Minimal Reaction Sets for Escherichia coli Metabolism under Different Growth Requirements and Uptake Environments

Burgard, Anthony P.; Vaidyaraman, Shankar; Maranas, Costas D.

doi:10.1021/bp0100880

Cited by 144 publications

(124 citation statements)

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“…A range of computational studies have sought to understand phenotypes through determining the essential genes 19,46,51,53,63 , metabolites 44, 60 and reactions 39,47,48,58 in the E. coli metabolic network. A common benchmark for examining GEM predictive ability is to determine the agreement with growth phenotype data from knock-out collections of E. coli.…”

Section: Nih-pa Author Manuscriptmentioning

confidence: 99%

“…(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] . (C) Utilizing the reconstruction in phenotypic studies, computational analyses have examined gene 19,46,51,53,63 , metabolite 44,60 and reaction 39,47,48,58 essentiality along with considering thermodynamics 19,40,47,49,52,54,55,57,61 to make better predictions about the physiological state (i.e., the active pathways) of the cell for a given environmental condition. (D) The E. coli reconstructions have been used to analyze and interpret the intrinsic properties of biological networks.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…Whereas comparisons to flux data from wild-type and E. coli mutants reveals that the computational algorithm MOMA 41 provides better predictions for transient growth rates (early post perturbation state), the algorithm ROOM 45 (and basic FBA) was found to be more successful in predicting final steady-state growth rates and overall lethality 45 . These algorithms have been utilized, in addition to basic FBA, for genome-wide essentiality screens, as now outlined.A range of computational studies have sought to understand phenotypes through determining the essential genes 19,46,51,53,63 , metabolites 44, 60 and reactions 39,47,48,58 in the E. coli metabolic network. A common benchmark for examining GEM predictive ability is to determine the agreement with growth phenotype data from knock-out collections of E. coli.…”

mentioning

confidence: 99%

“…Such studies will be further enabled by the recent availability of a comprehensive single-gene knock-out library for E. coli 95 (for example 19,53 ). Implications for examining network essentiality in E. coli include determining network essentiality in similar organisms 39,48,53,58 , deciphering network makeup and enzyme dispensability (i.e., measures of robustness) 46,58,60 , aiding in metabolic network annotation, validation and refinement 44 , and even rescuing knock-out strains through additional gene deletions 63 , to name a few. The predictive capability of the E. coli GEM, as demonstrated by these studies, has been instrumental in the adaptation of its use.…”

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confidence: 99%

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

2008

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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...

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Section: Nih-pa Author Manuscriptmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

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confidence: 99%

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

2008

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“…In other important work, the iJR904 [45] genome-scale metabolic model of Escherichia coli was applied with mixed-integer linear optimization to predict the minimal set of metabolic reactions needed for E. coli viability in minimal and complex media [46]; the study found that 122 metabolic reactions are required for growth in complex media, with an additional 102 reactions required for growth in minimal media. This is the first work to quantify approximately how many metabolic genes must be added to the minimal organism to obtain growth on defined minimal media instead of undefined complex media (~102 additional genes).…”

Section: Model-driven Design Of a Minimal Metabolismmentioning

confidence: 99%

Building the blueprint of life

Henry

Overbeek²,

Stevens

2010

Biotechnology Journal

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With recent breakthroughs in experimental microbiology making it possible to synthesize and implant an entire genome to create a living cell, the challenge of constructing a working blueprint for the first truly minimal synthetic organism is more important than ever. Here we review the significant progress made in the design and creation of a minimal organism. We discuss how comparative genomics, gene essentiality data, naturally small genomes, and metabolic modeling are all being applied to produce a catalogue of the biological functions essential for life. We compare the minimal gene sets from three published sources with functions identified in 13 existing gene essentiality datasets. We examine how genome-scale metabolic models have been applied to design a minimal metabolism for growth in simple and complex media. Additionally, we survey the progress of efforts to construct a minimal organism, either through implementation of combinatorial deletions in Bacillus subtilis and Escherichia coli or through the synthesis and implantation of synthetic genomes.

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Modeling with Binary Variables and MILP Fundamentals

2016

Optimization Methods in Metabolic Networks

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Minimal Reaction Sets for Escherichia coli Metabolism under Different Growth Requirements and Uptake Environments

Cited by 144 publications

References 18 publications

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

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

Building the blueprint of life

Modeling with Binary Variables and MILP Fundamentals

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