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dc.contributor.advisorLones, Doctor Michael
dc.contributor.advisorCorne, Professor David
dc.contributor.authorTaou, Nadia Solime
dc.date.accessioned2019-05-17T16:28:26Z
dc.date.available2019-05-17T16:28:26Z
dc.date.issued2018-07
dc.identifier.urihttp://hdl.handle.net/10399/4005
dc.description.abstractBiological cell behaviours emerge from complex patterns of interactions between genes and their products, known as gene regulatory networks (GRNs). More specifically, GRNs are complex dynamical structures that orchestrate the activities of biological cells by governing the expression of mRNA and proteins. Many computational models of these networks have been shown to be able to carry out complex computation in an efficient and robust manner, particularly in the domains of control and signal processing. GRNs play a central role within living organisms and efficient strategies for controlling their dynamics need to be developed. For instance, the ability to push a cell towards or away from certain behaviours, is an important aim in fields such as medicine and synthetic biology. This could, for example, help to find novel approaches in the design of therapeutic drugs. However, current approaches to controlling these networks exhibit poor scalability and limited generality. This thesis proposes a new approach and an alternative method for performing state space targeting in GRNs, by coupling an artificial GRN to an existing GRN. This idea is tested in simulation by coupling together Boolean networks that represent controlled and controller systems. Evolutionary algorithms are used to evolve the controller Boolean networks. Controller Boolean networks are applied to a range of controlled Boolean networks including Boolean models of actual biological circuits, each with different dynamics. The results show that controller Boolean networks can be optimised to control trajectories in the target networks. Also, the approach scales well as the target network size increases. The use of Boolean modelling is potentially advantageous from an implementation perspective, since synthetic biology techniques can be used to refine an optimised controller Boolean network into an in vivo form, which could then control a genetic network directly from within a cell.en
dc.language.isoenen
dc.publisherMathematical and Computer Sciencesen
dc.titleTowards the control of cell states in gene regulatory networks by evolving Boolean networksen
dc.typeThesisen


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