Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism
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Identification of sensitive enzymes in the photosynthetic carbon metabolism.pdf
Description
Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism
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409.35 KB
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Author(s)
Umeton, Renato
Stracquadanio, Giovanni
Papini, Alessio
Costanza, Jole
Lio, Pietro
Nicosia, Giuseppe
Date Issued
2012
Journal
Advances in Systems Biology
Publisher
Springer-Verlag
Citation
Umeton, Renato, Giovanni Stracquadanio, Alessio Papini, Jole Costanza, Pietro Lio, and Giuseppe Nicosia. “Identification of Sensitive Enzymes in the Photosynthetic Carbon Metabolism.” Advances in Systems Biology (November 18, 2011): 441–459.
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Author's final manuscript
Abstract
Understanding and optimizing the CO[subscript 2] fixation process would allow human beings to address better current energy and biotechnology issues. We focused on modeling the C[subscript 3] photosynthetic Carbon metabolism pathway with the aim of identifying the minimal set of enzymes whose biotechnological alteration could allow a functional re-engineering of the pathway. To achieve this result we merged in a single powerful pipe-line Sensitivity Analysis (SA), Single- (SO) and Multi-Objective Optimization (MO), and Robustness Analysis (RA). By using our recently developed multipurpose optimization algorithms (PAO and PMO2) here we extend our work exploring a large combinatorial solution space and most importantly, here we present an important reduction of the problem search space. From the initial number of 23 enzymes we have identified 11 enzymes whose targeting in the C[subscript 3] photosynthetic Carbon metabolism would provide about 90% of the overall functional optimization. Both in terms of maximal CO[subscript 2] Uptake and minimal Nitrogen consumption, these 11 sensitive enzymes are confirmed to play a key role. Finally we present a RA to confirm our findings.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1007/978-1-4419-7210-1_26
