Robustness in Boolean Models of Genetic Regulatory Systems
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چکیده
This thesis focuses on characterising and understanding robustness in Boolean models of genetic regulatory systems, both in terms of abstract models — specifically the Random Boolean Network model — and in terms of models of real-world regulatory systems. More specifically, the characterisation of robustness to state perturbation is considered in terms of features of a system’s dynamic structure (state space) such as attractors and attractor basins. This perspective on robustness seeks to quantify the long-term behavioural effects of perturbation in models of genetic regulatory systems. Robustness is studied within the conceptual framework of a system’s state space and the structures of that state space. Schemas — a way of representing multiple system states in a compact fashion — are used to characterise the structure of state spaces and the complexity of the decisions made by a system’s state space. The first study introduces a formal definition of robustness to perturbation, termed coherency, in terms of switching between basins of attraction in a discrete dynamic system. The formal definition allows bounds (upper, random and lower) to be established on the expected coherency of basins of attraction with respect to attractor basin size. The structure of system state space determines these bounds, with the upper bound defined by a highly structured state space, and the random bound defined by an unstructured space. Experiments measuring coherency with respect to basin size in the Random Boolean Network model show that network connectivity has the effect of moving the coherency of attractor basins between the upper bound (at low-connectivity) and the random bound (as the network becomes completely connected). The second study extends the investigation of state-space structures and robustness. As is common in genetic regulatory systems modelling, the assumption is
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تاریخ انتشار 2006