Dynamic phenotypes as criteria for model discrimination: fold-change detection in R. sphaeroides

TL;DR

This paper investigates fold-change detection (FCD) in Rhodobacter sphaeroides chemotaxis, presenting models that predict FCD behavior and proposing experiments to discriminate between models based on dynamic phenotypes.

Contribution

It introduces theoretical assumptions and models that demonstrate FCD in R. sphaeroides and suggests experimental tests to validate these models, advancing understanding of bacterial chemotaxis dynamics.

Findings

Models reproduce experimental time-series data.

Predicted robust FCD behavior in specific ligand ranges.

Proposed experiments to distinguish model validity.

Abstract

The chemotaxis pathway of the bacterium Rhodobacter sphaeroides has many similarities to the well-studied pathway in Escherichia coli. It exhibits robust adaptation and has several homologues of the latter's chemotaxis proteins. Recent theoretical results have been able to correctly predict that the chemotactic response of Escherichia coli exhibits the same output behavior in response to scaled ligand inputs, a dynamic property known as fold-change detection (FCD), or input-scale invariance. In this paper, we present theoretical assumptions on the R. sphaeroides chemotaxis sensing dynamics that can be analytically shown to yield FCD behavior in a specific ligand concentration range. Based on these assumptions, we construct two models of the full chemotaxis pathway that are able to reproduce experimental time-series data from earlier studies. To test the validity of our assumptions, we propose a series of experiments in which our models predict robust FCD behavior where earlier models do not. In this way, we illustrate how a dynamic phenotype such as FCD can be used for the purposes of discriminating between two models that reproduce the same experimental time-series data.