Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks.
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.1007/978-3-030-32409-4_22 |
| Doi | http://dx.doi.org/10.1007/978-3-030-32409-4_22 |
| Keywords | Attractor analysis; Machine learning; Boolean networks |
| Description | Boolean networks offer an elegant way to model the behaviour of complex systems with positive and negative feedback. The long-term behaviour of a Boolean network is characterised by its attractors. Depending on various logical parameters, a Boolean network can exhibit vastly different types of behaviour. Hence, the structure and quality of attractors can undergo a significant change known in systems theory as attractor bifurcation. In this paper, we establish formally the notion of attractor bifurcation for Boolean networks. We propose a semi-symbolic approach to attractor bifurcation analysis based on a parallel algorithm. We use machine-learning techniques to construct a compact, human-readable, representation of the bifurcation analysis results. We demonstrate the method on a set of highly parametrised Boolean networks. |
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