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By  and Hong Noh and Hong Noh

Abstract

Cells we see today are the consequence of a selection process that has taken place over some billions of years for optimal survival and proliferation in the appropriate environments, which will have changed drastically during this time period. At shorter timescales, a cell is able to adapt to fluctuations in the environment by means of regulating gene expression. By modelling gene regulatory interactions in a graph-theoretic framework and using a constraints-based approach to measuring cell growth or by using dynamic graphical models and suitable learning algorithms, several studies have investigated the short-term behaviour of cell evolution. However, the modelling the long-term evolution of genetic information and the resulting metabolic behaviour (or metabolic network architecture) of a cell has received less attention. Here we propose a model of coupled regulatory-metabolic network evolution and discuss the theoretical and computational aspects of the model. By representing gene regulatory relationships as Boolean functions and applying constraints-based methods of assessing the robustness of an evolutionary event in the genome, the model attempts to predict the long-term metabolic behaviour of a cell. Acknowledgements I acknowledge Professor Jotun Hein and Dr. Gail Preston for presenting the idea of coupled evolution of regulatory-metabolic networks. I thank Dr. Adam Novak, Dr. Rune Lyngsø and Dr. Bhalchandra Thatte for their company in the Oxford Centre for Gene Function. I would like to thank Dr. Steven Kelly and Dr. Craig Maclean for some fun discussions. I am indebted to Dr. Aziz Mithani for his guidance throughout the project as well as his cheerful company. I am also very grateful to Professor David Fell for his invaluable guidance and feedback on flux balance analysis, which without, would have made the report less readable than what it currently is. Last but not least, I would like to thank my parents and my brother for thei

Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.185.1478
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