New surveyor tools for charting microbial metabolic maps

Breitling, Rainer; Vitkup, Dennis; Barrett, Michael P.

doi:10.1038/nrmicro1797

Cited by 84 publications

(83 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If S. cerevisiae, the most well-characterized eukaryote, is isolated as an example, the most recent metabolic reconstructed network has genome coverage of 13.6%, while 4,691 of the 6,608 total ORFs, 70.9%, have a verified function (Fisk et al, 2006;Hong et al, 2008). From a more general perspective, the problem of metabolic gap closing is exacerbated by the relatively large orphan metabolic activities, where 30-40% of the known metabolic activities that are classified by the Enzyme Commission have no associated genomic sequences in any organism (Breitling et al, 2008;Green and Karp, 2005;Lespinet and Labedan, 2006). There is currently significant effort under-way to extend pathway reconstructions to regions of metabolism that are poorly understood or to a large degree, have been functionally neglected (Breitling et al, 2008;Viswanathan et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

“…From a more general perspective, the problem of metabolic gap closing is exacerbated by the relatively large orphan metabolic activities, where 30-40% of the known metabolic activities that are classified by the Enzyme Commission have no associated genomic sequences in any organism (Breitling et al, 2008;Green and Karp, 2005;Lespinet and Labedan, 2006). There is currently significant effort under-way to extend pathway reconstructions to regions of metabolism that are poorly understood or to a large degree, have been functionally neglected (Breitling et al, 2008;Viswanathan et al, 2008). Industrial biotechnology has largely focused on the production of added value and commodity chemicals; however, the largest expected growth sector is in the area of specialty and fine chemicals, where industrial biotechnology offers simpler routes for complex synthetic chemistry, or the possibility of de novo chemicals that may offer similar or enhanced application (Gavrilescu and Chisti, 2005;Hirche, 2006).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Industrial systems biology

Otero

Nielsen

2009

Biotech & Bioengineering

134

View full text Add to dashboard Cite

ABSTRACT:The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Industrial systems biology

Otero

Nielsen

2009

Biotech & Bioengineering

134

View full text Add to dashboard Cite

show abstract

“…This capability is based on comparison of computed and actual experimental outcomes. Given the fact that BiGG knowledge bases are incomplete and that they contain gaps 91 , they provide a context for systematic discovery of missing information. The comparison between computation and experiments are summarized in Fig.…”

Section: Directing Discovery: Gem-driven Discovery In E Colimentioning

confidence: 99%

“…The scope of reconstructions is bound to grow, representing more and more BiGG knowledge in the structured format of a GEM 91 . Growth in scope in the near-term will on one front, involve the transcriptional and translational machinery of bacterial cells [100][101][102] .…”

Section: Closingmentioning

confidence: 99%

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

“…However, the development in the area of kinetic modeling of large metabolic networks does not follow the same pace. Although metabolomic and fluxomic data are widely available, large-scale kinetic models are very scarce in the literature (Goodacre et al, 2004;Breitling et al, 2008;Jamshidi and Palsson, 2008). The efforts in this line of research have been hindered by the lack of pertinent information of the kinetic properties of enzymes in a metabolic network.…”

Section: Introductionmentioning

confidence: 99%

Modeling of uncertainties in biochemical reactions

Mišković

Hatzimanikatis

2010

Biotech & Bioengineering

View full text Add to dashboard Cite

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme.Furthermore, we derive the conditions that must be satisfied by a reversible threestep enzymatic reaction operating far away from the equilibrium in order to respond to changes in metabolite levels according to the irreversible MichelisMenten kinetics. The efficient sampling procedure allows easy, scalable, A c c e p ted P r e p r i nt implementation of this methodology to modeling of large-scale biochemical networks.

show abstract

New surveyor tools for charting microbial metabolic maps

Abstract: Citation for published version (APA): Breitling, R., Vitkup, D., & Barrett, M. P. (2008). New surveyor tools for charting microbial metabolic maps. Nature Reviews Microbiology, 6(2), 156-161.

Cited by 84 publications

References 66 publications

Industrial systems biology

Industrial systems biology

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

Modeling of uncertainties in biochemical reactions

Contact Info

Product

Resources

About