machine Regression," Proc. Int'l Joint Conf. Neural Networks (IJCNN '01), vol. 4, pp. 2416-2421, 2001.
[29] C. Higuera, A.F. Villaverde, J.R. Banga, J. Ross, and F. Morán, "Multi-Criteria Optimization of Regulation in Metabolic Networks," PLoS one, vol. 7, no. 7,article e41122, 2012.
[30] O. Shoval, H. Sheftel, G. Shinar, Y. Hart, O. Ramote, A. Mayo, E. Dekel, K. Kavanagh, and U. Alon, "Evolutionary Trade-Offs, Pareto Optimality, and the Geometry of Phenotype Space," Science, vol. 336, pp. 1157-1160, 2012.
[31] R. Umeton, G. Stracquadanio, A. Sorathiya, A. Papini, P. Liò, and G. Nicosia, "Design of Robust Metabolic Pathways," Proc. 48th Design Automation Conf. (DAC '11), pp. 747-752, June 2011.
[32] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II," IEEE Trans. Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr. 2002.
[33] R.E. Schneider, M.T. Brown, A.M. Shiflett, S.D. Dyall, R.D. Hayes, Y. Xie, J.A. Loo, and P.J. Johnson, "The Trichomonas vaginalis Hydrogenosome Proteome is Highly Reduced Relative to Mitochondria, Yet Complex Compared with Mitosomes," Int'l J. Parasitology, vol. 41, pp. 1421-1434, 2011.
[34] M. Müller, M. Mentel, J.J. van Hellemond, K. Henze, C. Woehle, S.B. Gould, R.Y. Yu, M. van der Giezen, A.G.M. Tielens, and W.F. Martin, "Biochemistry and Evolution of Anaerobic Energy Metabolism in Eukaryotes," Microbiology and Molecular Biology Rev., vol. 76, no. 2, pp. 444-495, 2012.
[35] N. Mallo, J. Lamas, and J.M. Leiro, "Hydrogenosome Metabolism Is the Key Target for Antiparasitic Activity of Resveratrol against Trichomonas vaginalis," Antimicrobial Agents and Chemotherapy, vol. 57, pp. 2476-2484, 2013.
[36] E. Zitzler, D. Brockhoff, and L. Thiele, "The Hypervolume Indicator Revisited: On the Design of Pareto-Compliant Indicators via Weighted Integration," Proc. Fourth Int'l Conf. Evolutionary Multi-Criterion Optimization, pp. 862-876, 2007.
[37] L. Cottret, P.V. Milreu, V. Acuña, A. Marchetti-Spaccamela, L. Stougie, H. Charles, and M.F. Sagot, "Graph-Based Analysis of the Metabolic Exchanges between Two Co-Resident Intracellular Symbionts, baumannia cicadellinicola and Sulcia muelleri, with Their Insect Host, Homalodisca coagulata," PLoS Computational Biology, vol. 6, no. 9,article e1000904, 2010.
[38] E. Balsa-Canto and J.R. Banga, "Amigo, A Toolbox for Advanced Model Identification in Systems Biology Using Global Optimization," Bioinformatics, vol. 27, no. 16, pp. 2311-2313, 2011.
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