Adlung Lab

@adlunglab.com

Data-driven & knowledge-based. Rooted in Hamburg, Germany. Systems biology / Immunology. Fat + Gut <3 Account handled by π @LorenzAdlung.com www.adlunglab.com

Intriguing @nature.com paper from @yaleschoolofmed.bsky.social on commensal-derived Acetylcholine, which educates mucosal immunity. Among many other cool things, they monocolonize germ-free mice with more than 100 individual bacterial strains... www.nature.com/articles/s41... 🧪

Commensal-derived acetylcholine enhances mucosal immune education - Nature

A diet–microbiome–host axis strengthens mucosal immune defences and reinforces host–microbiota mutualism.

nature.com

"This work identifies EP2 signaling in tissue-resident macrophages as a central regulator of organ-wide aging through its control of senescent neutrophil clearance, reframing aging as a failure of active cellular clearance rather than passive degeneration." www.science.org/doi/10.1126/... 🧪

Restored clearance of senescent neutrophils by tissue-resident macrophages limits organ aging

Aging disrupts tissue homeostasis across organ systems. Here, we identify tissue-resident macrophages (TRMs) as central coordinators of age-related organ decline through impaired clearance of senescen...

science.org

Very happy to see this out - made a humble contribution analyzing some sequencing data to see our hypothesis confirmed in vitro and in a human cohort. 🧪

Journal of Experimental Medicine@jem.org · last mo.

Bile acid retention in efferocytic #macrophages shapes their inflammatory status during #cholangitis. New study from Amirah Al Jawazneh, Imke Liebold, Stephanie Leyk, Lidia Bosurgi (Universitätsklinikum Hamburg Eppendorf) et al.: rupress.org/jem/article/... @pjsaez.bsky.social #inflammation

Uncertainty-quantification in neural ODEs: www.nature.com/artic... 🧪

A novel approach to quantify out-of-distribution uncertainty in Neural and Universal Differential Equations | npj Systems Biology and Applications

Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In systems biology, dynamical systems provide a foundation for modeling and predicting the intricate behaviors of biological systems. Recent advances in data-driven approaches, such as Neural Ordinary Differential Equations (NODEs) and Universal Differential Equations (UDEs), have enabled the development of models that are either fully or partially data-driven. Integrating data-driven components into dynamical systems amplifies the challenge of generalization beyond training data, highlighting the need for robust methods to quantify uncertainty in out-of-distribution (OOD) scenarios—i.e., conditions not encountered during training. In this work, we investigate the reliability of uncertainty quantification (UQ) based on ensembles of models in the reconstruction of dynamical systems. We show that standard ensembles (i.e., models trained independently with different random initializations) risk producing overconfident predictions in previously unseen scenarios, as the models in the ensemble tend to exhibit similar behaviors. To address this issue, we propose a novel ensemble construction method for NODEs and UDEs that fosters diversity in the reconstructed vector field across models within specific regions of the state space, while maintaining explicit control over the fit on the training set. We first evaluate our method on numerical test cases derived from three models commonly used as benchmarks for data-driven reconstruction of dynamical systems: the Lotka–Volterra model, the damped oscillator, and the Lorenz system. We then apply the method to a biologically motivated model of cell apoptosis, considering more realistic conditions such as partial observability of the system outputs and noise in the training dataset. Overall, our results show that the proposed method improves the reliability of UQ in previously unseen scenarios compared with standard ensembles, especially where the latter exhibit overconfidence.

nature.com

🚀 What an amazing #SBMC2026! From inspiring welcome addresses, to the MTZ Award ceremony, scientific sessions, panel discussion, poster sessions, and Poster Awards — thank you to everyone who made these three days so special. Wonderful science, great discussions, and even better networking! 📸✨

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😦 I gave this to DeepL: Our core international scientific collaborations span Israel, Poland, Sweden, the UK, Switzerland, Austria, France and the US. What came out: Our core international scientific collaborations span Israel, Poland, Sweden, the UK, Switzerland, Austria and France.