David Brückner's Avatar

David Brückner

@davidbrueckner.bsky.social

Assistant Professor @biozentrum.unibas.ch • Theoretical biophysics • Postdoc ISTAustria, PhD LMU Munich, MSc Cambridge University www.biozentrum.unibas.ch/brueckner

693 Followers  |  435 Following  |  83 Posts  |  Joined: 06.10.2023
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Posts by David Brückner (@davidbrueckner.bsky.social)

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Tissue phase transitions in development: more than just mechanics Summary: Tissue material phase transitions are classically thought to regulate tissue deformability. This Review emphasises their unexpected roles in directly influencing growth and patterning signall...

Excited to share our new review in @dev-journal.bsky.social on tissue phase transitions during development!

@karengrace12.bsky.social @nicolettapetridou.bsky.social

🔗 doi.org/10.1242/dev....

19.02.2026 13:31 — 👍 70    🔁 26    💬 2    📌 2

We summarised the many roles of material phase transitions in development (and they are not just mechanical 🤔)

19.02.2026 14:07 — 👍 30    🔁 9    💬 0    📌 0

Amazing and so well deserved! 🥳🥳

19.02.2026 17:13 — 👍 1    🔁 0    💬 1    📌 0

I’m thrilled to share that I will join @gustaveroussy.fr as a Group Leader!

Our objective: to understand how mechanochemical feedbacks regulate tumor dynamics and plasticity, with the overarching goal of bridging tissue mechanobiology and tumor pathophysiology using human organoids.

18.02.2026 15:17 — 👍 35    🔁 10    💬 9    📌 1

Best possible lab for a PhD / postdoc! Stay tuned for future openings and wonderful science.

18.02.2026 20:44 — 👍 12    🔁 3    💬 1    📌 0
From models to molecules: self-organized and instructed modes of developmental patterning - Nature Reviews Genetics In this Journal Club article, David Brückner discusses how seminal molecular genetic studies by Driever and Nüsslein-Volhard and Sick et al. demonstrated that both instructed (Wolpert model) and self-...

From models to molecules: self-organized and instructed modes of developmental patterning www.nature.com/articles/s41... - nice piece by @davidbrueckner.bsky.social

19.02.2026 07:38 — 👍 21    🔁 7    💬 0    📌 0
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Endogenous FGFs drive ERK-dependent cell fate patterning in 2D human gastruloids Summary: In a 2D gastruloid model for human gastrulation, FGF4 and FGF17 signal through basolateral receptors to induce primitive streak-like cells and derivatives, potentially restricted by FGF8.

After being reviewed as a preprint in Elife our paper about endogenous FGF signaling in 2D human gastruloids is now published in Development:
journals.biologists.com/dev/article-...

09.02.2026 15:56 — 👍 8    🔁 2    💬 0    📌 0
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Anti-resonance in developmental signaling regulates cell fate decisions Human cells decode dynamics Wnt signals using an anti-resonant filter that suppresses intermediate-frequency inputs and is capable of redirecting developmental fate outcomes, including germ-layer spec...

In a really fun collaboration with Max Wilson’s lab at UCSB, we’ve developed a simple model that can capture the non-monotonic expression of Wnt target genes with respect to frequency in periodic (optogenetic) Wnt pulses: elifesciences.org/articles/107.... Great work Sam, Olivier, Naomi and Ryan :)

30.01.2026 18:32 — 👍 11    🔁 3    💬 0    📌 0
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New preprint! We show how mesoscopic nonequilibrium fluctuations in active gels emerge from the breaking of detailed balance at the molecular scale. Warning: Long technical paper ahead! Enjoy! @mpipks.bsky.social @ub.edu @ubics.bsky.social @icreacommunity.bsky.social

arxiv.org/abs/2601.20483

30.01.2026 18:08 — 👍 40    🔁 15    💬 1    📌 0
ESFS: A Noise-Resilient Framework for Feature Selection and Marker Gene Discovery in Single-Cell Transcriptomics Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity, but extracting meaningful signals remains challenging due to technical noise, batch effects, and the limitations of current feature selection methods. We present Entropy Sorting Feature Selection (ESFS), a modular, user-friendly framework that captures multivariate gene expression relationships without imputation or denoising via latent spaces. Across diverse datasets, ESFS improves interpretability and reveals biology missed by standard workflows: identifying coherent developmental programs in eight independent human embryo datasets without batch integration; resolving spatial gene expression in mouse colon obscured by conventional analyses; distinguishing shared and tumour-specific microenvironments in glioblastoma; and disambiguating spatial, temporal, and neurogenic programs in the developing mouse neural tube. By operating in gene expression space, ESFS produces interpretable, biologically meaningful outputs while reducing artefacts introduced by feature extraction. These results position ESFS as a powerful means to uncover relevant molecular signatures in noisy, high-dimensional transcriptomics data. ### Competing Interest Statement The authors have declared no competing interest. Cancer Research UK, CC001051 Medical Research Council, https://ror.org/03x94j517, CC001051 Wellcome Trust, CC001051 Wellcome Trust, 220379/D/20/Z European Molecular Biology Organization, 792-2021 UK Research and Innovation, EP/X031225/1

Our latest: A gene selection method for single-cell RNA-seq that identifies developmental & spatial patterns missed by other analysis pipelines

ESFS: A Noise-Resilient Framework for Feature Selection and Marker Gene Discovery in Single-Cell Transcriptomics | bioRxiv www.biorxiv.org/content/10.6...

28.01.2026 05:59 — 👍 29    🔁 9    💬 0    📌 0

Our review on Marr's levels in embryonic development is now out in @prxlife.bsky.social !

journals.aps.org/prxlife/abst...

23.01.2026 13:12 — 👍 11    🔁 4    💬 0    📌 0
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Visiting @hhmijanelia.bsky.social just before the winter storm hits! So much awesome science here!

23.01.2026 13:11 — 👍 1    🔁 0    💬 0    📌 0
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Prof. Fiona Doetsch has been awarded the 2026 Louis-Jeantet Prize for Medicine!
Her discoveries reveal how neural stem cells support lifelong brain plasticity and repair. 🧠
Read more: www.biozentrum.unibas.ch/news/detail/...

15.01.2026 10:09 — 👍 40    🔁 9    💬 3    📌 3

Welcome to Basel @robinjournot.bsky.social !! :)

14.01.2026 16:35 — 👍 1    🔁 0    💬 0    📌 0
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My time at @institutcurie.bsky.social is done! Over the past months, I completed my PhD, published my first first-author paper in DevCell (www.cell.com/developmenta..., and submitted my first last-author manuscript to bioRxiv (www.biorxiv.org/content/10.1...).

14.01.2026 15:47 — 👍 6    🔁 2    💬 1    📌 0
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Feb 1 deadline approaching for our @kitp-ucsb.bsky.social QBio summer course on Physical Principles of Morphogenesis in Plants and Animals. @streichan.bsky.social
@maurazimmermann.bsky.social @maizel-lab.org @yusuke-mori.bsky.social @akankshi.bsky.social @nicolettapetridou.bsky.social

13.01.2026 23:36 — 👍 25    🔁 20    💬 1    📌 1
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Biophysics of organoids In this review, Weichselberger, Moore et al. discuss how physics-based approaches illuminate organoid development and homeostasis by integrating mechanical, chemical, and informational processes. They...

Interested in the biophysics of organoids? We just published a review in Dev Cell—take a look! dlvr.it/TPyTb8 #Organoids #Biophysics

06.01.2026 10:27 — 👍 81    🔁 26    💬 0    📌 2
From models to molecules: self-organized and instructed modes of developmental patterning - Nature Reviews Genetics In this Journal Club article, David Brückner discusses how seminal molecular genetic studies by Driever and Nüsslein-Volhard and Sick et al. demonstrated that both instructed (Wolpert model) and self-...

Self-organized or instructed?

My @natrevgenet.nature.com Journal Club traces how classic papers revealed two modes of developmental patterning & why the debate between Turing and Wolpert is as timely as ever for organoid engineering today

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

06.01.2026 10:02 — 👍 16    🔁 6    💬 0    📌 0
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And here is the PDF of the 4th course @college-de-france.fr. You will find the justification and presentation of geometric representation of cell decisions, a parsimonious mathematical model for signalling and cell state transitions

tinyurl.com/yc7u296j

16.12.2025 09:57 — 👍 31    🔁 9    💬 1    📌 0
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Here is the link to download the PDF of my course#5 on Biological computation. I present & discuss Self-tuning, Adaptation and Learning in biological (non-neuronal) systems, in particular during embryonic development.
This course contains various personal ideas/proposals.
tinyurl.com/hcpwsbtm

03.01.2026 09:47 — 👍 32    🔁 11    💬 0    📌 0
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Qu’est-ce que l’information biologique (suite) ? (5) - Thomas Lecuit (2025-2026) Enseignement 2025-2026 : Qu’est-ce que l’information biologique (suite) ? Cours du 18 décembre 2025 : Apprentissage non neuronal dans un système vivant Professeur : Thomas Lecuit Chaire Dynamiques du vivant Retrouvez les enregistrements audios et vidéos du cycle : https://www.college-de-france.fr/fr/agenda/cours/qu-est-ce-que-information-biologique Tous les enseignements du Pr Thomas Lecuit : https://www.college-de-france.fr/chaire/thomas-lecuit-dynamiques-du-vivant-chaire-statutaire https://www.youtube.com/playlist?list=PLOj9pZ2YNGZ84uMY6DhPnqpL1rq5qNAVJ Le Collège de France est une institution de recherche fondamentale dans tous les domaines de la connaissance et un lieu de diffusion du « savoir en train de se faire » ouvert à tous. Les cours, séminaires, colloques sont enregistrés puis mis à disposition du public sur le site internet du Collège de France. Découvrez toutes les ressources du Collège de France : https://www.college-de-france.fr Soutenir le Collège de France : https://www.fondation-cdf.fr/faire-un-don Suivez-nous sur : Threads : https://www.threads.net/@collegedefrance Bluesky : https://bsky.app/profile/college-de-france.fr Facebook : https://www.facebook.com/College.de.France Instagram : https://www.instagram.com/collegedefrance LinkedIn : https://fr.linkedin.com/company/coll%C3%A8gedefrance

Please find below the link to watch the VIDEO of my course#5 @college-de-france.fr on Biological computation. I present and discuss Self-tuning, Adaptation and Learning in biological (non/neuronal) systems, in particular during embryonic development.
Enjoy!

tinyurl.com/4pv95an9

03.01.2026 09:44 — 👍 7    🔁 2    💬 0    📌 0

Super nice to see this awesome set of lectures online! A really timely perspective on biology through the lens of information, which inspires much of our own research as well.

05.01.2026 08:34 — 👍 10    🔁 2    💬 0    📌 0
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Munro, E., Nance, J., & Priess, J. R. (2004). Cortical flows powered by asymmetrical contraction transport PAR proteins to establish and maintain anterior-posterior polarity in the early C. elegans embryo. Developmental cell, #EpithelialMechanics www.sciencedirect.com/science/arti...

05.01.2026 08:00 — 👍 6    🔁 1    💬 0    📌 0

This was a fun theory collaboration led by amazing postdoc Alex Zhang, in collab with Gasper Tkacik (ISTA)

05.01.2026 08:00 — 👍 0    🔁 0    💬 0    📌 0
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Marr's three levels for embryonic development: information, dynamical systems, gene networks Developmental patterning comprises processes that range from purely instructed, where external signals specify cell fates, to fully self-organized, where spatial patterns emerge autonomously through…

Throughout this work, we found it really helpful to organize our thinking along 'Marr's three levels of analysis':
1. the computational problem
2. the algorithmic solution
3. biophysical implementation

Check out our review laying out this conceptual framework here: buff.ly/NE4JEOA

05.01.2026 08:00 — 👍 15    🔁 7    💬 1    📌 0
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Remarkably, these optimal algorithms predicted by our theory are almost perfectly matched by minimalistic mechanistic models, including averaging through short-range diffusion and divisive normalization through a fast diffusing 'normalizer' species

05.01.2026 08:00 — 👍 0    🔁 0    💬 1    📌 0

Spatially uncorrelated noise: it's best to spatially average the input you get from the neighbors

Long-range correlated noise: it's best to perform a divisive normalization - normalize your measured value by that of your neighbors

Complex things happen in between these limits...

05.01.2026 08:00 — 👍 0    🔁 0    💬 1    📌 0
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Here, we ask how CI can be useful to single cell decision making, by predicting the additional PI gained when cells interact with their neighbors.

Depending on the spatial correlations of the input pattern, completely different strategies to communication are required to gain information

05.01.2026 08:00 — 👍 1    🔁 0    💬 1    📌 0
PNAS Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly…

In our recent paper on self-organized patterning, we demonstrated how the total information of a pattern is composed of positional (PI) and correlational information (CI).

While PI measures local accuracy, CI is determined by the spatial correlations of a pattern.

www.pnas.org/doi/10.1073/...

05.01.2026 08:00 — 👍 0    🔁 0    💬 1    📌 0
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How can cells use information from neighboring cells to improve the spatial precision of morphogen patterns? 🤔

We show that cells can gain positional information by "talking" to their neighbors - how much depends critically on spatial correlations of the patterns.

buff.ly/w56OUJT

05.01.2026 08:00 — 👍 64    🔁 20    💬 1    📌 0