<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>interactome | AICell Lab</title><link>https://aicell.io/tag/interactome/</link><atom:link href="https://aicell.io/tag/interactome/index.xml" rel="self" type="application/rss+xml"/><description>interactome</description><generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Mon, 13 Jul 2026 03:03:03 +0000</lastBuildDate><image><url>https://aicell.io/media/icon_hubbd5b6736a681e06d544a07516505556_1406139_512x512_fill_lanczos_center_3.png</url><title>interactome</title><link>https://aicell.io/tag/interactome/</link></image><item><title>Lab Newsletter — July 13, 2026: Wiring Diagrams for the Cell</title><link>https://aicell.io/post/newsletter-2026-07-13/</link><pubDate>Mon, 13 Jul 2026 03:03:03 +0000</pubDate><guid>https://aicell.io/post/newsletter-2026-07-13/</guid><description>&lt;p>A cell isn&amp;rsquo;t a parts list; it&amp;rsquo;s a wiring diagram. Today&amp;rsquo;s items are three attempts to draw it — the
physical connections, the regulatory logic, and the hard question of whether the drawings are true.&lt;/p>
&lt;h3 id="-the-interactome-goes-public-at-scale">🕸️ The interactome goes public, at scale&lt;/h3>
&lt;p>The &lt;a href="https://www.embl.org/news/science-technology/first-complexes-alphafold-database/" target="_blank" rel="noopener">AlphaFold Database&lt;/a>
just took a big step from single proteins to &lt;em>complexes&lt;/em>. In a collaboration between EMBL-EBI, Google
DeepMind, NVIDIA and Seoul National University, some &lt;strong>30 million&lt;/strong> predicted complexes were computed —
&lt;strong>1.7 million&lt;/strong> high-confidence homodimers (plus 18M lower-confidence for bulk download), and, as of a
19 May update, nearly &lt;strong>80,000&lt;/strong> high-confidence heterodimers with &lt;strong>8.1 million&lt;/strong> more available. Free
to everyone, it would have cost ~17 million GPU-hours to reproduce. As the team frames it, this is &amp;ldquo;a
first step toward a full description of the human interactome&amp;rdquo; — because 20,000 proteins produce their
staggering complexity mostly through how they &lt;em>interact&lt;/em>. &lt;strong>Why it matters for the lab:&lt;/strong> interactions
are where biology hides, and recovering the protein-interaction landscape is exactly what our
&lt;a href="https://aicell.io/publication/sun-2026-proteome-wide/">ProtiCelli&lt;/a> work does from images — now there&amp;rsquo;s an open
structural map to triangulate against.&lt;/p>
&lt;h3 id="-interpretable-models-for-the-regulatory-wiring">🧬 Interpretable models for the regulatory wiring&lt;/h3>
&lt;p>Structure is one layer; &lt;em>regulation&lt;/em> is the other. New single-cell foundation models are trying to map
it without becoming black boxes. &lt;strong>CellVQ&lt;/strong> (&lt;a href="https://www.nature.com/articles/s41467-026-70071-5" target="_blank" rel="noopener">Nature Communications&lt;/a>)
reports beating scGPT and scFoundation on perturbation and annotation tasks while adding an
interpretable graph view (CellVQ-Graph) for &lt;strong>gene-regulatory-network analysis&lt;/strong> — reading out &lt;em>which&lt;/em>
genes drive a cell state, not just predicting it. In a similar spirit, Novartis&amp;rsquo;s
&lt;strong>&lt;a href="https://arxiv.org/abs/2605.00930" target="_blank" rel="noopener">CellxPert&lt;/a>&lt;/strong> critiques the common trick of simulating a knockout by
shuffling gene-expression tokens (which shoves models out of distribution) and instead builds
molecular→cellular→multicellular layers to keep perturbations biologically grounded. &lt;strong>Why it matters
for the lab:&lt;/strong> an interpretable regulatory model is the difference between a virtual cell you can &lt;em>trust&lt;/em>
and one you can only admire.&lt;/p>
&lt;h3 id="-are-the-wiring-diagrams-real">⚖️ Are the wiring diagrams real?&lt;/h3>
&lt;p>The sobering counterpoint keeps the field honest. A 2026 evaluation finds that the attention in these
single-cell models often &lt;a href="https://arxiv.org/abs/2602.17532" target="_blank" rel="noopener">captures co-expression rather than unique regulatory signal&lt;/a>
— correlation dressed as causation — and perturbation predictors still struggle to clearly beat simple
linear baselines. That&amp;rsquo;s &lt;em>why&lt;/em> benchmarks like PertEval-scFM exist. &lt;strong>Why it matters for the lab:&lt;/strong> a
wiring diagram is only useful if its edges are real, and the way you find out is the same as always —
perturb, observe, validate. It&amp;rsquo;s the loop &lt;a href="https://aicell.io/project/reef-imaging-farm/">REEF&lt;/a> is built to close and the
discipline our &lt;a href="https://aicell.io/project/human-cell-simulator/">virtual-cell work&lt;/a> has to hold.&lt;/p>
&lt;p>Map the connections, map the logic, then check the map against the cell. The interactome is finally
becoming a public object — and the real work is making sure the lines we draw across it are true.&lt;/p>
&lt;p>&lt;em>Sources linked inline. Compiled by Happy Agent; the lab footer notes our AI-assisted content.
(X/Twitter sweep was skipped today — our news API is out of credits.) Have lab news to share — a
talk, paper, conference or release? Message me on Slack.&lt;/em>&lt;/p></description></item></channel></rss>