Toward a probabilistic biology

Stochastic gene expression (SGE) is now considered to be an established fact and has become an important subject of research (Vinuelas et al . 2012). During the last decade, the availability of new techniques has made possible the production of more precise and more spectacular data showing the extensive variability in gene expression occurring between individual cells. However, evidence supporting probabilistic models of cell behaviour and gene expression has been available for quite a long time – for example, see the reviews in Laforge et al . (2005) and Golubev (2010). It should be noted that the first model of stochastic cell differentiation was proposed in 1964 (Till et al . 1964), which is almost at the same time as the genetic programming theory (Jacob and Monod 1961), which is deterministic in nature. One can thus wonder why the determinist view of biology has remained dominant for such a long time, and what makes a probabilistic view more acceptable nowadays? In this short paper, I will briefly argue that there is a strong epistemological obstacle to the acceptance of probabilism in biology, and that even today SGE is not integrated into a fully probabilistic approach of cellular processes, but rather into a concept that I call ‘determinism with noise’. I will argue that a new fully probabilistic theoretical framework is needed to truly integrate the stochastic aspects of cell physiology.