Abstract
One indication of the relevance of stochastic laws in biology is the fact that genetically similar cells can behave in very different ways. This suggests an analogy with statistical physics, in which stochastic laws govern the behavior of large ensembles of particles. Biological randomness has important theoretical and practical consequences in medicine and may be responsible for the resistance of pathogens or cancer to drug treatment. Modeling biological randomness is now a major field in theoretical biology and has been approached with methods adapted from physics. Like in physics, biological systems are governed by hierarchical processes involving variables with different timescales. Contrary to most physical processes, biological systems can exhibit inversions in the relationship between timescale and hierarchical rank. As a consequence, microscopic fluctuations can be transmitted to and even amplified by the phenotype. Because of these peculiarities, new approaches are needed for the study of biological randomness.