Literature DB >> 15923052

Systems biotechnology for strain improvement.

Sang Yup Lee1, Dong-Yup Lee, Tae Yong Kim.   

Abstract

Various high-throughput experimental techniques are routinely used for generating large amounts of omics data. In parallel, in silico modelling and simulation approaches are being developed for quantitatively analyzing cellular metabolism at the systems level. Thus informative high-throughput analysis and predictive computational modelling or simulation can be combined to generate new knowledge through iterative modification of an in silico model and experimental design. On the basis of such global cellular information we can design cells that have improved metabolic properties for industrial applications. This article highlights the recent developments in these systems approaches, which we call systems biotechnology, and discusses future prospects.

Mesh:

Year:  2005        PMID: 15923052     DOI: 10.1016/j.tibtech.2005.05.003

Source DB:  PubMed          Journal:  Trends Biotechnol        ISSN: 0167-7799            Impact factor:   19.536


  65 in total

1.  Prediction of metabolic fluxes by incorporating genomic context and flux-converging pattern analyses.

Authors:  Jong Myoung Park; Tae Yong Kim; Sang Yup Lee
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-02       Impact factor: 11.205

2.  In silico identification of gene amplification targets for improvement of lycopene production.

Authors:  Hyung Seok Choi; Sang Yup Lee; Tae Yong Kim; Han Min Woo
Journal:  Appl Environ Microbiol       Date:  2010-03-26       Impact factor: 4.792

Review 3.  Systems strategies for developing industrial microbial strains.

Authors:  Sang Yup Lee; Hyun Uk Kim
Journal:  Nat Biotechnol       Date:  2015-10       Impact factor: 54.908

4.  Web-based applications for building, managing and analysing kinetic models of biological systems.

Authors:  Dong-Yup Lee; Rajib Saha; Faraaz Noor Khan Yusufi; Wonjun Park; Iftekhar A Karimi
Journal:  Brief Bioinform       Date:  2008-09-19       Impact factor: 11.622

5.  Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes.

Authors:  Jonathan M Monk; Anna Koza; Miguel A Campodonico; Daniel Machado; Jose Miguel Seoane; Bernhard O Palsson; Markus J Herrgård; Adam M Feist
Journal:  Cell Syst       Date:  2016-09-22       Impact factor: 10.304

Review 6.  Metabolic engineering for production of biorenewable fuels and chemicals: contributions of synthetic biology.

Authors:  Laura R Jarboe; Xueli Zhang; Xuan Wang; Jonathan C Moore; K T Shanmugam; Lonnie O Ingram
Journal:  J Biomed Biotechnol       Date:  2010-04-06

7.  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

8.  Open access to sequence: browsing the Pichia pastoris genome.

Authors:  Diethard Mattanovich; Nico Callewaert; Pierre Rouzé; Yao-Cheng Lin; Alexandra Graf; Andreas Redl; Petra Tiels; Brigitte Gasser; Kristof De Schutter
Journal:  Microb Cell Fact       Date:  2009-10-16       Impact factor: 5.328

9.  Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels.

Authors:  Marc Carnicer; Kristin Baumann; Isabelle Töplitz; Francesc Sánchez-Ferrando; Diethard Mattanovich; Pau Ferrer; Joan Albiol
Journal:  Microb Cell Fact       Date:  2009-12-09       Impact factor: 5.328

10.  Evaluation and characterization of bacterial metabolic dynamics with a novel profiling technique, real-time metabolotyping.

Authors:  Shinji Fukuda; Yumiko Nakanishi; Eisuke Chikayama; Hiroshi Ohno; Tsuneo Hino; Jun Kikuchi
Journal:  PLoS One       Date:  2009-03-16       Impact factor: 3.240

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