Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA

Xu, Peng; Ranganathan, Sridhar; Fowler, Zachary L.; Maranas, Costas D.; Koffas, Mattheos

doi:10.1016/j.ymben.2011.06.008

Cited by 301 publications

(220 citation statements)

References 52 publications

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“…1b). Indeed, improved cellular concentration of malonyl-CoA by expression of E. coli or heterologous ACC has proven to be an effective approach to increase the production titre of a range of malonyl-CoA-derived compounds including flavonoids 20 , polyketides 21 and fatty acids 4,22 .…”

Section: Resultsmentioning

confidence: 99%

“…In our previous work 20 , using a constraint-based flux balance model, we identified gene targets whose overexpression and deletion led to a four-fold increase in cellular acetyl-CoA/ malonyl-CoA levels. These targets included overexpression of glycolytic pathway enzymes such as glyceraldehyde-3-phosphate dehydrogenase (gapA), phosphoglycerate kinase (pgk) and pyruvate dehydrogenase multi-enzyme complex (aceEF and lpdA) along with the expression of the ACC pathway.…”

Section: Resultsmentioning

confidence: 99%

“…In addition to malonyl-CoA, acetyl-CoA has also previously been identified as a rate-limiting step for producing a range of recombinant compounds including polyketides 21 and flavonoids 20 . Boosting acetyl-CoA flux by overexpressing parts of the glycolytic pathway significantly improved fatty acid production.…”

Section: Discussionmentioning

confidence: 99%

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Modular optimization of multi-gene pathways for fatty acids production in E. coli

et al. 2013

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Modular optimization of multi-gene pathways for fatty acids production in E. coli

et al. 2013

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“…Our approach has similarities with the OptForce method (Ranganathan et al, 2010;Xu et al, 2011), which makes network-wide predictions on whether a wild-type flux range must change in order to meet a prespecified overproduction target. Like the OptForce method, we do not use the optimization function to predict the maximal possible production of a desired product but compare the predicted metabolic flux patterns for different prespecified outcomes (i.e.…”

Section: Similarity To Approaches To Simulate Metabolic Overproductionmentioning

confidence: 99%

Predictive Modeling of Biomass Component Tradeoffs in Brassica napus Developing Oilseeds Based on in Silico Manipulation of Storage Metabolism

Schwender

Hay

2012

Plant Physiology

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Seed oil content is a key agronomical trait, while the control of carbon allocation into different seed storage compounds is still poorly understood and hard to manipulate. Using bna572, a large-scale model of cellular metabolism in developing embryos of rapeseed (Brassica napus) oilseeds, we present an in silico approach for the analysis of carbon allocation into seed storage products. Optimal metabolic flux states were obtained by flux variability analysis based on minimization of the uptakes of substrates in the natural environment of the embryo. For a typical embryo biomass composition, flux sensitivities to changes in different storage components were derived. Upper and lower flux bounds of each reaction were categorized as oil or protein responsive. Among the most oil-responsive reactions were glycolytic reactions, while reactions related to mitochondrial ATP production were most protein responsive. To assess different biomass compositions, a tradeoff between the fractions of oil and protein was simulated. Based on flux-bound discontinuities and shadow prices along the tradeoff, three main metabolic phases with distinct pathway usage were identified. Transitions between the phases can be related to changing modes of the tricarboxylic acid cycle, reorganizing the usage of organic carbon and nitrogen sources for protein synthesis and acetylcoenzyme A for cytosol-localized fatty acid elongation. The phase close to equal oil and protein fractions included an unexpected pathway bypassing a-ketoglutarate-oxidizing steps in the tricarboxylic acid cycle. The in vivo relevance of the findings is discussed based on literature on seed storage metabolism.

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“…In a study by Xu et al, the OptForce system demonstrated its utility by predicting the minimal genetic interventions which actually helped the strain to overproduce naringenin up to 474 mg L −1 which is the highest titer ever achieved in a lab-scale fermentation. [156] OptForce was also used to increase resveratrol titer up to 60% (1.6 g L −1 ), which is the highest titer obtained so far without adding expensive fatty acid synthesis inhibitors such as cerulenin. [157] Vanillin, one of the most famous flavoring additives for foods and pharmaceuticals, has also been a popular target metabolite for computational simulations.…”

Section: In Silico Genome-scale Modelling and Simulationmentioning

confidence: 99%

Metabolic Engineering of Microorganisms for the Production of Natural Compounds

Park

Yang

et al. 2017

Adv. Biosys.

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Natural products have been attracting much interest around the world for their diverse applications, especially in drug and food industries. Plants have been a major source of many different natural products. However, plants are affected by weather and environmental conditions and their successful extraction is rather limited. Chemical synthesis is inefficient due to the complexity of their chemical structures involving enantioselectivity and regioselectivity. For these reasons, an alternative means of overproducing valuable natural products using microorganisms has emerged. In recent years, various metabolic engineering strategies have been developed for the production of natural products by microorganisms. Here, the strategies taken to produce natural products are reviewed. For convenience, natural products are classified into four main categories: terpenoids, phenylpropanoids, polyketides, and alkaloids. For each product category, the strategies for establishing and rewiring the metabolic network for heterologous natural product biosynthesis, systems approaches undertaken to optimize production hosts, and the strategies for fermentation optimization are reviewed. Taken together, metabolic engineering has enabled microorganisms to serve as a prominent platform for natural compounds production. This article examines both the conventional and novel strategies of metabolic engineering, providing general strategies for complex natural compound production through the development of robust microbial‐cell factories.

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Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA

Cited by 301 publications

References 52 publications

Modular optimization of multi-gene pathways for fatty acids production in E. coli

Modular optimization of multi-gene pathways for fatty acids production in E. coli

Predictive Modeling of Biomass Component Tradeoffs in Brassica napus Developing Oilseeds Based on in Silico Manipulation of Storage Metabolism

Metabolic Engineering of Microorganisms for the Production of Natural Compounds

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