  Living organisms survive and multiply even though they have uncertain and
incomplete information about their environment and imperfect models to predict
the consequences of their actions. Bayesian models have been proposed to face
this challenge. Indeed, Bayesian inference is a way to do optimal reasoning
when only uncertain and incomplete information is available. Various
perceptive, sensory-motor, and cognitive functions have been successfully
modeled this way. However, the biological mechanisms allowing animals and
humans to represent and to compute probability distributions are not known. It
has been proposed that neurons and assemblies of neurons could be the
appropriate scale to search for clues to probabilistic reasoning. In contrast,
in this paper, we propose that interacting populations of macromolecules and
diffusible messengers can perform probabilistic computation. This suggests that
probabilistic reasoning, based on cellular signaling pathways, is a fundamental
skill of living organisms available to the simplest unicellular organisms as
well as the most complex brains.
