Geometric Characteristics of Dynamic Correlations for Combinatorial Regulation in Gene Expression Noise

TL;DR

This paper introduces a method using dynamic cross-correlation functions to distinguish between AND and OR combinatorial regulation modes in gene expression noise, aiding in understanding gene regulatory network functions.

Contribution

It presents a novel approach to infer regulatory logic in gene expression by analyzing the convexity of correlation functions, applicable to various noise sources.

Findings

Correlation is upward convex for AND regulation.

Correlation is downward convex for OR regulation.

Method works with intrinsic and extrinsic noise sources.

Abstract

Knowing which mode of combinatorial regulation (typically, AND or OR logic operation) that a gene employs is important for determining its function in regulatory networks. Here, we introduce a dynamic cross-correlation function between the output of a gene and its upstream regulator concentrations for signatures of combinatorial regulation in gene expression noise. We find that the correlation function is always upwards convex for the AND operation whereas downwards convex for the OR operation, whichever sources of noise (intrinsic or extrinsic or both). In turn, this fact implies a means for inferring regulatory synergies from available experimental data. The extensions and applications are discussed.