Complex autoimmune diseases, such as multiple sclerosis (MS), develop as a result of perturbations in the regulatory system controlling the function of immune cells. The disease course of MS is heterogeneous but is characterised by chronic inflammation in the central nervous system causing neurodegeneration resulting in gradual disability worsening. Disease biomarkers which are present at early stages of a disease can help clinicians to tailor treatment strategies to the expected disease course of individual persons. Gene products, i.e. RNA and proteins, serve as promising disease biomarkers due to the possibility to detect changes in abundance at early stages of a disease. Putative biomarkers can be identified by modelling different levels of gene regulation from high-throughput measurements of gene product abundance. Extracting information of disease relevance from high-throughput data is a complex problem which requires the use of efficient and targeted computational algorithms. 

The aim of this thesis was to develop and refine methods for identifying key biomarkers involved in the development and progression of complex diseases, with the main focus on MS. In Paper I, we used a machine learning approach to identify a combination of protein biomarkers, present in the cerebrospinal fluid, which could predict the disease trajectory of persons in the early stages of MS. The abundance of proteins is a result of an intricate network of multiple regulatory factors controlling the expression of genes. A large part of the expression of genes is controlled by a few key regulators, which are believed to be crucial for the development of diseases. In addition, disease-associated genes are believed to colocalise in these networks forming so called disease modules. In Paper II, we developed a method, named ComHub, for extracting the key regulators of gene expression. In Paper III, we combined ComHub with the tool MODifieR, for disease module predictions, in a network analysis pipeline for identifying a limited set of disease-associated genes. Using this network analysis pipeline we identified a set of MS-associated genes, as well as a promising key regulator of MS. 

The work performed in this doctoral thesis covers development of new and refined methods for modelling complex diseases, while simultaneously utilising these methods to identify disease biomarkers important for the development and progression of MS. The identified biomarkers can be used for understanding the pathology of MS, as candidate drug targets, and as promising biomarkers to aid clinicians in tailoring treatment strategies to individual persons.