A stochastic molecular scheme for an artificial cell to infer its environment from partial observations

TL;DR

This paper introduces a novel stochastic reaction network scheme that enables an artificial cell to infer its environment from partial data by encoding conditional distributions as equilibrium states, leveraging the inherent stochasticity for information processing.

Contribution

It presents the first scheme to utilize stochastic reaction networks for performing information projection, linking thermodynamics and statistical inference in artificial cells.

Findings

Successfully encodes conditional distributions as reaction network equilibria

Demonstrates the application to environmental inference in artificial cells

Bridges stochastic thermodynamics with statistical inference methods

Abstract

The notion of entropy is shared between statistics and thermodynamics, and is fundamental to both disciplines. This makes statistical problems particularly suitable for reaction network implementations. In this paper we show how to perform a statistical operation known as Information Projection or E projection with stochastic mass-action kinetics. Our scheme encodes desired conditional distributions as the equilibrium distributions of reaction systems. To our knowledge this is a first scheme to exploit the inherent stochasticity of reaction networks for information processing. We apply this to the problem of an artificial cell trying to infer its environment from partial observations.