An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems

Pharkya, Priti; Maranas, Costas D.

doi:10.1016/j.ymben.2005.08.003

Cited by 291 publications

(225 citation statements)

References 47 publications

Supporting

Mentioning

219

Contrasting

Unclassified

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

Section: Ask Not What You Can Do For a Reconstruction But What A Recmentioning

confidence: 99%

“…Through the application of computational methods that incorporate linear, mixed integer linear, and non-linear programming, it has been demonstrated that model-directed strain design can lead to increased metabolite production [20][21][22][23][24][25][26][27][28][29][30] . In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest.…”

Section: Applications Of Gems To Metabolic Engineering Of E Colimentioning

confidence: 99%

“…In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest. Thus, E. coli strains have been systematically designed through in silico analysis to overproduce target metabolites such as lycopene 23,24 , lactic acid 25 , ethanol 26 , succinic acid 27,28 , Lvaline 29 , L-threonine 30 , additional amino acids 21 , as well as diverse products from hydrogen to vanillin 22 . Select exemplary metabolic engineering applications will be described in more detail.…”

Section: Applications Of Gems To Metabolic Engineering Of E Colimentioning

confidence: 99%

“…(A) A drawing of a predicted effect from a loss of function mutation in a simple system is shown. Metabolic engineering studies have investigated in silico strain design using E. coli metabolic reconstructions to overproduce desired products [20][21][22][23][24][25][26][27][28][29][30] . (B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions 32,33,[35][36][37] .…”

Section: Supplementary Materialsmentioning

confidence: 99%

See 2 more Smart Citations

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

2008

View full text Add to dashboard Cite

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded since the first review of the use of constraint-based analysis in Nature Biotechnology some 14 years ago. In particular, the Escherichia coli metabolic network reconstruction has reached the genome-scale and has been broadly adapted. Specifically, it has been used to address a broad spectrum of basic and practical applications, falling into five main categories: 1) metabolic engineering, 2) model-directed discovery, 3) interpretations of phenotypic screens, 4) analysis of network properties, and 5) studies of evolutionary processes. With these accomplishments in hand, the field is expected to move forward and seek to further, i) broaden the scope and content of network reconstructions, ii) develop new and novel in silico analysis tools, and iii) expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.The availability of reconstructed metabolic networks for microorganisms has increased rapidly in recent years, and a growing number of research groups are reconstructing metabolic networks for organisms of interest 1 . A network reconstruction represents a highly curated set of primary biological information for a particular organism and thus can be considered a biochemically, genetically and genomically structured (BiGG) data base 1, 2 . A curated BiGG data base (de facto a knowledge base) can be converted into a mathematical format (i.e., an in silico model), and used to computationally assess phenotypic properties using a variety of computational methods 2,3 . Genome-scale reconstructions are thus, a key step in quantifying the genotype-phenotype relationship and can be used to 'bring genomes to life' 4 . The purpose of this review is to summarize and classify applications utilizing the E. coli reconstruction to answer a broad spectrum of biological questions. These studies provide both an up to date review of the applications of constraint-based analysis and a guide to similar applications for the growing number of organisms for which genome-scale reconstructions are becoming available. The Key Steps in the Formulation of Genome-scale Metabolic Network ModelsThe four key steps in the formulation and use of genome-scale models are illustrated in Fig. 1. Foundational to the process is the generation of global, or genome-scale, omics data. Omics data, along with legacy information (i.e., the 'bibliome') and small-scale detailed experiments, can be used to define the interactions amongst the biological components that are used to reconstruct organism-specific networks 1 . Network reconstruction is also an iterative, on-going process that continually integrates data in a formal fashion as it becomes available 5 . As a result, a current and well curated genome-scale network reconstruction is a common denominator for those studying systems biology of an or...

show abstract

Computational Strain Design

2016

Optimization Methods in Metabolic Networks

View full text Add to dashboard Cite

An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems

Cited by 291 publications

References 47 publications

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

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

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

Computational Strain Design

Contact Info

Product

Resources

About