Bayesian integrative modelling of metabolic and transcriptional data using pathway information
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School of Science |
Master's thesis
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Date
2010
Major/Subject
Informaatiotekniikka
Mcode
T-61
Degree programme
Language
en
Pages
vi + 54
Series
Abstract
One of the rising trends in computational systems biology is to characterize biological systems through integrated analysis of different sources of biological information. While gene expression and metabolic measurements are among the most prevalent biological information sources their integration is problematic due to the difficulties raised by the high dimensionality of the datasets, excessive noise and lack of data samples. The biochemical skeleton of metabolism has been extensively studied an widely used for simulating the metabolic behaviours in cells. Nevertheless, the rigid structure of metabolism can also be utilized as a scaffold for analysis of the genome-scale datasets. This helps to manage the high dimensionality and noise more effectively while also providing a natural link for integrating several omics datasets in the mean time. The ultimate goal of the work presented in this thesis is to develop a novel data fusion scheme by taking advantage of the genome-scale reconstructed models of metabolism as prior knowledge for integrated analysis of transcriptional and metabolic measurements.Description
Supervisor
Kaski, SamuelThesis advisor
Salojärvi, JarkkoKeywords
integrative modelling, gene expression, metabolic measurement, stoichiometry, metabolic network modelling, bayesian analysis