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

Source: [A stochastic molecular scheme for an artificial cell to infer its environment from partial observations](<https://www.sota2.com/research/paper/68fa35811e209131ce8a1fde>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T21:17:23.261Z

Authors: Muppirala Viswa Virinchi, Abhishek Behera, Manoj Gopalkrishnan

Published: 2017-04-06T07:42:50.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1704.01733>)

[PDF](<https://arxiv.org/pdf/1704.01733.pdf>)