Mechanistic computational models, particularly rule-based stochastic models, are a vital complement to wet-lab experiments (and a vital chunk of our work at Amber Biology), but can also provide insights into evolutionary processes. In a paper just published in Nature Communications, the team, led by Kun Xiong and Joanna Masel, and included myself and Mark Siegal, asked whether a particular 3-node feed-forward loop motif (specifically the type 1 coherent FFL, or C1-FFL), widely hypothesized to have evolved to filter out spurious signals, actually evolved for that purpose. Due to it’s overrepresentation in the transcriptional networks of many species, and it’s  function in filtering out spurious input signals many researchers have previously accepted this ‘just-so’ account of the origin of the feed-forward motif. To test this hypothesis properly, we built a detailed stochastic model of the dynamics of transcriptional networks, and then allowed the network to evolve under selection for the function, and without selection for the function to see under which scenario the motif evolved (see also last year’s Ronin Institute seminar).

Spoiler alert: it looks like selection was responsible, but we learned much much more, including the fact that different kinds of motifs also can evolve for the same function. The study reinforces the important role that in-silico approaches have in understanding the “noisy” world of biological systems. The paper is open-access and you can read the work in full in the link below, all code is also available on GitHub.

Read full open-access paper Xiong, Lancaster, Siegal, Masel (2019) at Nature Communications…