The topic of the thesis targets the general problem of systems identification specialised to the context of discrete-time discrete-value dynamical systems represented by means of Boolean Networks (BNs) with asynchronous update. BNs represent a widely used formalism for describing emergent behaviour of complex systems, i.e., gene regulatory networks. Although inference of Boolean Network models is a well-studied topic in the case of synchronous semantics of node updates. However, not much has been done in the case of BNs with asynchronous update which are supposed to be more significant for modelling of real-world systems.

The general goal of the thesis is to contribute to the problem of identification of update functions determining the asynchronous dynamics of the Boolean Network from time-series and possibly also steady-state data provided that the basic structure of the network is known.

In the theoretical part, the student will establish the concept of systems identification from the perspective of asynchronous Boolean Networks and will identify the problems of practical identifiability of systems represented as Boolean Networks with partial knowledge of update functions. In the practical part, the student will propose an algorithmic solution that will improve the practical indetification of Boolean Models from particular types of experimental data. The proposed solution will be evaluated on several (small scale) examples.

The prototype software developed for the purpose of this thesis will primarily serve as an experimental framework that will provide fast incorporation of various inference algorithms.