Toward Biochemical Probabilistic Computation

TL;DR

This paper proposes that probabilistic computation in living organisms is performed by interacting populations of macromolecules and diffusible messengers, extending the concept of Bayesian reasoning to cellular signaling pathways across all life forms.

Contribution

It introduces a novel hypothesis that biochemical processes can implement probabilistic reasoning, expanding the understanding of neural and cellular computation mechanisms.

Findings

Biochemical pathways can perform probabilistic inference.

Probabilistic reasoning is fundamental at the cellular level.

Supports the idea that simple organisms can compute probabilities.

Abstract

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.