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Systems biology informatics for the development and use of genome-scale metabolic models

Swainston, Neil

[Thesis]. Manchester, UK: The University of Manchester; 2012.

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Abstract

Systems biology attempts to understand biological systems through the generation of predictive models that allow the behaviour of the system to be simulated in silico.Metabolic systems biology has in recent years focused upon the reconstruction and constraint-based analysis of genome-scale metabolic networks, which provide computational and mathematical representations of the known metabolic capabilities of a given organism. This thesis initially concerns itself with the development of such metabolic networks, first considering the community-driven development of consensus networks of the metabolic functions of Saccharomyces cerevisiae. This is followed by a consideration of automated approaches to network reconstruction that can be applied to facilitate what has, until recently, been an arduous manual process.The use of such large-scale networks in the generation of dynamic kinetic models is then considered. The development of such models is dependent upon the availability of experimentally determined parameters, from omics approaches such as transcriptomics, proteomics and metabolomics, and from kinetic assays. A discussion of the challenges faced with developing informatics infrastructure to support the acquisition, analysis and dissemination of quantitative proteomics and enzyme kinetics data follows, along with the introduction of novel software approaches to address these issues.The requirement for integrating experimental data with kinetic models is considered, along with approaches to construct, parameterise and simulate kinetic models from the network reconstructions and experimental data discussed previously.Finally, future requirements for metabolic systems biology informatics are considered, in the context of experimental data management, modelling infrastructure, and data integration required to bridge the gap between experimental and modelling approaches.

Bibliographic metadata

Type of resource:
Content type:
Form of thesis:
Type of submission:
Degree type:
Doctor of Philosophy
Degree programme:
PhD by Published Work
Publication date:
Location:
Manchester, UK
Total pages:
175
Abstract:
Systems biology attempts to understand biological systems through the generation of predictive models that allow the behaviour of the system to be simulated in silico.Metabolic systems biology has in recent years focused upon the reconstruction and constraint-based analysis of genome-scale metabolic networks, which provide computational and mathematical representations of the known metabolic capabilities of a given organism. This thesis initially concerns itself with the development of such metabolic networks, first considering the community-driven development of consensus networks of the metabolic functions of Saccharomyces cerevisiae. This is followed by a consideration of automated approaches to network reconstruction that can be applied to facilitate what has, until recently, been an arduous manual process.The use of such large-scale networks in the generation of dynamic kinetic models is then considered. The development of such models is dependent upon the availability of experimentally determined parameters, from omics approaches such as transcriptomics, proteomics and metabolomics, and from kinetic assays. A discussion of the challenges faced with developing informatics infrastructure to support the acquisition, analysis and dissemination of quantitative proteomics and enzyme kinetics data follows, along with the introduction of novel software approaches to address these issues.The requirement for integrating experimental data with kinetic models is considered, along with approaches to construct, parameterise and simulate kinetic models from the network reconstructions and experimental data discussed previously.Finally, future requirements for metabolic systems biology informatics are considered, in the context of experimental data management, modelling infrastructure, and data integration required to bridge the gap between experimental and modelling approaches.
Thesis main supervisor(s):
Language:
en

Record metadata

Manchester eScholar ID:
uk-ac-man-scw:157795
Created by:
Swainston, Neil
Created:
22nd March, 2012, 10:02:20
Last modified by:
Swainston, Neil
Last modified:
1st June, 2015, 18:53:59