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Efficient Behavior of Photosynthetic Organelles via Pareto Optimality, Identifiability, and Sensitivity Analysis

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Download Hi-Res ImageDownload to MS-PowerPointCite This:ACS Synth. Biol. 2013, 2, 5, 274-288
    Research Article

    Efficient Behavior of Photosynthetic Organelles via Pareto Optimality, Identifiability, and Sensitivity Analysis
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    † Department of Mathematics and Computer Science, University of Catania, Italy
    ‡ University of Rome “La Sapienza”, S. Andrea Hospital, and Department of Biological Engineering, Massachussets Institute of Technology, United States
    Computer Laboratory, University of Cambridge, U.K.
    § Department of Biomedical Engineering, Johns Hopkins University, United States
    || Department of Evolutionary Biology, University of Florence, Italy
    *E-mail: [email protected]; [email protected]
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    ACS Synthetic Biology

    Cite this: ACS Synth. Biol. 2013, 2, 5, 274–288
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    https://doi.org/10.1021/sb300102k
    Published December 13, 2012
    Copyright © 2012 American Chemical Society
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    Abstract

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    In this work, we develop methodologies for analyzing and cross comparing metabolic models. We investigate three important metabolic networks to discuss the complexity of biological organization of organisms, modeling, and system properties. In particular, we analyze these metabolic networks because of their biotechnological and basic science importance: the photosynthetic carbon metabolism in a general leaf, the Rhodobacter spheroides bacterium, and the Chlamydomonas reinhardtii alga. We adopt single- and multi-objective optimization algorithms to maximize the CO2 uptake rate and the production of metabolites of industrial interest or for ecological purposes. We focus both on the level of genes (e.g., finding genetic manipulations to increase the production of one or more metabolites) and on finding concentration enzymes for improving the CO2 consumption. We find that R. spheroides is able to absorb an amount of CO2 until 57.452 mmol h–1 gDW–1, while C. reinhardtii obtains a maximum of 6.7331. We report that the Pareto front analysis proves extremely useful to compare different organisms, as well as providing the possibility to investigate them with the same framework. By using the sensitivity and robustness analysis, our framework identifies the most sensitive and fragile components of the biological systems we take into account, allowing us to compare their models. We adopt the identifiability analysis to detect functional relations among enzymes; we observe that RuBisCO, GAPDH, and FBPase belong to the same functional group, as suggested also by the sensitivity analysis.

    Copyright © 2012 American Chemical Society

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    Cited By

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    This article is cited by 9 publications.

    1. Irene Otero-Muras and Julio R. Banga . Automated Design Framework for Synthetic Biology Exploiting Pareto Optimality. ACS Synthetic Biology 2017, 6 (7) , 1180-1193. https://doi.org/10.1021/acssynbio.6b00306
    2. Alessio Greco, Salvatore Danilo Riccio, Jon Timmis, Giuseppe Nicosia. Assessing Algorithm Parameter Importance Using Global Sensitivity Analysis. 2019, 392-407. https://doi.org/10.1007/978-3-030-34029-2_26
    3. Yuan Cao, Mingquan Jiang, Fuling Xu, Shuo Liu, Fanjuan Meng. The effects of elevated CO 2 (0.5%) on chloroplasts in the tetraploid black locust ( Robinia pseudoacacia L.). Ecology and Evolution 2017, 7 (24) , 10546-10555. https://doi.org/10.1002/ece3.3545
    4. Andrea Patané, Piero Conca, Giovanni Carapezza, Andrea Santoro, Jole Costanza, Giuseppe Nicosia. Metabolic Circuit Design Automation by Multi-objective BioCAD. 2016, 30-44. https://doi.org/10.1007/978-3-319-51469-7_3
    5. Claudio Angione, Jole Costanza, Giovanni Carapezza, Pietro Lió, Giuseppe Nicosia, . Multi-Target Analysis and Design of Mitochondrial Metabolism. PLOS ONE 2015, 10 (9) , e0133825. https://doi.org/10.1371/journal.pone.0133825
    6. Andrea Patane, Andrea Santoro, Jole Costanza, Giovanni Carapezza, Giuseppe Nicosia. Pareto Optimal Design for Synthetic Biology. IEEE Transactions on Biomedical Circuits and Systems 2015, 9 (4) , 555-571. https://doi.org/10.1109/TBCAS.2015.2467214
    7. Saheed Imam, Colin M. Fitzgerald, Emily M. Cook, Timothy J. Donohue, Daniel R. Noguera. Quantifying the effects of light intensity on bioproduction and maintenance energy during photosynthetic growth of Rhodobacter sphaeroides. Photosynthesis Research 2015, 123 (2) , 167-182. https://doi.org/10.1007/s11120-014-0061-1
    8. Miguel Angel Gonzalez-Salazar, Mirko Morini, Michele Pinelli, Pier Ruggero Spina, Mauro Venturini, Matthias Finkenrath, Witold-Roger Poganietz. Methodology for estimating biomass energy potential and its application to Colombia. Applied Energy 2014, 136 , 781-796. https://doi.org/10.1016/j.apenergy.2014.07.004
    9. Maryam Darabi, Hamideh Farhadi-Nejad. Study of the 3-hydroxy-3-methylglotaryl-coenzyme A reductase (HMGR) protein in Rosaceae by bioinformatics tools. Caryologia 2013, 66 (4) , 351-359. https://doi.org/10.1080/00087114.2013.856089
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    ACS Synthetic Biology

    Cite this: ACS Synth. Biol. 2013, 2, 5, 274–288
    Click to copy citationCitation copied!
    https://doi.org/10.1021/sb300102k
    Published December 13, 2012
    Copyright © 2012 American Chemical Society
    Request reuse permissions

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