Fundamental Dynamic Units: Feedforward Networks and Adjustable Gates

TL;DR

This paper explores how feedforward networks and adjustable gates in gene regulation can generate diverse dynamic behaviors, revealing their potential as fundamental units in biological networks.

Contribution

It introduces novel gene regulatory motifs with tunable functions, demonstrating their ability to act as filters, oscillators, switches, and adjustable logic gates.

Findings

Feedforward motifs function as amplitude filters and concentration detectors.

Tunable motifs can act as oscillators or switches based on input levels.

Adjustable gates switch between AND/OR logic depending on transcription factor concentration.

Abstract

The activation/repression of a given gene is typically regulated by multiple transcription factors (TFs) that bind at the gene regulatory region and recruit RNA polymerase (RNAP). The interactions between the promoter region and TFs and between different TFs specify the dynamic responses of the gene under different physiological conditions. By choosing specific regulatory interactions with up to three transcription factors, we designed several functional motifs, each of which is shown to perform a certain function and can be integrated into larger networks. We analyzed three kinds of networks: (i) Motifs derived from incoherent feedforward motifs, which behave as `amplitude filters', or `concentration detectors'. These motifs respond maximally to input transcription factors with concentrations within a certain range. From these motifs homeostatic and pulse generating networks are derived. (ii) Tunable network motifs, which can behave as oscillators or switches for low and high concentrations of an input transcription factor, respectively. (iii) Transcription factor controlled adjustable gates, which switch between AND/OR gate characteristics, depending on the concentration of the input transcription factor. This study has demonstrated the utility of feedforward networks and the flexibility of specific transcriptional binding kinetics in generating new novel behaviors. The flexibility of feedforward networks as dynamic units may explain the apparent frequency that such motifs are found in real biological networks.