Network analysis to model diseases and cellular reprogramming for therapeutic intervention

Applications of network analysis to the study of disease can be divided into two main categories: disease description, including characterisation, diagnosis and prognosis; and disease treatment, including drug target discovery and cellular reprogramming, together with its applications to regenerative medicine. In this dissertation, I will critically discuss some research projects on which I have been working during my PhD program. In correspondence with the two aforementioned categories, these projects can be broken down into two different blocks of content, with the common goal of acquiring insights into the study of disease.
In the first block of contents, corresponding with Chapter 2, I will explain and discuss novel strategies for network-based analysis and modelling which have been applied for disease description and characterisation in different case-studies, namely the metabolic syndrome, prion disease and the epithelial to mesenchymal transition in breast cancer. Indeed, these projects exploited the evolutionary conservation of motifs of regulatory interactions and consistency between computed and experimentally validated expression so as to reconstruct dynamical models and create a network-based characterisation of the corresponding systems.
With regards the second block of content, corresponding with Chapter 3, I explain and discuss novel computational methods which have been developed during my PhD program to address the task of the artificial induction of cellular reprogramming; something with a wealth of potential applications when it comes to the creation of disease models and in the field of regenerative medicine.
Within the general conclusion discussion focuses on the fact that, although the methodology explained in this work was developed in the context of disease study, one may find the application of some of these ideas and strategies fitting for other problems. Indeed, the same principles applied to detect driver genes capable of changing the cell phenotype when perturbed can also be applied to control biological living systems for basic research or industrial purposes. These principles could also be potentially extended to higher level systems than the cellular level (tissue or cell population level).