Dynamics of Complex Networks

Statistical physics is the theory of interacting particles, gases and liquids. Its way of thought, however, goes beyond the domain of material science. In a broader perspective what it provides us with is a bridge between the microscopic description of a system and its observed macroscopic behavior. With it we can track the way in which system-level phenomena emerge from the mechanistic description of the system’s interacting components. For instance how the blind interactions between pairs of magnetic spins lead to the seemingly cooperative phenomena of magnetism. In our lab we are developing the statistical physics of complex systems: our interacting particles are not atoms or spins, but rather genes and proteins, animal species or humans. We track the way in which individual human interactions lead to the spread of ideas, perceptions and also diseases, or how biochemical reactions between proteins transfer information between cellular components. These systems defy many of the classic principles that are central to the way physics is traditionally done. The particles are self-driven and non-Newtonian, the interactions are nonlinear and the underlying geometry in random and non-localized. In two words – these are complex systems. With these challenges at hand, we find that the dynamical behavior of these complex systems – social, biological or technological – can be predicted, analyzed and understood using the tools and, indeed, the way of thought, of statistical physics.