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Title: Mathematical modelling of eukaryotic stress-response gene networks
Author: Haque, Mainul
ISNI:       0000 0004 2726 0785
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2012
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Mathematical modelling of gene regulatory networks is a relatively new area which is playing an important role in theoretical and experimental investigations that seek to open the door to understanding the real mechanisms that take place in living systems. The current thesis concentrates on studying the animal stress-response gene regulatory network by seeking to predict the consequence of environmental hazards caused by chemical mixtures (typical of industrial pollution). Organisms exposed to pollutants display multiple defensive stress responses, which together constitute an interlinked gene network (the Stress-Response Network; SRN). Multiple SRN reporter-gene outputs have been monitored during single and combined chemical exposures in transgenic strains of two invertebrates, Caenorhabditis elegans and Drosophila melanogaster. Reporter expression data from both species have been integrated into mathematical models describing the dynamic behaviour of the SRN and incorporating its known regulatory gene circuits. We describe some mathematical models of several types of different stress response networks, incorporating various methods of activation and inhibition, including formation of complexes and gene regulation (through several known transcription factors). Although the full details of the protein interactions forming these types of circuits are not yet well-known, we seek to include the relevant proteins acting in different cellular compartments. We propose and analyse a number of different models that describe four different stress response gene networks and through a combination of analytical (including stability, bifurcation and asymptotic) and numerical methods, we study these models to gain insight on the effect of several stresses on gene networks. A detailed time-dependent asymptotic analysis is performed for relevant models in order to clarify the roles of the distinct biochemical reactions that make up several important proteins production processes. In two models we were able to verify the theoretical predictions with the corresponding laboratory experimental observations that carried out by my coworkers in Britain and India.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA276 Mathematical statistics