In the past century several disciplines have evolved from a descriptive point of view to a quantitative one. Biology is an interesting case, and has give rise to mathematical Biology and Biophysics. Roughly speaking, the advantage of a quantification process (models) lies in its predictive properties that strongly depends on a compromise between simplification (identification of fundamental mechanisms), and the complexity that is inherent to Biology. Thus, the mathematical and in silico approaches constitute a new frontier that is becoming increasingly important at an accelerated pace to experimental biologist since may suggest in vitro and in vivo experiments.
Our research lines lie within the Statistical Mechanics framework applied to Biological problems. The former is a discipline that tries to understand how collective behavior arises from mutual interaction between individual units. Then, Biological problems are suitable to study within this formalism since Biological complexity always comes from activity of small units that drives the evolution of the system as an ensemble. Complementary, during the last thirty years a number of studies have revealed that the fluctuations play a key role in physical, chemical, and biological processes. Therefore, to consider the effect of stochastic perturbations in biological models is, in some cases, a way to incorporate part of the complexity and even of the fundamental aforementioned mechanisms.
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