Simulating information flow in cellular scale models of signal transduction

Fearnley, Liam Guy (2014). Simulating information flow in cellular scale models of signal transduction PhD Thesis, Aust Institute for Bioengineering & Nanotechnology, The University of Queensland. doi:10.14264/uql.2014.276

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Author Fearnley, Liam Guy
Thesis Title Simulating information flow in cellular scale models of signal transduction
School, Centre or Institute Aust Institute for Bioengineering & Nanotechnology
Institution The University of Queensland
DOI 10.14264/uql.2014.276
Publication date 2014-01-01
Thesis type PhD Thesis
Supervisor Lars K. Nielsen
Mark A. Ragan
Robin Palfreyman
Total pages 144
Total colour pages 13
Total black and white pages 131
Language eng
Subjects 0806 Information Systems
0601 Biochemistry and Cell Biology
0102 Applied Mathematics
Formatted abstract
The main aim of this thesis is the design and implementation of a large-scale model of eukaryotic (specifically human) signal transduction. The signal transduction system is an important component in the regulation of homeostasis and cellular response to stimuli. Defects within this system are implicated or causal in a host of diseases. Existing models focus on relatively small subsets of the signal transduction system due to their need for reaction rate data, which are not widely available. This prevents or biases the simulation of many events of interest, and limits the utility of these models in investigating cellular behaviour.

Here we present an overview of the available modelling techniques, assessing their suitability for large-scale modelling given currently available experimental and computational methods. We then proceed to design and implement the first model framework capable of simulating large-scale signal transduction systems. We then further assess the available data, identifying a number of structural and design issues within the available databases of signal transduction that hinder modelling. Finally, we implement and generate a predictive model of approximately 32% of known human signal transduction, using the data contained in the Reactome database.

In doing so, we find that there are methods of simulating signal transduction at a cellular scale that predict phenotype with high accuracy. We show that models using these techniques are useful in exploring the possible phenotypes of cells in an experimental setting. Finally, we outline methods for future expansion of the model to capture behaviour resulting from the effects of changes to transcription and translation on the signal transduction systems.
Keyword Cellular-scale modelling
Signal transduction
Integer programming.
Predictive models
Computational biology

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Created: Thu, 21 Aug 2014, 20:29:29 EST by Liam Fearnley on behalf of Scholarly Communication and Digitisation Service