Modeling Evolution of Crosstalk in Noisy Signal Transduction Networks

TL;DR

This paper investigates how crosstalk in noisy signal transduction networks evolves, revealing that different fitness functions can lead to either highly interconnected or highly specific signaling pathways.

Contribution

It introduces an evolutionary model showing how different fitness criteria influence the development of crosstalk or specificity in signaling networks.

Findings

Different fitness functions produce distinct network architectures.

High crosstalk networks emerge under certain fitness conditions.

Highly specific pathways evolve under alternative fitness criteria.

Abstract

Signal transduction networks can form highly interconnected systems within cells due to network crosstalk, the sharing of input signals between multiple downstream responses. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk and the emergence of specificity for two parallel signaling pathways that arise via gene duplication and are subsequently allowed to diverge. We focus on a sequence based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. Surprisingly, we find that the two fitness functions lead to very different evolutionary outcomes, one with a high degree of crosstalk and the other without.