Genome-Scale Thermodynamic Analysis of Escherichia coli Metabolism

Henry, Christopher S.; Jankowski, Madeline; Broadbelt, Linda J.; Hatzimanikatis, Vassily

doi:10.1529/biophysj.105.071720

Cited by 204 publications

(214 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%

“…Furthermore, these efforts have examined E. coli physiology for a vast number of given genetic and environmental conditions and incorporation of the developed methods will have an impact on future design of biological systems and modeling approaches. A large subset of these studies of phenotypic behavior aim to utilize thermodynamic laws and information to refine phenotype predictions of GEMs and to incorporate metabolomic and fluxomic data into modeling 19,40,47,49,52,54,55,57,61 .…”

Section: Phenotypic Functions: Gem Aided Assessmentmentioning

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%

“…Predictive capability is expected to improve through examining growth behavior across a greater number of environments (additional phenotyping screens will be necessary) and with an increase of integration of additional cellular processes. Genetic perturbations have played a key role in the study of the genotypephenotype relationship in biology and GEMs can be used to mechanistically interpret the results and predict the outcomes of such perturbations.Incorporating thermodynamic information into E. coli GEMs has shown promise in narrowing predictions of allowable physiological states in a given environment 19,40,47,49,52,54,55,57,61 and in identifying reactions likely to be subject to active allosteric or genetic regulation 49,54 . This field is progressing rapidly and should prove to increase the predictive capabilities of genome-scale modeling through the addition of governing thermodynamic physiochemical constrains.…”

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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: Phenotypic Functions: Gem Aided Assessmentmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

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

2008

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Biofuels: Microbially Generated Methane and Hydrogen

McAnulty

Vepachedu

Wood

et al. 2013

Encyclopedia of Life Sciences

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The production of methane (CH 4 ) or hydrogen (H 2 ) from renewable biomass by microorganisms growing anaerobically has the potential for contribution to independence from fossil fuels. Anaerobes function in Nature by converting biomass to CH 4 through food chains comprised of fermentative and acetogenic species, which decompose the complex biomass to H 2 , formate and acetate that are further metabolised to CH 4 by methanogens. Methanogens reduce the concentration of products to levels that permit the initial decomposition of biomass by fermentative and acetogenic species. Current H 2 production relies extensively on energy-intensive fossil fuel sources. Photosynthetic and fermentative species offer more efficient routes for H 2 production. Although fermentatives have significantly higher production rates, they have lower yields of H 2 but may be a source of other valuable compounds that are synthesised along with H 2 . Further research must be conducted on obtaining H 2 from reductive pools of NAD(P)H to increase yields and increase economic competitiveness.

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Tracer‐Based Analysis of Metabolic Flux Networks

Dauner¹

2012

Engineering Complex Phenotypes in Industrial Strains

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Genome-Scale Thermodynamic Analysis of Escherichia coli Metabolism

Cited by 204 publications

References 35 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

Biofuels: Microbially Generated Methane and Hydrogen

Tracer‐Based Analysis of Metabolic Flux Networks

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