Dataset Information

NIHBA: a network interdiction approach for metabolic engineering design.

ABSTRACT: MOTIVATION:Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run. RESULTS:Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solutions that meet users' production requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ?1?h, e.g. studied in this article). AVAILABILITY AND IMPLEMENTATION:Source code implemented in the MATALAB Cobratoolbox is freely available at https://github.com/chang88ye/NIHBA. CONTACT:math4neu@gmail.com or natalio.krasnogor@ncl.ac.uk. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Jiang S

PROVIDER: S-EPMC7267835 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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NIHBA: a network interdiction approach for metabolic engineering design.

Jiang Shouyong S Wang Yong Y Kaiser Marcus M Krasnogor Natalio N

Bioinformatics (Oxford, England) 20200601 11

<h4>Motivation</h4>Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run.<h4>Results</h ...[more]

PMID: 32167529

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Project description:Background:Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. Results:In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Conclusions:The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

Dataset Information

NIHBA: a network interdiction approach for metabolic engineering design.

Publications

NIHBA: a network interdiction approach for metabolic engineering design.

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