However,despite the many molecular and cellular studies, we still lack a comprehensive understanding of how the immune Orbifloxacin system is controlled and how autoimmune diseases arise. Given the complex interactions between the cellsand molecules that regulate thisprocess, a systems approach to analyse these processes might identify critical functional interactions that are disturbed in autoimmune diseases. Moreover, the identification of such pathological interactions might facilitate the development of new therapeutic targets. MS is a chronic inflammatory and neurodegenerative disease of the central nervous system. MS is characterized by the presence of plaques composed by chronic inflammatory infiltrates, including T and B cells as well as monocytes into the brain, accompanied by the presence of large areas of demyelination and axonal loss. MS is the second cause of permanent disability in young adults after spinal cord injury and due to its chronic nature imposes a significant health and social cost in western countries. Although current immunotherapies are able to modify disease course, we still need to develop more effective and safe therapies for improving the quality of life of patients. The development of network theory is providing important insights into gene and protein networks. However, the translation of such advances to humans complex diseases such as autoimmune diseases is confronted with many challenges, such as incomplete knowledge of the molecules involved, lack of quantitative data, the higher degree of complexity and the limited availability of analytical methods. Among several methods of network analysis for reconstructing network topology from experimental datasets, Bayesian networks are those that offer the best results. In human complex diseases, the use of different clinical phenotypes such as quantitative traits, disease subtypes or therapies, can introduce meaningful perturbations into a network to help infer its topology. In the Echinatin present study we have provided proof of concept that a gene network analysis approach is feasible to study human systems and diseases, providing valuable information about the complex interactions involved in biological process and in complex diseases. This is important since most systems biology studies have been applied to lower organisms and its application to higher animals and humans has been restricted by the lack of biological knowledge, technological and analytical tools, as well as by the higher degree of complexity of such organisms. Biological functions, as well as complex traits and diseases, can only rarely be attributed to an individual molecule. On the contrary, complex interactions between dozens of molecules lead to a specific biological function, and altering the relationships between these elements may disrupt the activity in such systems. Network analysis has emerged as a powerful tool to understand complex intercellular interactions that contribute to the structure and function of living systems and it can be used to study complex traits and diseases in order to discover new therapies. Indeed, the application of a Bayesian approach to define cell networks has been successfully used and as well as to infer classic, well understood signalling networks. Such an approach has also provided new insights into specific systems that have not been previously identified through hypothesis-directed research. In our network analysis, 
we identified 31 interactions between 20 genes acting in the immune system of which, 18 were predicted either from the literature or by bioinformatics analysis of coexpression databases. However, the other 13 interactions were new and had not been predicted using bioinformatics strategies.