✨New preprint!✨ We built dLEM - a differentiable Loop Extrusion Model that bridges biophysics and machine learning for 3D genome folding. dLEM makes loop extrusion learnable and interpretable—predicting how genomes fold and how they respond to perturbations. 🧵

Learning interpretable mathematical models from data is transforming science. But what if different models apply in different areas or at differnt times? After all, living systems are plastic and self-organized. Now you can learn *local* model clusters! doi.org/10.1098/rspa...

🧵 We @mosaicgroup.bsky.social just published in @aip-publishing.bsky.social JChemPhys (doi.org/10.1063/5.02...): Turns out that grid resolution in stochastic reaction-diffusion models represents molecule size! This completely changes how we understand "reaction loss" in these models:

GRDME (Gaussian Reaction Diffusion Master Equation) is a method that makes exact stochastic simulations of molecular interactions more efficient! It was also the subject of my first PhD paper (doi.org/10.1063/5.01...) with @mosaicgroup.bsky.social from my time at @mpicbg.bsky.social 🧵

My second PhD paper just got published! 🎉 So, it’s good time to introduce my PhD work to Blue Sky.

Great to be here! I guess I should introduce myself: I’m a postdoc (Chikina lab), using physical modeling and machine learning to study transcription and genome organization. But, I’d really like to get into plant science and tackling climate change!