Hey, I wrote a thing about AI in astrophysics
ergosphere.blog/posts/the-ma...

Do different regions in the cell nucleus find each other by random motion or is there a directed component? In short: likely both. Below, I summarize a few predictions from a recently updated preprint from last June: doi.org/10.48550/arX.... (1/9)
#biophysics #theory #active #chromatin #condensates

On this 1st April I'm happy to release a technical report from our startup MirageBio, describing a new protein structure model with state-of-the-art performance.

jgreener64.github.io/posts/techni...

Papers are like buses... You wait for ages, then two come along at once.

Huge congrats to @bornanovak.bsky.social and @jefflotthammer.bsky.social for pushing and driving every aspect of this work, preprinted ~1 year ago to the day (Friday before BPS), now published!

www.nature.com/articles/s41...

Pleased to share the final version of this behemoth of a paper, now finally published. I guess I can retire now?
www.nature.com/articles/s41...

More functional data, many thousands of words removed, and a few other updates from last year's preprint.

Congrats jerelle and Pablo:)

Congrats @kyogok.bsky.social and team :) nice paper and really clear biophysical data.

Gene-scale in vitro reconstitution reveals histone acetylation directly controls chromatin architecture Reconstituting 20-kb chromatin shows that tuning acetylation alone reshapes its folding, dynamics, and contact domain formation.

To probe gene-scale chromatin physics, we built 96-mer (20 kb) arrays with defined histone marks. Combining single-molecule tracking, AFM imaging, and developing in vitro Hi-C, we saw how specific modifications dictate chromatin structure and dynamics. www.science.org/doi/10.1126/...

Selective RNA sequestration in biomolecular condensates directs cell fate transitions - Nature Biotechnology Stem cell differentiation is controlled by manipulating RNA condensates.

1/ Excited to share our new study with @brumbaugh-lab.bsky.social, out in @natbiotech.nature.com! P-bodies selectively sequester RNAs encoding cell fate regulators, often from the preceding developmental stage. Releasing these RNAs can drive changes in cell identity. 🧵 www.nature.com/articles/s41...

Congrats Ben, Hue Sun, and all authors!

Sequence-based prediction of condensate composition reveals that specificity can emerge from multivalent interactions among disordered regions - Communications Chemistry Condensates composed of the disordered region of the mediator of RNA polymerase II transcription subunit 1 (MED1) are known to partition specific proteins, but whether this specificity arises from ord...

In collaboration with Hue Sun Chan and @jonaswessen.bsky.social, we present a polymer theory for sequence-based prediction of selective partitioning of charged IDRs. This method correctly predicted the partitioning of IDRs for which we had no experimental data. www.nature.com/articles/s42...

Happy to share our latest in @natcomputsci.nature.com
led by (amazing) Ryan Krueger + colab w M. Brenner!

We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...

Thanks karthik :)

Our framework:

We bridge machine learning & statistical physics to directly invert molecular simulations to design IDPS and engineer examples that:

🌀 form loops & linkers with tuned flexibility
⚡ sense salt, temperature, or phosphorylation stimuli
🤝 bind disordered targets like FUS or Whi3

The problem:
AI tools like AlphaFold & ProteinMPNN accelerate design of stable protein folds by inverting the sequence-structure map.

But IDPs don't have 1 shape - they occupy a huge ensemble of shapes. Physics simulations are good models to generate ensembles but hard to design/invert over!

Happy to share our latest in @natcomputsci.nature.com
led by (amazing) Ryan Krueger + colab w M. Brenner!

We introduce a framework to directly design intrinsically disordered proteins (IDPs) from physics-based simulations.
🧬 doi.org/10.1038/s435...
📰 www.mccormick.northwestern.edu/news/article...

Coupling and decoupling of the cell cycle from cell differentiation in development Summary: This Spotlight surveys investigations of the dependence of cellular differentiation on the cell cycle in animals. Strict dependence is uncommon. The decoupling of the cell cycle and different...

Our review on cell cycle – cell fate (de)coupling is out! doi.org/10.1242/dev....
Was a lot of fun writing this with Allon Klein, reading old papers(earliest from 1902), and speculating on why cell cycle progression is not necessary for differentiation across many many tissues and species.
(1/3)

Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins - Nature Computational Science The authors introduce a method that combines physics and machine learning to design dynamic unstructured proteins with tunable ensemble properties like size, shape, sensing and binding.

📢 @shrinivaslab.bsky.social and colleagues introduce a method for designing unstructured proteins with tunable properties. www.nature.com/articles/s43... 🖥️ 🧬

🔓 rdcu.be/eJSuV

A good day to remember John Gurdon’s school report from his biology master at Eton

Many congrats Alex! Your labs research has been a pleasure to read (and try code openly). Hope you are celebrating 🍾

Preprint!

Inspired by condensates that form on specific DNA, we ask:

can we design multicomponent fluids to form distinct condensates on diff. surfaces?

i.e. perform classification by condensation ⚛️ 💻 exploiting phase transitions beyond compartmentalization!
arxiv.org/abs/2509.08100
(1/2)

Led by the amazing Aidan Zentner, with contribs from Ethan Halingstad, and in collab with Cameron Chalk, Michael Brenner, @amurugan.bsky.social, and Erik Winfree.

For a more fun overview, see Erik's version of the abstract www.dna.caltech.edu/DNAresearch_... :) (2/2)

Preprint!

Inspired by condensates that form on specific DNA, we ask:

can we design multicomponent fluids to form distinct condensates on diff. surfaces?

i.e. perform classification by condensation ⚛️ 💻 exploiting phase transitions beyond compartmentalization!
arxiv.org/abs/2509.08100
(1/2)

Bioengineer Clifford Brangwynne wins Keio Medical Science Prize Japan’s Keio University awards the prize annually to honor contributions in medicine and life sciences. Brangwynne is being recognized for groundbreaking work that has opened up a new field of cell bi...

A tremendous honor! Thrilled & humbled to receive 2025 Keio Medical Science Prize for launching LLPS #phaseseparation field (= #softmatter + #cellbio) w collaborators esp @HymanLab. & Congrats to Akiko Iwasaki @virusesimmunity.bsky.social. www.princeton.edu/news/2025/09...
#KeioMedicalSciencePrize

congrats Amy!

Our work highlighted in @science.org by L. Bryan Ray!

www.science.org/doi/10.1126/...

Cell Feature Implicated in Cancer Forms Differently than Previously Thought A team with Professor Krishna Shrinivas discovered how a landmark found in the nucleus of cells in many organ systems forms through a different mechanism than the well-established view, a revelation t...

This project began at the @mblscience.bsky.social Physiology course 🌊 and grew into a FUN collaboration over many years across Northwestern and Duke - thanks all for the support and more coverage below!

📰 Coverage:
www.mccormick.northwestern.edu/news/article...

www.mbl.edu/news/physiol...

Extra curiosities 🔍
•⁠ ⁠Across tissues & species, stoichiometries of NONO/FUS are conserved, hinting at evolutionary tuning.
•⁠ ⁠Simulations by Mary Skillicorn in the lab also suggest important roles for co-transcriptional nucleation of paraspeckles for tuning paraspeckle size/number.

Another surprise: core & shell proteins don’t mix well (they’re immiscible, like oil & water).

Putting these observations together in simulations suggests 🖥️⚛️: competition for RNA + immiscibility naturally push proteins to form different layers, even if they individually like the same parts of RNA.

We combined in vitro assays of binding and condensation with bioinformatics to ask which parts of NEAT1 each protein preferred binding to.

Surprise: core proteins (FUS, NONO) actually prefer the same shell RNA regions as the shell protein TDP-43! Everyone crowds into the same RNA zones. 🌀