Computational physics for the study of complex networks
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Prof. Roger GUIMERA & Prof. Marta SALES, Biochimics, Universitat Rovira i Virgili, Tarragona, Spain. Cells, the brain, ecosystems and economies are complex systems. In complex systems, individual components interact with each other, usually in nonlinear ways, giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction topology, complex systems cannot be properly understood by just analyzing their constituent parts. For example, one cannot properly understand consciousness by studying isolated neurons, or economic crises by studying isolated individuals.\\n
The reason why complex networks of interactions are non-trivial is that any bias, however small, in the way components establish connections gives rise to structural correlations. This makes understanding complex systems challenging but, at the same time, it means that each network contains, hidden within its structure, important clues about how the system operates and evolves. Recent technological developments have made it possible to gather unprecedented amounts of data on a variety of complex systems from social to biological. However, our knowledge on these systems has not increased proportionally due to the lack of tools to extract information from large pools of data and to assess data reliability. In this talk we will discuss recent developments on complex networks theory that tackle the aforementioned challenges and what are the implications for systems biology and social problems.