This thesis explains what is cell reprogramming in Boolean networks, and what are several methods to solve this problem.
First, formal definitions of perturbations, perturbation sequences, reprogramming strategies, inevitability and existentiality of the strategies, and of reprogrammability are given, in the scope of Boolean networks. Moreover, a list of actual methods to find cell reprogramming targets is given, both in the scope of Boolean networks and outside of it.
Then, it is described how a static analysis of the networks allows for better understanding of their dynamics, and how important strongly connected components of the interaction graph are. From this network with the added information of the attractor list, an algorithm finds a list of variables to perturb, sometimes with the necessity of a precise order.
Then, how to construct a new model is explained, allowing to make perturbations sequentially, thus using the Boolean network dynamics between the perturbations. Given the high complexity of this approach, we also explain an in-between approach, where only the attractors of the network can be perturbed, thus allowing for a smaller complexity.
Lastly, a case study is done, where biological Boolean networks from literature are used, and on which the different algorithms from the thesis are applied. We show that sequential reprogramming strategies allow for new perturbation sequences, with smaller perturbations than one-step reprogramming strategies.