Control of Noise in Chemical and Biochemical Information Processing

TL;DR

This paper reviews error-control models and approaches for chemical and biochemical computing, focusing on preventing noise buildup in scalable reaction-based information processing networks.

Contribution

It provides a comprehensive survey of models, methodologies, and future challenges in error control for chemical and biochemical information processing.

Findings

Illustrates rate-equation models for gate optimization

Summarizes recent methodologies for error control

Discusses future research directions

Abstract

We review models and approaches for error-control in order to prevent the buildup of noise when gates for digital chemical and biomolecular computing based on (bio)chemical reaction processes are utilized to realize stable, scalable networks for information processing. Solvable rate-equation models illustrate several recently developed methodologies for gate-function optimization. We also survey future challenges and possible new research avenues.