The thesis will target the topic of automatised inference of Boolean models of biological systems from experimental data achieved in steady-state. The theoretical goal of the thesis is to study a selected recently published algorithm for optimisation-based inference of Boolean Networks from steady-state data (Trinh et al.) and to identify its performance limits. In the practical part, the student will design and implement improvements of the algorithm and evaluate the implementation on the created benchmarks.

• to describe a selected algorithm for inference of BNs,
• to make a set of own benchmarks (synthetically generated data),
• to systematically identify performance issues in the analysed algorithm and its implementation,
• to propose extensions that will improve performance of the existing implementation of the algorithm.

The concrete practical goals are two fold. First, the student will automatise the generation of new benchmarks. Second, the student will implement and evaluate the proposed improvements of the inference algorithm and compare them against the original implementation.