Precision medicine is centred on the objective of integrating a patient's data (genetic and clinical) with knowledge about the biological mechanisms of the disease, in order to identify its own activation mechanisms, and based on them, optimal therapeutic strategies for the patient's case. A patient's genomic data can be integrated into a network of disease- and patient-specific interactions, describing the individual's disease configuration. Computational modelling in precision medicine, in particular network analysis, is well suited to identify novel combinatorial therapies. Our project focuses on the problem of controlling a dynamic network. The general problem that we are working on is that of a network in which we seek control over a manifold of targets, in the sense that we achieve the ability to change their configuration through a series of external interventions in a series of input nodes in the network, aided by the topology of the network. We are interested in finding a minimal set of input nodes in the network so that the configuration of the targets can be changed by a series of signals applied to the input nodes, cascaded through the network. We focus on formalising the controllability problem that maximises the applicability of this method in medicine. The target nodes we choose are genes essential for cancer cell survival, and the input nodes are genes on which oncological drugs approved for treatment act. We will test the applicability of these methods in the medical context through two case studies: glioblastoma and multiple myeloma.