Handle URI:
http://hdl.handle.net/10754/601414
Title:
Systems Biology for Mapping Genotype-Phenotype Relations in Yeast
Authors:
Nielsen, Jens
Abstract:
The yeast Saccharomyces cerevisiae is widely used for production of fuels, chemicals, pharmaceuticals and materials. Through metabolic engineering of this yeast a number of novel new industrial processes have been developed over the last 10 years. Besides its wide industrial use, S. cerevisiae serves as an eukaryal model organism, and many systems biology tools have therefore been developed for this organism. Among these genome-scale metabolic models have shown to be most successful as they easy integrate with omics data and at the same time have been shown to have excellent predictive power. Despite our extensive knowledge of yeast metabolism and its regulation we are still facing challenges when we want to engineer complex traits, such as improved tolerance to toxic metabolites like butanol and elevated temperatures or when we want to engineer the highly complex protein secretory pathway. In this presentation it will be demonstrated how we can combine directed evolution with systems biology analysis to identify novel targets for rational design-build-test of yeast strains that have improved phenotypic properties. In this lecture an overview of systems biology of yeast will be presented together with examples of how genome-scale metabolic modeling can be used for prediction of cellular growth at different conditions. Examples will also be given on how adaptive laboratory evolution can be used for identifying targets for improving tolerance towards butanol, increased temperature and low pH and for improving secretion of heterologous proteins.
Conference/Event name:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology
Issue Date:
25-Jan-2016
Type:
Presentation
Appears in Collections:
KAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnology, January 2016

Full metadata record

DC FieldValue Language
dc.contributor.authorNielsen, Jensen
dc.date.accessioned2016-03-16T12:53:36Zen
dc.date.available2016-03-16T12:53:36Zen
dc.date.issued2016-01-25en
dc.identifier.urihttp://hdl.handle.net/10754/601414en
dc.description.abstractThe yeast Saccharomyces cerevisiae is widely used for production of fuels, chemicals, pharmaceuticals and materials. Through metabolic engineering of this yeast a number of novel new industrial processes have been developed over the last 10 years. Besides its wide industrial use, S. cerevisiae serves as an eukaryal model organism, and many systems biology tools have therefore been developed for this organism. Among these genome-scale metabolic models have shown to be most successful as they easy integrate with omics data and at the same time have been shown to have excellent predictive power. Despite our extensive knowledge of yeast metabolism and its regulation we are still facing challenges when we want to engineer complex traits, such as improved tolerance to toxic metabolites like butanol and elevated temperatures or when we want to engineer the highly complex protein secretory pathway. In this presentation it will be demonstrated how we can combine directed evolution with systems biology analysis to identify novel targets for rational design-build-test of yeast strains that have improved phenotypic properties. In this lecture an overview of systems biology of yeast will be presented together with examples of how genome-scale metabolic modeling can be used for prediction of cellular growth at different conditions. Examples will also be given on how adaptive laboratory evolution can be used for identifying targets for improving tolerance towards butanol, increased temperature and low pH and for improving secretion of heterologous proteins.en
dc.titleSystems Biology for Mapping Genotype-Phenotype Relations in Yeasten
dc.typePresentationen
dc.conference.dateJanuary 25-27, 2016en
dc.conference.nameKAUST Research Conference on Computational and Experimental Interfaces of Big Data and Biotechnologyen
dc.conference.locationKAUST, Thuwal, Saudi Arabiaen
dc.contributor.institutionDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Swedenen
dc.contributor.institutionNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Lyngby, Denmarken
dc.contributor.institutionScience for Life Laboratory, Royal Instutute of Technology, Swedenen
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