ArticleBMC bioinformatics2012
An in silico platform for the design of heterologous pathways in nonnative metabolite production.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Elucidating the impact ofFrontiers in plant science · 2023Article
- Metabolic engineering ofMetabolic engineering communications · 2019Article
- Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.PLoS computational biology · 2016Article
- FastPros: screening of reaction knockout strategies for metabolic engineering.Bioinformatics (Oxford, England) · 2014Article
- Metabolic modelling in the development of cell factories by synthetic biology.Computational and structural biotechnology journal · 2012Review
- Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundMicroorganisms are used as cell factories to produce valuable compounds in pharmaceuticals, biofuels, and other industrial processes. Incorporating heterologous metabolic pathways into well-characterized hosts is a major strategy for obtaining these target metabolites and improving productivity. However, selecting appropriate heterologous metabolic pathways for a host microorganism remains difficult owing to the complexity of metabolic networks. Hence, metabolic network design could benefit greatly from the availability of an in silico platform for heterologous pathway searching.
resultsWe developed an algorithm for finding feasible heterologous pathways by which nonnative target metabolites are produced by host microorganisms, using Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae as templates. Using this algorithm, we screened heterologous pathways for the production of all possible nonnative target metabolites contained within databases. We then assessed the feasibility of the target productions using flux balance analysis, by which we could identify target metabolites associated with maximum cellular growth rate.
conclusionsThis in silico platform, designed for targeted searching of heterologous metabolic reactions, provides essential information for cell factory improvement.
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