@sreejithsanthosh.bsky.social

PhD (physics) at UC San Diego. ex- IIT Madras. Biophysics, dynamical systems and developmental biology. Website : sreejithsanthosh.github.io

As a modeller, it’s sometimes hard to convey the idea that adding complexity to our models (ostensibly making them more realistic) doesn’t necessarily give us more explanatory power. This paper makes that point clear by arguing why simpler models are often more illuminating. Congrats to the authors!

Alejandro Fábregas-Tejeda@alejandrofabregastejeda.com · 2mo ago

As simulations grow in complexity, a set of non-trivial issues emerge: diminished identifiability as free parameters become too numerous relative to exp. constraints & difficulties grasping causality. In our new 📄, we point to potential avenues to move #CellBio forward 👇 arxiv.org/abs/2608.06998

How can we learn tissue mechanics directly from cell patterns and images? In our new preprint, we introduce VertAX, a differentiable vertex-model framework in JAX for simulating epithelia, inferring parameters, and designing target tissue behaviors. shorturl.at/PUzT0 1/5

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We have postdoc openings for data-driven modeling and information flows in living systems, with a focus on embryonic development. mattiaserra.com Requirements: PhD in Physics, Applied Math, or similar; prior exposure to biological data preferred. Please share with potential candidates. Thank you!

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Now #published @physreve.bsky.social: "Elasticity and plasticity of epithelial gap closure" doi.org/10.1103/qtcv-gk3y #PhD work of Maryam Setoudeh, a model of cell intercalations during epiboly as elastoplastic deformations of a surface. @mpipks.bsky.social @mpi-cbg.de @csbdresden.bsky.social

Pierre Haas@lepuslapis.bsky.social · last yr.

Another #preprint: "Elasticity and plasticity of epithelial gap closure", from the #PhD work of Maryam Setoudeh: arxiv.org/abs/2509.05110 This one required a lot of (I think) beautiful #mechanics! @mpipks.bsky.social @mpi-cbg.de @csbdresden.bsky.social

How can we hope to understand organismal development when it is controlled by huge, complex networks of interacting genes? 
One option is to move away from molecular details and focus on learning representations and rules. Check out the new perspective from me and @jamesbriscoe.bsky.social

James Briscoe@jamesbriscoe.bsky.social · 7mo ago

New Perspective form Rory Maizels & me: "Gene regulatory networks: from correlative models to causal explanations" Gene regulatory networks are supposed to give us mechanistic explanations of development, so why are we drowning in 'hairballs' of statistical correlations? rdcu.be/e7zx7

New preprint! arxiv.org/abs/2602.08105 We use mutual information to find the shared dimensionality of shared latent space between two high-dimensional variables. Turning inference into optimization, we use it to figure out dofs in movies and critical scaling in Ising!

Mutual information and task-relevant latent dimensionality

Estimating the dimensionality of the latent representation needed for prediction -- the task-relevant dimension -- is a difficult, largely unsolved problem with broad scientific applications. We cast ...

arxiv.org

Happy to share our new work from my graduate lab: DynamicAtlas, a morphodynamic atlas of Drosophila development! We develop methods for morphological time alignment of live data. Surprisingly, we find that the kinematics of fly morphogenesis are *very* simple. Enjoy! 
www.nature.com/articles/s41...

DynamicAtlas: a morphodynamic atlas for Drosophila development - Nature Methods

DynamicAtlas integrates fixed and live imaging data to generate a morphodynamic atlas of Drosophila development.

nature.com