Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

Nagrath, Deepak; Avila-Elchiver, Marco; Berthiaume, François; Tilles, Arno W.; Messac, Achille; Yarmush, Martin L.

doi:10.1007/s10439-007-9283-0

Cited by 66 publications

(56 citation statements)

References 25 publications

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“…It is necessary, hence, the usage of proper heuristics and algorithms to optimize the objective functions while satisfying several constraints. Recently, in multi-objective optimization has been found important applications in a growing number of fields, for example, molecular biology, chemical engineering and biomedical engineering, and shown to have significant benefits compared to single-objective optimization, e.g., selection of single nucleotide polymorphisms [5], protein structure prediction [6], and estimation of intracellular fluxes [7]. In this research work, we have optimized the photosynthetic carbon metabolism in order to maximize the CO 2 uptake rate, and investigated the Pareto frontiers in the carbon metabolism in terms of photosynthetic rate versus protein-nitrogen.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Optimization of C3 Photosynthetic Carbon Metabolism

Stracquadanio

Umeton

Papini

et al. 2010

2010 IEEE International Conference on BioInformatics and BioEngineering

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Abstract-We have studied the C3 photosynthetic carbon metabolism centering our investigation on the following four design principles. (1) Optimization of the photosynthetic rate by modifying the partitioning of resources between the different enzymes of the C3 photosynthetic carbon metabolism using a constant amount of protein-nitrogen. (2) Identify sensitive and less sensitive enzymes of the studied model. (3) Maximize photosynthetic productivity rate through the choice of robust enzyme concentrations using a new precise definition of robustness. (4) Modeling photosynthetic carbon metabolism as a multiobjective problem of two competing biological selection pressures: light-saturated photosynthetic rate versus total protein-nitrogen requirement. Using the designed single-objective optimization algorithms, PAO and A-CMA-ES, we have obtained an increase in photosynthetic productivity of the 135% from 15.486 µmol m −2 s −1 to 36.382 µmol m −2 s −1 , and improving the previous best-found photosynthetic productivity value (27.261 µmol m −2 s −1 , 76% of enhancement). Optimized enzyme concentrations express a maximal local robustness (100%) and a high global robustness (97.2%), satisfactory properties for a possible "in vitro" manufacturing of the optimized pathway. Morris sensitivity analysis shows that 11 enzymes over 23 are high sensitive enzymes, i.e., the most influential enzymes of the carbon metabolism model. Finally, we have obtained the tradeoff between the maximization of the leaf CO2 uptake rate and the minimization of the total protein-nitrogen concentration. This trade-off search has been carried out for the three c i concentrations referring to the estimate of CO2 concentration in the atmosphere characteristic of 25 million years ago, nowadays and in 2100 a.C. Remarkably, the three Pareto frontiers identify the highest photosynthetic productivity rates together with the fewest protein-nitrogen usage.

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Section: Introductionmentioning

confidence: 99%

Analysis and Optimization of C3 Photosynthetic Carbon Metabolism

Stracquadanio

Umeton

Papini

et al. 2010

2010 IEEE International Conference on BioInformatics and BioEngineering

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“…49 Previous studies showed that objective functions compete against each other. 27,47,50 This means that one objective function can only be improved if another is worsened. Optimal solutions for competing objectives are called Pareto optimal.…”

Section: Response Of Saccharomyces Cerevisiae To a Glucose Pulsementioning

confidence: 99%

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

et al. 2015

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. (2015). MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Molecular BioSystems, 11(1), 137-145. DOI: 10.1039/c4mb00510d General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters.Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13 C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

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“…Identification of appropriate cellular objective functions has been subject to several in silico and in vitro experiments [21,22]. Moreover, multi-objective programming permits the simultaneous optimization for multiple objectives [23] to investigate the trade-off between several competing objectives. Alternatively, one may choose methods that allow the characterization of a representative set of the steady-state solution space, rather than a set of optimal solutions.…”

Section: Current Opinion In Biotechnologymentioning

confidence: 99%