Systems Biology #4: Autoregulation as a Network Motif
A new interactive article on how to find patterns in a network, and what to make of them when they occur in transcription networks
I've published a new interactive article on Newt Interactive, the fourth in the Systems Biology series, that introduces the topic of autoregulation and explores how we can start to understand complex biological networks by breaking them down into smaller networks and looking for patterns. The article includes clear explanations, easy-to-follow math, and a couple of interactives, including the Erdős-Rényi graph generator I shared last week. Here’s a quick screen recording:
As you follow along, you’ll learn the concept of network motifs through the lens of gene regulation in E. coli. We start with a seemingly chaotic network of hundreds of genes and their interactions, but then zoom in on a fascinating pattern: genes that regulate themselves — autoregulation.
What makes this particularly interesting is how we determine whether these patterns are meaningful or just random occurrences. Through interactive visualizations, I show how to:
Compare "real" biological networks with random ones
Generate and explore different random network configurations
See how network statistics change as parameters are adjusted
I also break down some of the math a little more clearly, like explaining where the standard deviation equation for a network comes from (the side drawer with extra info in the screen recording). It’s what’s used to compare whether a pattern is a motif — i.e. whether it occurs far more often than it would at random. In the article, you’ll learn how in a portion of E.Coli, autoregulation occurs at a frequency about 35 standard deviations away from random.
This is the first in the chapter about autoregulation in biological systems. The next part will explore why negative autoregulation (genes that repress their own expression) is so prevalent in nature and what advantages it provides to organisms.
Thanks for reading and stay tuned!
(If you’re wondering why this is #4, articles 1-3 were shared in my introductory post on my Systems Biology series). You can also check them out here:
Transcription Network Basics: the fundamental concepts of transcription factors and gene expression
Activators and Repressors: the two types of transcription factors and their mathematical models (Hill function)
Dynamics and Response Time: how cells respond to changes in signals and modelling how long that takes