Literature DB >> 24334395

Pareto optimality in organelle energy metabolism analysis.

Claudio Angione1, Giovanni Carapezza2, Jole Costanza2, Pietro Lió1, Giuseppe Nicosia2.   

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.

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Year:  2013        PMID: 24334395     DOI: 10.1109/TCBB.2013.95

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  5 in total

1.  Bioremediation in marine ecosystems: a computational study combining ecological modeling and flux balance analysis.

Authors:  Marianna Taffi; Nicola Paoletti; Claudio Angione; Sandra Pucciarelli; Mauro Marini; Pietro Liò
Journal:  Front Genet       Date:  2014-09-12       Impact factor: 4.599

2.  Multi-Target Analysis and Design of Mitochondrial Metabolism.

Authors:  Claudio Angione; Jole Costanza; Giovanni Carapezza; Pietro Lió; Giuseppe Nicosia
Journal:  PLoS One       Date:  2015-09-16       Impact factor: 3.240

3.  Multiplex methods provide effective integration of multi-omic data in genome-scale models.

Authors:  Claudio Angione; Max Conway; Pietro Lió
Journal:  BMC Bioinformatics       Date:  2016-03-02       Impact factor: 3.169

4.  Predictive analytics of environmental adaptability in multi-omic network models.

Authors:  Claudio Angione; Pietro Lió
Journal:  Sci Rep       Date:  2015-10-20       Impact factor: 4.379

5.  Global Profiling of Lysine Acetylation in Borrelia burgdorferi B31 Reveals Its Role in Central Metabolism.

Authors:  Sébastien Bontemps-Gallo; Charlotte Gaviard; Crystal L Richards; Takfarinas Kentache; Sandra J Raffel; Kevin A Lawrence; Joseph C Schindler; Joseph Lovelace; Daniel P Dulebohn; Robert G Cluss; Julie Hardouin; Frank C Gherardini
Journal:  Front Microbiol       Date:  2018-08-31       Impact factor: 5.640

  5 in total

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