Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.560748
Title: Stochastic modeling and inference of large-scale gene regulatory networks
Author: Kim, Haseong
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2012
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Abstract:
Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. This thesis mainly focuses on developing methodologies for large-scale GRN analyses. It includes applications of a stochastic process theory called G-networks and a reverse engineering technique for large-scale GRNs. Additionally a series of bioinformatics techniques was applied in brain tumor data to detect disease candidate genes along with their large-scale GRNs. The proposed techniques such as stochastic modeling (bottom-up) and reverse engineering (top-down) could provide a systematic view of a complex system and an efficient guideline to identify candidate genes or pathways triggering a specific phenotype of a cell. As further work, the combinatorial use of the modeling and reverse engineering approaches would be helpful in obtaining a reliable mathematical model and even in developing a synthetic biological system.
Supervisor: Gelenbe, Erol Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.560748  DOI: Not available
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