# SP

- **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:** [PARAMESWAR SAHU](https://cerebralvalley.ai/u/s-parameswar)
- **GitHub:** https://github.com/Sandbox-commission/Hackathon-claude-built-with-science-The-Tempo-of-Transformation
- **Demo video:** https://github.com/Sandbox-commission/Hackathon-claude-built-with-science-The-Tempo-of-Transformation/blob/main/video/tempo_of_transformation_demo.mp4
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/300

1. Project name

||The Tempo of Transformation — Post-Translational Timing Gates How Driver Mutations Spread Through the Colorectal Cancer Network||

Short variant : ||Tempo of Transformation: PTM Timing Gates CRC Damage||


2. Tagline 

When a PTM is modelled as timing rather than a switch, it changes how far a driver mutation's damage travels through the cancer network.


3. What it does

The project tests a modelling choice that cancer systems biology usually makes without noticing: a driver mutation and a post-translational modification (PTM) are both encoded the same way — a node forced ON or OFF. In a published, independently re-verified 96-node Boolean model of cancer signalling (Fumiã & Martins 2013), we encode the *same* β-catenin destruction-complex lesion two ways — as a node clamp (the standard approach) and as a PTM-edge deletion on a separated fast timescale (the biologically faithful approach) — and measure how far a seeded perturbation spreads as driver mutations accumulate in the canonical colorectal order.

Under synchronous updating the two encodings are indistinguishable — a documented negative control (Δ(η-slope) = −0.0020 per driver). Once fast (phospho/degron) and slow (transcriptional) processes run on separate clocks, the PTM-edge encoding propagates the perturbation substantially further: Δ(AUC) = +0.309 (95% CI [0.3058, 0.3115], p < 0.0001) at the APC+KRAS stage. A pre-registered timescale-knockout control (τ=1) removes 98.2% of the effect — proof that the phenomenon *is* the timescale separation, not the lesion itself.

We report the finding with its limits intact. The effect exceeds a degree- and sign-preserving topology null (p = 0.042) but sits at the 94th percentile of a random-edge null (p = 0.058), so it is a property of a *class* of coupling-preserving edges rather than the β-catenin degron uniquely; the network stays firmly ordered (η < 1) throughout; and external tests in static CPTAC phosphoproteomics and DepMap dependencies are honest nulls or weak, underpowered probes, not validation. The contribution is a ||novel multi-timescale encoding method plus an honest attempt to test it|| — not a claim to have found a colorectal-cancer drug target.


4. Inspiration

The study began as a "butterfly effect" question: does a small change early in tumorigenesis ripple out to reshape everything downstream? We retired that framing deliberately, because in this setting it is a category error. A butterfly (Lyapunov) exponent describes sensitive dependence on *initial conditions* in a chaotic system. A mutation is not a change of initial condition — it is a change of the *rule*, a structural (bifurcation) perturbation of the network itself. And eukaryotic regulatory networks are not chaotic: they sit in an ordered or near-critical regime (Kauffman 1969; Shmulevich, Kauffman & Aldana 2005).

The honest discrete-dynamics analogue of "a small change spreading" is therefore ||Boolean damage-spreading|| and the ||Derrida annealed map|| (Derrida & Pomeau 1986), applied to a network whose *rules* are edited by the mutation and the PTM. Turning a vague metaphor into a rigorous, measurable estimand is what made the study both tractable and novel — and it sharpened the real question: not *whether* damage spreads, but whether the post-translational layer changes the *pace* at which it spreads.


5. How we built it

A six-stage pipeline, entirely in the Claude Science workbench:

1. ||Verified model sourcing (not reconstructed).|| The Fumiã & Martins (2013) update rules live only in the paper's supplementary Text S1, so we sourced them two independent ways — the EuropePMC supplementary ZIP and an independent GitHub sympy re-encoding — confirmed the network closes exactly (96 nodes, 249 edges, zero dangling references), and validated the simulator against the paper's three published normal-cell attractors (proliferative period-7 cycle, quiescent fixed point, apoptotic fixed point).
2. ||LOF/GOF driver encoding.|| Drivers edit the rules, not generic toggles: APC (loss-of-function, node clamped 0), KRAS (gain-of-function, Ras clamped 1 and decoupled from EGFR/RTK to respect anti-EGFR resistance biology), TP53 (LOF), SMAD4 (LOF) — accumulated in Fearon–Vogelstein order.
3. ||PTM-edge layer (the crux).|| The β-catenin degron lesion is encoded as deletion of the single inhibitory edge GSK-3/APC ⊣ β-catenin, which keeps β-catenin coupled to p53 (σ_βcat = sgn(−p53 + 1)), and contrasted against the standard node clamp that severs that coupling.
4. ||Multi-timescale simulator.|| A custom pure-numpy engine partitions the network into 33 fast (phospho/signalling/degron) and 57 slow (transcriptional/cyclin) nodes plus 6 inputs; slow nodes update on a separated clock (τ_slow = 50); the τ=1 knockout collapses the separation as the pre-registered mechanism test.
5. ||Modal sweep.|| The full damage-spreading sweep — primary estimand + three null models + synchronous negative control — ran on Modal (BYOC, 32-core CPU container, pure-numpy worker) in ~22 min: M = 500 null/bootstrap realizations, 15,000 samples per timescale cell, 2,000 bootstrap draws.
6. ||CPTAC / DepMap validation.|| Cross-validated against CPTAC colon phosphoproteomics (cBioPortal `coad_cptac_2019`, phospho matrix 97 × 106) and probed against DepMap 23Q2 CRC dependencies; CrossRef verified every citation; all statistics (empirical two-sided p-values, bootstrap CIs, Benjamini–Hochberg FDR) were pre-registered before the sweep.


6. Challenges we ran into

- ||The synchronous-null → multi-timescale-signal reframe.|| Our first comparison of the two PTM encodings under synchronous updating found nothing (Δ(η-slope) = −0.0020 per driver, negligible). Instead of abandoning the hypothesis, we recognised that a synchronous clock is exactly what erases a timescale effect — and made the timescale separation the treatment. The "null" became the documented negative control, and the effect appeared (Δ(AUC) = +0.309) once fast and slow clocks were separated.
- ||The count-vs-combination scoring bug we caught and fixed.|| The DepMap external score initially keyed criticality on raw *driver count*, which mis-scored APC+TP53 lines as near-critical and inflated an apparent signal to p = 0.038 with 4/7 effectors surviving FDR. But the sweep showed the effect requires *both* the APC-driven degron lesion *and* an intact p53 seed (it collapses once TP53 loss clamps p53). Scoring lines mechanism-faithfully (APC-mutant AND TP53-wild-type) dropped the result to p = 0.20 — the honest number we report, superseding our own inflated version.
- ||Data-access chains.|| The base model existed only as paper supplementary text; CPTAC came through the cBioPortal REST API; DepMap through figshare. We also caught and corrected a data-hygiene error — an early "CTNNB1 S45F in 4 patients" assumption traced to a *different* dataset, not this CPTAC cohort (where the Wnt degron is disabled overwhelmingly through APC loss, 74.5%).
- ||Ordered, not critical.|| The network stays firmly in the ordered regime (η < 1 at every driver load), not at the edge of chaos. This was a pre-registered contingency (Park et al. 2023, PRX Life), so rather than force "criticality" language we reframed the result as a shift of *position on the order↔criticality axis* — precisely at the intermediate +APC/+KRAS loads where the degron lesion is active.


7. Accomplishments we're proud of

- ||Cleared a real novelty gate.|| We gated the idea against actual prior art — Chu, Lee & Cho (2015), criticality-versus-driver-load in CRC, which we cite as the platform, and the Cho-lab attractor-reversion program, whose vocabulary and target-nomination framing we deliberately avoided — and pre-registered a narrow surviving wedge before computing the headline result.
- ||A clean mechanism proof.|| The τ=1 timescale knockout removes 98.2% of the effect with the *same* lesion and the *same* seed — direct evidence that the phenomenon is the timing, not the mutation.
- ||Honest null reporting.|| Every null model (topology p = 0.042; PTM-location p = 0.058; driver-order rank 8/24), the ordered-regime reframe, the CPTAC identifiability nulls, and the weak DepMap probe are reported whether or not they favour the hypothesis — including superseding our own tempting-but-wrong count-based DepMap result.
- ||Full reproducibility.|| Verified model provenance (dual-sourced rules + attractor validation), a locked pre-registration, a pure-numpy simulator, a reproducible Modal sweep, and every figure and dataset saved as a project artifact.


8. What we learned

- ||Timing can be the mechanism, not a detail.|| Modelling a PTM as a fast-timescale edge rewiring rather than a node switch changes a mutation's reach by a large, highly significant margin (Δ(AUC) = +0.309, p < 0.0001) that the standard node encoding literally cannot see. For a perturbation spreading through a network, *when* a coupling acts can matter as much as *whether* it acts.
- ||Pre-registration and null models earn their keep.|| Locking the estimand, the mechanism control, and three null models before the sweep is what let us separate a genuine multi-timescale phenomenon from degree/wiring artefacts — and what kept us honest when the DepMap scoring bug produced a seductive significant result.
- ||Static data has hard identifiability limits.|| Δ(AUC) is a statement about *dynamics over time*. A single-timepoint bulk snapshot (CPTAC) can replicate a *direct* mutation→PTM link (TP53-R273 → Ser315, p = 0.018) but cannot resolve a *dynamic* multi-timescale coupling (Tests 2 and 3 null, p = 0.83 and p = 0.70). The data type bounds the claim — a limit we pre-registered and confirmed rather than papered over.


9. What's next

- ||Time-resolved perturbation phosphoproteomics.|| The one experiment that could test Δ(AUC) directly — a dynamic estimand — is time-course phospho data following a defined perturbation, which does not exist for this cohort. That is the decisive next measurement.
- ||Multi-model generalisation.|| The null-model class argument implies the effect is a property of coupling-preserving edges in *any* multi-timescale network, not the Fumiã–Martins model or the β-catenin degron uniquely. Sweeping additional base models and PTM families would test that argument head-on.
- ||The tractable-kinase PTM-edge-writer angle.|| Because the effect is carried by a *class* of coupling-preserving edges, the actionable question becomes which druggable kinases and enzymes *write* those edges — reframing "which node to hit" as "which timing to retune." This is a hypothesis-generation direction, not a target claim.

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