Jenna Elliott
@jennaelliott.bsky.social
62 followers 64 following 12 posts
Biology-inspired physics | Information processing | PhD student in the Erzberger group at EMBL | she/her
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Reposted by Jenna Elliott
embl.org
EMBL @embl.org · Sep 4
Congratulations, Michael Dorrity and Anna Erzberger!

The two EMBL Heidelberg group leaders have received ERC Starting Grants that will enable ambitious projects related to developmental timing and tissue self-organisation, respectively.

Learn more: www.embl.org/news/awards-...
Reposted by Jenna Elliott
gautamdey.bsky.social
Want to acquire #ExM images like this and help us understand the true extent of cytoskeletal diversity across the tree of life? This position might be for you!

embl.wd103.myworkdayjobs.com/en-US/EMBL/j...

With @dudinlab.bsky.social
@embl.org @biology-unige.bsky.social @moorefound.bsky.social
jennaelliott.bsky.social
Thanks for sharing! I think scale-hierarchical “zones” offer an interesting lens on sub-cellular information flow. I’d be curious how compression, selection, or computation at each scale could be approached analytically, especially with physical and emergent properties shaping the flow.
jennaelliott.bsky.social
10/ Huge thanks to my incredible coauthors Hiral Shah (@hiralshah.bsky.social), Roman Belousov, Gautam Dey (@gautamdey.bsky.social), and Anna Erzberger -- this project was a true collaboration, combining theory, modelling, and experimental validation. So grateful for your brilliance and support!
jennaelliott.bsky.social
9/ The observed patterns matched our model, and their parameters place these systems near the predicted optimal filtering regime -- These NPCs may act as efficient spatial thresholding filters! #Microscopy #QuantBio #Microtubules #UExM
Expansion microscopy image, and accompanying sketch, of S. arctica nucleus with labelled microtubules and nuclear pore complexes.
jennaelliott.bsky.social
8/ To test our predictions, we used expansion microscopy to examine distributions of nuclear pore complexes in Sphaeroforma arctica.
jennaelliott.bsky.social
7/ Surprisingly (and excitingly!), when we analysed real biological systems from the literature, their particle parameters often fell within these optimal regions, suggesting that cells might indeed be taking advantage of this mechanism! #EvoDevo #Biophysics
Plot of the mutual information between input energy profiles and output density profiles as a function of parameter ratio, for various input dimensions.
jennaelliott.bsky.social
6/ We identified an optimal phase space region where this classification works best. Interestingly, this region depends on the dimensionality of the input signal.
jennaelliott.bsky.social
5/ Cells could perform a binary classification of spatial cues based on particle organisation -- transmitting only “relevant” information across compartment interfaces (like membranes). It’s a simple yet energy-efficient and powerful way for cells to decide what signals to pass on. #CellSignaling
jennaelliott.bsky.social
4/ The particle distribution therefore acts like a spatial thresholding filter, providing a new way to think about how membrane-bound structures manage information flow. #MembraneBiology #InformationProcessing
jennaelliott.bsky.social
3/ We found that when surface-associated particles (e.g., proteins) repel each other and interact with nearby structures, their density exhibits a nonlinear, sigmoidal response to spatial features in the environment.
Plot of equilibrium particle density dependence on environment interaction energy, which takes the form of a sigmoid when there are particle repulsions, and is exponential if not.
jennaelliott.bsky.social
2/ Living systems use chemical signals to communicate, but physical properties like repulsion between particles also shape how information flows. We propose a physics-based mechanism by which cells could interpret spatial cues. #CellBiology #Biophysics #Physics
jennaelliott.bsky.social
🧵 🧪 1/ Hi! I’m excited to share our latest work, now on arXiv:

Repulsive particle interactions at cellular interfaces enable selective information processing (arxiv.org/abs/2506.14739)

Where we explore how the physical properties of living systems can help cells process spatial information.
Sketch of membrane-enclosed compartment with particles on its surface, which interact with an adjacent structure