New Publication: NRas slows the rate at which a model lipid bilayer phase separates Philip Fowler, 13th June 2014 Here we examine by computer simulation what effect adding a small cell-signalling protein does to a model ternary lipid mixture that has been shown before to phase separate. This paper was presented at the 169th Faraday Discussion meeting in Nottingham in May 2014, the theme of which was Molecular simulations and visualization. We followed the progress of the phase separation of the lipid bilayer by measuring the length of the interface using an edge detection algorithm from image processing. An example python script can be downloaded here. We found that the protein, NRas, indeed slows down the rate at which the bilayer phase separates. The protein also tends to localise to the interface between the domains which is consistent with it acting to reduce the line tension between the phases. The questions asked during the discussion (and my answers) will be posted on the journal’s website soon. I’ll update this post when that happens. This paper is open access so is free to download. Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Related publication
publication New Publication: Membrane Compartmentalization Reduces the Mobility of Lipids. 23rd September 201629th September 2018 Lipids are not free to diffuse around the cell membrane. Rather they are constrained not… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network 29th October 202530th October 2025 In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
publication New publication: Nothing to Sneeze At – A Dynamic and Integrative Computational Model of an Influenza A Virion 6th March 2015 In this paper we show how we built and then simulated a model of the… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More