Fabian Fröhlich

Fabian Fröhlich

@frohlichlab.com

Dynamics of Living Systems (https://www.frohlichlab.com) group leader @thecrick.bsky.social Understanding signalling & cell state dynamics through mathematical modelling and machine learning.

1.1K followers413 following158 posts

Fabian Fröhlich reposted

Our work out today in @nature.com! We identified a previously unknown function for WEE1 in cell cycle control that could explain some of the inhibitor side-effects. We found WEE1 inhibits APC/C(Cdh1) to promote S-phase and cell cycle entry. Inhibit Wee1 , block or impair cell cycle entry.

Promising cancer drugs called Wee1 inhibitors have been limited by serious side effects. Researchers have discovered a previously unknown role for Wee1 that could explain why. The findings could help identify patients most likely to benefit & support development of safer, more targeted treatments.

Fabian Fröhlich reposted

Fabian Fröhlich reposted

🚨New preprint! Ligands at the same GPCR can produce wildly different signaling outputs. Why? We built a mutational scanning platform to measure ~10,000 µOR variants across 15 opioids, yielding ~400k measurements of how sequence and ligand chemistry set signaling. www.biorxiv.org/content/10.6... 1/10

Fabian Fröhlich reposted

It‘s been two years since the pre-print. In the meantime, we have been busy validating ConvexGating based gating strategies in the lab. Check out our publication at Nat Comm: www.nature.com/articles/s41...

nature.comConvexGating infers gating strategies from clusters in single cell cytometry data - Nature CommunicationsHere, the authors develop ConvexGating, an AI tool that learns interpretable, data-driven gating strategies for cell sorting. They show that when analyzing scRNA-seq data, it yields low-contamination ...
Maren Groeneveld (née Buettner)@marenbuettner.bsky.social

I'm proud to share our work to foster the use of machine learning and AI in immunology! 🚀 ConvexGating: Unlocking the Power of AI, Grounded in Human Intuition 🚀 Preprint: www.biorxiv.org/content/10.1... Github repo: github.com/buettnerlab/... Why, what and more details in the thread!

I got really excited about the paper and then it just ended 🫠

Richard Sever@richardsever.bsky.social

"Because manuscripts reflected real progress towards our field’s goals, our assessment systems began relying on them as primary metrics...which is obviously flawed...Generative AI has broken this already-flawed proxy completely" journals.plos.org/plosbiology/...

German lesson for today: „Dampflaberer" — someone whose AI hot takes are as intellectually lazy as the slop they're criticising. Some concerns are real and deserve better than recycled talking points. Running their Gschmarri past an AI assistant might help.

As habitual AI user for writing who regularly sees "100% human" scores on text with 80%+ LLM-written words, I suspect that: (i) reliability of AI detection is overestimated, (ii) writing of latest-gen models is under-appreciated, and (iii) "LLM-generated" is not as clear cut as many people think

Fabian Fröhlich reposted

Fabian Fröhlich reposted

"What are we even trying to do here?" Is what I have been asking many people in academia more widely lately. It is long past time for us, across *all* fields, to rethink the faux-meritocracy of numerical metrics (# of papers, citations, etc). We really should decide and reward what we truly value.

Daniel Litt@littmath.bsky.social

I've written an essay on how I think the mathematics profession should adapt to highly capable AI systems. It's hosted here on "Proofs and Prompts": proofsandprompts.com/2026/09/14/a... though you should also feel free to complain/comment on my website here: daniellitt.com/blog/2026/9/...

The general attitude of our community towards a technology that can [...] suggests that these operationalizations of our values are at best incomplete.

Daniel Litt@littmath.bsky.social

I've written an essay on how I think the mathematics profession should adapt to highly capable AI systems. It's hosted here on "Proofs and Prompts": proofsandprompts.com/2026/09/14/a... though you should also feel free to complain/comment on my website here: daniellitt.com/blog/2026/9/...

Fabian Fröhlich reposted

I'm really excited to be heading to the Crick to start the Quantitative Spatial Biology lab! We'll be doing all things #spatialbiology, mathematical modelling, and AI, to get a deep understanding of the biological mechanisms underlying how spatial structures form in health and disease #newPI 🔬🧬🧪🧮

We’re excited to announce five new group leaders will join the Crick in the coming months. Welcome to Katya Osipova, @joshuabull.bsky.social, @vchandra.bsky.social, @sami-c.bsky.social and James Cranley, whose specialties range from artificial intelligence to cell biology. https://bit.ly/3SPC6vJ

Fabian Fröhlich reposted

Fabian Fröhlich reposted

A massive update: At least ~15 companies~ are selling scientists antibodies using faked validation data. We've documented 18,000+ manipulated images on 17,000+ products sold by leading laboratory suppliers including Thermo Fisher, Abcam, Santa Cruz Biotechnology, Millipore Sigma and Bio-Techne. 1/🧵

Fabian Fröhlich reposted

Join us for our first online AI for Biological Discovery Seminar, where our Chief AI Scientist Mihaela van der Schaar will host Caroline Uhler to discuss her research at the intersection of machine learning, statistics and genomics. Learn more and book your free ticket: https://bit.ly/4xAvNLL

bit.lyAI in Biology Seminar | Professor Caroline UhlerA new AI for Biological Discovery Seminar Series, presented by the Francis Crick Institute.

Fabian Fröhlich reposted

Many don't see how much AI has changed recently. Coding agents aren't just chatbots. We can’t deal with the challenges of AI if we don’t understand it. Here's a post to provide some evidence (Claude doing small ML experiments) and form intuitions. davidpreichert.substack.com/p/if-you-hav... 🧵..

davidpreichert.substack.comIf you haven’t recently used Claude Code*, you might not understand where AI is atA report on a series of mini machine learning projects, executed with, and mostly by, Claude

Fabian Fröhlich reposted

ANNOUNCEMENT: All our sundials will be out of order this evening between about 6pm and 8pm, we apologise for any inconvenience this may cause. Normal service will resume tomorrow. In unrelated news, enjoy the eclipse later... Photos: Mike Selby, Annapurna Mellor, Chris Davies, John Millar

Fabian Fröhlich reposted

Mechanistic ODE models encode pathway topology but are blind to anything outside the pathway. Representation learning compresses data into low-dimensional embeddings but says nothing about mechanism. Deep Mechanistic Models (DMMs) are our attempt at both in one model www.biorxiv.org/content/10.6...

biorxiv.orgDeep Mechanistic Models reveal pathway-extrinsic drivers of mammary MAPK signalling heterogeneityCells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computa...
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Data-driven integration calls breast cancer subtype important; supply EGFR levels to the ODE instead and the subtype signal vanishes. Two explanations with same predictive power so the data cannot arbitrate between a molecular and a systems lens. DMMs surface the ambiguity and let us be the judge.

Mechanistic ODE models encode pathway topology but are blind to anything outside the pathway. Representation learning compresses data into low-dimensional embeddings but says nothing about mechanism. Deep Mechanistic Models (DMMs) are our attempt at both in one model www.biorxiv.org/content/10.6...

biorxiv.orgDeep Mechanistic Models reveal pathway-extrinsic drivers of mammary MAPK signalling heterogeneityCells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computa...
View full thread

The unexpected result: across 63 mammary cell lines, most heterogeneity sits at the inputs and outputs rather than in the core machinery. Baseline ERBB2 activation and ERK-to-RSK gain emerge as the major axes, likely set by endocytic, cytoskeletal and calcium programmes.

Per usual, mechanistic models stay informative where they fail: DMMs systematically underpredict phospho-ERK under MEK inhibition, which could reflect unmodelled crosstalk with AMPK signalling.

Mechanistic ODE models encode pathway topology but are blind to anything outside the pathway. Representation learning compresses data into low-dimensional embeddings but says nothing about mechanism. Deep Mechanistic Models (DMMs) are our attempt at both in one model www.biorxiv.org/content/10.6...

biorxiv.orgDeep Mechanistic Models reveal pathway-extrinsic drivers of mammary MAPK signalling heterogeneityCells sense and respond to their environment through signalling pathways, and the dynamics of these pathways shape cell fate even within genetically identical populations. Two largely separate computa...

DMMs couple semi-supervised representation learning to an ODE model of EGFR/MAPK signalling, end-to-end. The encoder proposes cell-line-specific parameters; the ODE model tests them against dynamic perturbation data. Representation and mechanism constrain each other rather than sitting side by side.