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Scalable External Control of Probabilistic Boolean Networks

Budget Code: R-AGR-0744-11
Funding: University of Luxembourg
Start Date: July 1, 2016
End Date: June 30, 2019

Description

Computational modelling plays a prominent role in systems biology. Modelling of certain parts of cellular machinery such as gene regulatory networks (GRNs) often leads to models characterised by huge state spaces. Therefore, profound understanding of biological processes asks for the development of scalable methods that would provide means for analysis and reasoning about such huge systems. In this project, we concentrate on external control of GRNs, modelled as probabilistic Boolean networks. Instead of deriving optimal control strategies, our methods aim for approximate, suboptimal solutions, which are computationally efficient. Our proposed methods will be valuable in practice, e.g, in cellular reprogramming.

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