Generalized end-product feedback circuit senses high dimensional environmental fluctuations

TL;DR

This paper extends the understanding of simple biological regulatory circuits, demonstrating that a generalized end-product feedback circuit can learn multiple environmental fluctuation modes in higher dimensions, beyond just the dominant one.

Contribution

It introduces a generalized model of end-product feedback circuits capable of learning multiple environmental fluctuation modes in high-dimensional settings.

Findings

Circuit learns dominant fluctuation directions.

Circuit can identify subdominant modes.

Model extends previous one-dimensional analysis.

Abstract

Understanding computational capabilities of simple biological circuits, such as the regulatory circuits of single-cell organisms, remains an active area of research. Recent theoretical work has shown that a simple regulatory architecture based on end-product inhibition can exhibit predictive behavior by learning fluctuation statistics of one or two environmental parameters. Here we extend this analysis to higher dimensions. We show that as the number of inputs increases, a generalized version of the circuit can learn not only the dominant direction of fluctuations, as shown previously, but also the subdominant fluctuation modes.