Optimization of Enzymatic Logic Gates and Networks for Noise Reduction and Stability

TL;DR

This paper reviews methods to optimize enzymatic biochemical logic gates and networks to minimize noise amplification and enhance stability, using kinetic analysis and response function fitting.

Contribution

It introduces a systematic approach to optimize enzymatic logic gates and networks for noise reduction through kinetic analysis and response function adjustments.

Findings

Using co-substrates with smaller affinities reduces noise buildup.

Fitting response functions helps identify and improve gate quality.

Modified systems operate with lower analog noise amplification.

Abstract

Biochemical computing attempts to process information with biomolecules and biological objects. In this work we review our results on analysis and optimization of single biochemical logic gates based on enzymatic reactions, and a network of three gates, for reduction of the "analog" noise buildup. For a single gate, optimization is achieved by analyzing the enzymatic reactions within a framework of kinetic equations. We demonstrate that using co-substrates with much smaller affinities than the primary substrate, a negligible increase in the noise output from the logic gate is obtained as compared to the input noise. A network of enzymatic gates is analyzed by varying selective inputs and fitting standardized few-parameters response functions assumed for each gate. This allows probing of the individual gate quality but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify experimental single gate and network systems to operate them in a regime of reduced analog noise amplification.