Understanding control mechanisms present in biological processes is crucial for the development of potential therapeutic applications, for instance cell reprogramming or drug target identification. Experimental approaches aimed at identifying possible control targets are usually costly and time-consuming. Mathematical modeling provides a formal framework to study biological systems and to predict potential successful candidate interventions. A common modeling framework is Boolean modeling, which stands out for its ability to capture the qualitative behavior of the system using coarse representations of the interactions between the components, overcoming the usual parametrization problem.

The main goal of this thesis is the study of the control problems present in biological systems and the development of efficient and complete approaches for control strategy identification. In particular, we aim at developing methods to identify sets of minimal controls that are able to induce the desired states in biological systems modeled by Boolean networks. With the goal of making our approaches attractive for application, we establish two key factors: efficiency and diversity. We want our approaches to be able to deal with state-of-the-art networks in a reasonable amount of time while providing as many different optimal control sets as possible. With these factors in mind, we developed two different approaches to meet these needs.