Applications of genome‐scale metabolic reconstructions

Oberhardt, Matthew; Palsson, Bernhard Ø.; Papin, Jason A.

doi:10.1038/msb.2009.77

Cited by 784 publications

(640 citation statements)

References 166 publications

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“…In this respect, constraint-based modeling, most notably flux-balance analysis (FBA), has become a quasi-standard in the field. FBA is increasingly utilized to elucidate and characterize largescale network properties, to direct the discovery of novel or alternative pathways, to guide metabolic engineering, as well as for the conceptualization of high-throughput data (Oberhardt et al, 2009;Steuer and Junker, 2009). As one of its prime advantages, constraint-based modeling does not require knowledge of the kinetic parameters of individual metabolic reactions, making it applicable to large-scale, up to genome-scale, metabolic networks.…”

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confidence: 99%

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

et al. 2010

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and Manchester Interdisciplinary Biocentre, University of Manchester, M1 7DN Manchester, United Kingdom (R.S.) Unicellular cyanobacteria have attracted growing attention as potential host organisms for the production of valuable organic products and provide an ideal model to understand oxygenic photosynthesis and phototrophic metabolism. To obtain insight into the functional properties of phototrophic growth, we present a detailed reconstruction of the primary metabolic network of the autotrophic prokaryote Synechocystis sp. PCC 6803. The reconstruction is based on multiple data sources and extensive manual curation and significantly extends currently available repositories of cyanobacterial metabolism. A systematic functional analysis, utilizing the framework of flux-balance analysis, allows the prediction of essential metabolic pathways and reactions and allows the identification of inconsistencies in the current annotation. As a counterintuitive result, our computational model indicates that photorespiration is beneficial to achieve optimal growth rates. The reconstruction process highlights several obstacles currently encountered in the context of large-scale reconstructions of metabolic networks.Cyanobacteria are among the evolutionarily oldest organisms and are the only known prokaryotes capable of plant-like oxygenic photosynthesis. As primary producers in aquatic environments, they play an important role in global CO 2 assimilation and oxygen recycling. Recently, cyanobacteria have also attracted growing attention for economic purposes, including drug discovery and as prolific producers of natural products (Sielaff et al., 2006;Tan, 2007). In particular their ability to directly convert atmospheric CO 2 into biomass and organic compounds, driven by sunlight, offers considerable potential as a novel and renewable resource for bioenergy (Deng and Coleman, 1999;Atsumi et al., 2009;Mascarelli, 2009;Lindberg et al., 2010).Among the diverse cyanobacterial strains, Synechocystis sp. PCC 6803 is one of the most extensively studied model organisms for the analysis of photosynthetic processes. With a rich compendium of genomic, biochemical, and physiological data available, Synechocystis sp. PCC 6803, therefore, offers an ideal starting point to obtain insights into the systemic properties of phototrophic metabolism. The prerequisite for such a systemic description is a detailed reconstruction of the metabolic network of the organism: that is, a reconstruction of the comprehensive set of enzyme-catalyzed reactions required to support cellular growth and maintenance. Once a metabolic reconstruction is available, the vast array of methods developed by computational systems biology over the past decades allows us to dissect the functioning and interplay of possible metabolic routes and biochemical interconversions. In this respect, constraint-based modeling, most notably flux-balance analysis (FBA), has become a quasi-standard in the field. FBA is increasingly utilized to elucidate and characterize largescale network...

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confidence: 99%

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

et al. 2010

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“…Beyond its contribution to obtaining better mechanistic insights into the way gene expression levels are controlled by their potential toxicity, EDGE bears considerable applicative value for biotechnologists: Genome-scale metabolic modeling has already been successfully applied to devise novel pathways for rational strain design (12,(44)(45)(46)(47)(48)(49)(50)(51), and gene overexpression has been considered in this framework as a means to produce a desired chemical (52,53). EDGE complements the existing computational methods by addressing a prime concern of metabolic engineers, who seek to foresee and mitigate the deleterious effects that often accompany the introduction of a foreign metabolic pathway into a host organism, or the overexpression of one of its native genes (17,18).…”

Section: Discussionmentioning

confidence: 99%

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Wagner¹,

Zarecki²,

Reshef

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Gene suppression and overexpression are both fundamental tools in linking genotype to phenotype in model organisms. Computational methods have proven invaluable in studying and predicting the deleterious effects of gene deletions, and yet parallel computational methods for overexpression are still lacking. Here, we present Expression-Dependent Gene Effects (EDGE), an in silico method that can predict the deleterious effects resulting from overexpression of either native or foreign metabolic genes. We first test and validate EDGE's predictive power in bacteria through a combination of small-scale growth experiments that we performed and analysis of extant large-scale datasets. Second, a broad cross-species analysis, ranging from microorganisms to multiple plant and human tissues, shows that genes that EDGE predicts to be deleterious when overexpressed are indeed typically downregulated. This reflects a universal selection force keeping the expression of potentially deleterious genes in check. Third, EDGEbased analysis shows that cancer genetic reprogramming specifically suppresses genes whose overexpression impedes proliferation. The magnitude of this suppression is large enough to enable an almost perfect distinction between normal and cancerous tissues based solely on EDGE results. We expect EDGE to advance our understanding of human pathologies associated with up-regulation of particular transcripts and to facilitate the utilization of gene overexpression in metabolic engineering.systems metabolic engineering | metabolic modeling | constraint-based modeling | flux balance analysis | horizontal gene transfer

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“…One of the most significant applications of metabolic network reconstruction is metabolic engineering which involves altering the metabolism to improve a desired cellular function. The reconstructed networks help in choosing a target, most commonly enzymes, for metabolic engineering (Oberhardt et al 2009). Pichia pastoris can be used for production of therapeutic glycoproteins and thus, is a promising host for industrial production of such proteins.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches

2012

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Pichia pastoris is an efficient expression system for production of recombinant proteins. To understand its physiology for building novel applications it is important to understand and reconstruct its metabolic network. The metabolic reconstruction approach connects genotype with phenotype. Here, we have attempted to reconstruct carbohydrate metabolism pathways responsible for high biomass density and N-glycosylation pathways involved in the post translational modification of proteins of P. pastoris CBS7435. Both these metabolic pathways play a crucial role in heterologous protein production. We report novel, missing and unannotated enzymes involved in the target metabolic pathways. A strong possibility of cellulose and xylose metabolic processes in P. pastoris CBS7435 suggests its use in the area of biofuels. The reconstructed metabolic networks can be used for increased yields and improved product quality, for designing appropriate growth medium, for production of recombinant therapeutics and for making biofuels.

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Applications of genome‐scale metabolic reconstructions

Cited by 784 publications

References 166 publications

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious

Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches

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