Lab Newsletter — June 23, 2026

Tue, 23 Jun 2026 06:00:00 +0000

A short digest of what’s caught our eye lately — from our own software stack to the wider world of AI for cell biology and bioimaging. Everything below links to its source.

BioEngine gets agent-readable. Our group has a new preprint out, BioEngine: scalable execution and adaptation of bioimage AI through agent-readable interfaces (bioRxiv, April 2026). It describes how BioEngine — built on Hypha — lets both people and AI agents run and adapt bioimage AI models through interfaces that agents can read and call directly. It’s a step toward the kind of autonomous, tool-using analysis pipelines we keep gesturing at in this newsletter.
A Model Zoo glow-up. AI4Life ran a week-long hackathon at EMBL Heidelberg to upgrade the BioImage Model Zoo. Highlights: a new internal model uploader (no more leaning on Zenodo) with authenticated contributions, CI moved to the collection-bioimage-io repo, and BioEngine now launchable on Slurm/Apptainer and other HPC backends. One nice detail — quantizing a 3D U-Net cut batch inference from 60 ms to 30 ms.
2026 DDLS postdoc decisions land. SciLifeLab and the Wallenberg DDLS Research School reach their funding decision on June 15 for the 2026 call: 22 fellowships (15 academic, 7 industrial), each 2 MSEK over two years, with employment starting October 1. Cell & Molecular Biology is one of the four strategic areas — squarely the neighbourhood we work in.
A single-cell model you can interrogate. Nature Communications published an interpretable single-cell foundation model trained on roughly 68 million cells with about 500 million parameters. The pitch is interpretability — being able to ask why the model places a cell in a given state — which matters a lot if these models are to inform real biology rather than just rank well on benchmarks.
Toward compositional foundation models. A Cell Systems perspective, From modality-specific to compositional foundation models for cell biology, argues for modular models that compose across modalities — chromatin accessibility, protein abundance, spatial transcriptomics, microscopy images, and text — into a shared picture of cellular behaviour, rather than training one monolith per data type.

Why it matters for the lab: agent-readable infrastructure (BioEngine/Hypha) and the BioImage Model Zoo are exactly the rails the field needs as foundation models for cells move from single-modality demos toward composable, interpretable systems — and the DDLS call is where the next people to build them get funded.

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Lab Newsletter — June 23, 2026