Literature DB >> 19891008

Industrial systems biology.

José Manuel Otero1, Jens Nielsen.   

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. 2009 Wiley Periodicals, Inc.

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Year:  2010        PMID: 19891008     DOI: 10.1002/bit.22592

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  29 in total

1.  In silico method for modelling metabolism and gene product expression at genome scale.

Authors:  Joshua A Lerman; Daniel R Hyduke; Haythem Latif; Vasiliy A Portnoy; Nathan E Lewis; Jeffrey D Orth; Alexandra C Schrimpe-Rutledge; Richard D Smith; Joshua N Adkins; Karsten Zengler; Bernhard O Palsson
Journal:  Nat Commun       Date:  2012-07-03       Impact factor: 14.919

2.  Production host selection for asymmetric styrene epoxidation: Escherichia coli vs. solvent-tolerant Pseudomonas.

Authors:  Daniel Kuhn; Bruno Bühler; Andreas Schmid
Journal:  J Ind Microbiol Biotechnol       Date:  2012-04-17       Impact factor: 3.346

Review 3.  Analysis of omics data with genome-scale models of metabolism.

Authors:  Daniel R Hyduke; Nathan E Lewis; Bernhard Ø Palsson
Journal:  Mol Biosyst       Date:  2012-12-18

4.  Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement.

Authors:  Bevan Ks Chung; Suresh Selvarasu; Camattari Andrea; Jimyoung Ryu; Hyeokweon Lee; Jungoh Ahn; Hongweon Lee; Dong-Yup Lee
Journal:  Microb Cell Fact       Date:  2010-07-01       Impact factor: 5.328

5.  Identification of metabolic engineering targets through analysis of optimal and sub-optimal routes.

Authors:  Zita I T A Soons; Eugénio C Ferreira; Kiran R Patil; Isabel Rocha
Journal:  PLoS One       Date:  2013-04-23       Impact factor: 3.240

6.  Expanding a dynamic flux balance model of yeast fermentation to genome-scale.

Authors:  Felipe A Vargas; Francisco Pizarro; J Ricardo Pérez-Correa; Eduardo Agosin
Journal:  BMC Syst Biol       Date:  2011-05-19

7.  Model-guided development of an evolutionarily stable yeast chassis.

Authors:  Filipa Pereira; Helder Lopes; Paulo Maia; Britta Meyer; Justyna Nocon; Paula Jouhten; Dimitrios Konstantinidis; Eleni Kafkia; Miguel Rocha; Peter Kötter; Isabel Rocha; Kiran R Patil
Journal:  Mol Syst Biol       Date:  2021-07       Impact factor: 11.429

8.  The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum.

Authors:  Rasmus Agren; Liming Liu; Saeed Shoaie; Wanwipa Vongsangnak; Intawat Nookaew; Jens Nielsen
Journal:  PLoS Comput Biol       Date:  2013-03-21       Impact factor: 4.475

9.  Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in Pichia pastoris.

Authors:  Mariana L Fazenda; Joao M L Dias; Linda M Harvey; Alison Nordon; Ruan Edrada-Ebel; David Littlejohn; Brian McNeil
Journal:  Microb Cell Fact       Date:  2013-05-21       Impact factor: 5.328

10.  The Penicillium echinulatum secretome on sugar cane bagasse.

Authors:  Daniela A Ribeiro; Júnio Cota; Thabata M Alvarez; Fernanda Brüchli; Juliano Bragato; Beatriz M P Pereira; Bianca A Pauletti; George Jackson; Maria T B Pimenta; Mario T Murakami; Marli Camassola; Roberto Ruller; Aldo J P Dillon; Jose G C Pradella; Adriana F Paes Leme; Fabio M Squina
Journal:  PLoS One       Date:  2012-12-05       Impact factor: 3.240

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