Abstract
The behaviour of biological cells emerges from complex patterns of interactions between genes and their products, known as gene regulatory networks (GRN). An important aim of biology is to control the dynamics of GRNs, in order to push a cell towards or away from certain behaviours. This could potentially be done by coupling a synthetic GRN to an existing biological GRN. In this work, we use Boolean networks, a methodology for modelling and simulating GRNs, to investigate the potential for doing this. Our results demonstrate that Boolean networks can be optimised to control other Boolean networks, and that the approach scales well as the target network size increases.
Original language | English |
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Pages (from-to) | 147-156 |
Journal | Journal of Computational Science |
Volume | 26 |
Early online date | 22 Apr 2018 |
DOIs | |
Publication status | Published - May 2018 |
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Michael Adam Lones
- School of Mathematical & Computer Sciences - Associate Professor
- School of Mathematical & Computer Sciences, Computer Science - Associate Professor
Person: Academic (Research & Teaching)