Tina Šubic
@tincamalinca.bsky.social
Postdoc at Chikina lab, Department of Computational and Systems Biology, University of Pittsburgh.
I’m combining biophysical modeling with machine learning to study genome folding.
I’m combining biophysical modeling with machine learning to study genome folding.
✨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.
🧵
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.
🧵
Mechanistic Genome Folding at Scale through the Differentiable Loop Extrusion Model https://www.biorxiv.org/content/10.1101/2025.10.17.682904v1
October 21, 2025 at 1:51 PM
✨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.
🧵
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.
🧵
Reposted by Tina Šubic
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...
Learning locally dominant force balances in active particle systems | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
We use a combination of unsupervised clustering and sparsity-promoting inference algorithms
to learn locally dominant force balances that explain macroscopic pattern formation
in self-organized active...
doi.org
December 21, 2024 at 8:13 AM
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:
Loss of bimolecular reactions in reaction–diffusion master equations is consistent with diffusion limited reaction kinetics in the mean field limit
We show that the resolution-dependent loss of bimolecular reactions in spatiotemporal Reaction–Diffusion Master Equations (RDMEs) is in agreement with the mean-
doi.org
December 20, 2024 at 1:00 PM
🧵 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 🧵
A Gaussian jump process formulation of the reaction–diffusion master equation enables faster exact stochastic simulations
We propose a Gaussian jump process model on a regular Cartesian lattice for the diffusion part of the Reaction–Diffusion Master Equation (RDME). We derive the r
doi.org
December 19, 2024 at 9:15 PM
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.
December 19, 2024 at 9:13 PM
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!
November 14, 2024 at 3:09 PM
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!