Metabolic networks of microbial systems

Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorgani...
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Autor*in:	Bhattacharya, Sumana [verfasserIn] Chakrabarti, Subhra Nayak, Amiya Bhattacharya, Sanjoy K

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2003

Schlagwörter:	Metabolic Network Cluster Coefficient Operation Variable Tandem Affinity Purification Average Cluster Coefficient

Anmerkung:	© Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

Übergeordnetes Werk:	Enthalten in: Microbial cell factories - London : Biomed Central, 2002, 2(2003), 1 vom: 11. Apr.
Übergeordnetes Werk:	volume:2 ; year:2003 ; number:1 ; day:11 ; month:04

Links:	Volltext

DOI / URN:	10.1186/1475-2859-2-3

Katalog-ID:	SPR028553330

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520			\|a Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors.
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allfields	10.1186/1475-2859-2-3 doi (DE-627)SPR028553330 (SPR)1475-2859-2-3-e DE-627 ger DE-627 rakwb eng Bhattacharya, Sumana verfasserin aut Metabolic networks of microbial systems 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. Metabolic Network (dpeaa)DE-He213 Cluster Coefficient (dpeaa)DE-He213 Operation Variable (dpeaa)DE-He213 Tandem Affinity Purification (dpeaa)DE-He213 Average Cluster Coefficient (dpeaa)DE-He213 Chakrabarti, Subhra aut Nayak, Amiya aut Bhattacharya, Sanjoy K aut Enthalten in Microbial cell factories London : Biomed Central, 2002 2(2003), 1 vom: 11. Apr. (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:2 year:2003 number:1 day:11 month:04 https://dx.doi.org/10.1186/1475-2859-2-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2003 1 11 04
spelling	10.1186/1475-2859-2-3 doi (DE-627)SPR028553330 (SPR)1475-2859-2-3-e DE-627 ger DE-627 rakwb eng Bhattacharya, Sumana verfasserin aut Metabolic networks of microbial systems 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. Metabolic Network (dpeaa)DE-He213 Cluster Coefficient (dpeaa)DE-He213 Operation Variable (dpeaa)DE-He213 Tandem Affinity Purification (dpeaa)DE-He213 Average Cluster Coefficient (dpeaa)DE-He213 Chakrabarti, Subhra aut Nayak, Amiya aut Bhattacharya, Sanjoy K aut Enthalten in Microbial cell factories London : Biomed Central, 2002 2(2003), 1 vom: 11. Apr. (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:2 year:2003 number:1 day:11 month:04 https://dx.doi.org/10.1186/1475-2859-2-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2003 1 11 04
allfields_unstemmed	10.1186/1475-2859-2-3 doi (DE-627)SPR028553330 (SPR)1475-2859-2-3-e DE-627 ger DE-627 rakwb eng Bhattacharya, Sumana verfasserin aut Metabolic networks of microbial systems 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. Metabolic Network (dpeaa)DE-He213 Cluster Coefficient (dpeaa)DE-He213 Operation Variable (dpeaa)DE-He213 Tandem Affinity Purification (dpeaa)DE-He213 Average Cluster Coefficient (dpeaa)DE-He213 Chakrabarti, Subhra aut Nayak, Amiya aut Bhattacharya, Sanjoy K aut Enthalten in Microbial cell factories London : Biomed Central, 2002 2(2003), 1 vom: 11. Apr. (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:2 year:2003 number:1 day:11 month:04 https://dx.doi.org/10.1186/1475-2859-2-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2003 1 11 04
allfieldsGer	10.1186/1475-2859-2-3 doi (DE-627)SPR028553330 (SPR)1475-2859-2-3-e DE-627 ger DE-627 rakwb eng Bhattacharya, Sumana verfasserin aut Metabolic networks of microbial systems 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. Metabolic Network (dpeaa)DE-He213 Cluster Coefficient (dpeaa)DE-He213 Operation Variable (dpeaa)DE-He213 Tandem Affinity Purification (dpeaa)DE-He213 Average Cluster Coefficient (dpeaa)DE-He213 Chakrabarti, Subhra aut Nayak, Amiya aut Bhattacharya, Sanjoy K aut Enthalten in Microbial cell factories London : Biomed Central, 2002 2(2003), 1 vom: 11. Apr. (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:2 year:2003 number:1 day:11 month:04 https://dx.doi.org/10.1186/1475-2859-2-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2003 1 11 04
allfieldsSound	10.1186/1475-2859-2-3 doi (DE-627)SPR028553330 (SPR)1475-2859-2-3-e DE-627 ger DE-627 rakwb eng Bhattacharya, Sumana verfasserin aut Metabolic networks of microbial systems 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL. Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. Metabolic Network (dpeaa)DE-He213 Cluster Coefficient (dpeaa)DE-He213 Operation Variable (dpeaa)DE-He213 Tandem Affinity Purification (dpeaa)DE-He213 Average Cluster Coefficient (dpeaa)DE-He213 Chakrabarti, Subhra aut Nayak, Amiya aut Bhattacharya, Sanjoy K aut Enthalten in Microbial cell factories London : Biomed Central, 2002 2(2003), 1 vom: 11. Apr. (DE-627)355987651 (DE-600)2091377-1 1475-2859 nnns volume:2 year:2003 number:1 day:11 month:04 https://dx.doi.org/10.1186/1475-2859-2-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2003 1 11 04
language	English
source	Enthalten in Microbial cell factories 2(2003), 1 vom: 11. Apr. volume:2 year:2003 number:1 day:11 month:04
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topic_facet	Metabolic Network Cluster Coefficient Operation Variable Tandem Affinity Purification Average Cluster Coefficient
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authorswithroles_txt_mv	Bhattacharya, Sumana @@aut@@ Chakrabarti, Subhra @@aut@@ Nayak, Amiya @@aut@@ Bhattacharya, Sanjoy K @@aut@@
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abstract	Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
abstractGer	Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
abstract_unstemmed	Abstract In contrast to bioreactors the metabolites within the microbial cells are converted in an impure atmosphere, yet the productivity seems to be well regulated and not affected by changes in operation variables. These features are attributed to integral metabolic network within the microorganism. With the advent of neo-integrative proteomic approaches the understanding of integration of metabolic and protein-protein interaction networks have began. In this article we review the methods employed to determine the protein-protein interaction and their integration to define metabolite networks. We further present a review of current understanding of network properties, and benefit of studying the networks. The predictions using network structure, for example, in silico experiments help illustrate the importance of studying the network properties. The cells are regarded as complex system but their elements unlike complex systems interact selectively and nonlinearly to produce coherent rather than complex behaviors. © Bhattacharya et al; licensee BioMed Central Ltd. 2003. This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
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Metabolic networks of microbial systems

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