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Tina Šubic

@tincamalinca.bsky.social
155 followers 344 following 29 posts

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.

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Tina Šubic @tincamalinca.bsky.social · 21/10/2025
✨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. 🧵
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Reposted by Tina Šubic
Sbalzarini Lab / MOSAIC Group @mosaicgroup.bsky.social · 21/12/2024
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...
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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...
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Tina Šubic @tincamalinca.bsky.social · 20/12/2024
🧵 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:
doi.org
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-
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Tina Šubic @tincamalinca.bsky.social · 19/12/2024
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 🧵
doi.org
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
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Tina Šubic @tincamalinca.bsky.social · 19/12/2024
My second PhD paper just got published! 🎉 So, it’s good time to introduce my PhD work to Blue Sky.
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Tina Šubic @tincamalinca.bsky.social · 14/11/2024
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!
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