# Sabih

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Syed Sabih ur Rehman](https://cerebralvalley.ai/u/Sabih)
- **GitHub:** https://github.com/Sneakypeat/HumanCD4CoDEGNet
- **Demo video:** https://youtu.be/unAUdmUt2Zg
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/78

We mapped the causal control architecture of a human immune cell and used it to nominate
druggable, disease-relevant targets. From the Marson-lab genome-scale CRISPRi Perturb-seq atlas (22M primary human CD4+ T cells; rest / 8h / 48h activation) we built the causal
trans-regulatory network for every validated knockdown -> differentially-expressed gene is one edge, and characterised its architecture.

Finding: the network is hub-dominated and sparse-but-pleiotropic (the top 5% of regulators
drive ~78% of all trans-effects). The first causal confirmation of the Pritchard-lab 2026
topology prediction. Across activation, it is shape-invariant but identity-labile; the
concentration (Gini ~ 0.92) is pinned across all states while up to 59% of the top hubs are
replaced. The cell keeps the shape of its control while swapping out who is in control. Every
result survives power, edge-definition, knockdown-efficiency and detectability confounds, and replicates in a different cell type (K562).

Why it matters: the hubs that switch on specifically with activation are ~2x enriched for
monogenic-disease genes and are druggable (ZAP70, ITK, LCK, PTPRC, IL12RB2). The candidate state-specific control points for immune modulation. All results are reproduced from public data with code, a streaming notebook, and figures on GitHub.

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