plant MT mechinterp

gistfile1.md

1. Treat simulators as the object of study, not the tool. Mech interp methodology applied to mechanistic simulators is almost completely unexplored, and it's a natural fit. Each simulator (CorticalSim, Cytosim, Tubulaton) makes different assumptions and produces different array behaviors. The mech interp move: don't just compare their outputs to data — intervene inside them. Ablate the collision rule and see what global statistic changes. Patch the dynamic instability module from Tubulaton into Cytosim and see if it rescues a phenotype. Treat each model as a circuit and ask which subcircuits are doing the work. This is exactly activation patching, just on a hand-built mechanistic model instead of a learned one. I don't think the plant MT community has framed it this way and the framing alone would be useful.
2. Learned surrogates of the simulators. Cytosim takes hours to run; you can't easily do MCMC over its parameters or run gradient-based optimization. Train a neural surrogate that maps (parameters, initial conditions) → (array statistics). Suddenly you can do Bayesian inference, sensitivity analysis, and design of experiments at the speed of inference rather than simulation. This is a standard move in scientific ML but the plant MT field hasn't really adopted it. If you build the surrogate well, you can then do mech interp on the surrogate — find which input dimensions matter, what internal representations the network learns about the array, whether it discovers the same structure the simulators were built to express. That's a genuinely novel research direction.
3. SAEs / probing on simulation trajectories. A long microtubule simulation produces a high-dimensional trajectory. Train a sparse autoencoder on the state at each timestep. What features does it find? Probably things like "local nematic order," "defect density," "regions of active zipping." These are exactly the order parameters the field cares about, but currently they're hand-designed. Letting unsupervised methods discover them and then comparing to the hand-designed ones is the kind of project that would interest both communities.
4. Inference from microscopy data. The biggest open methodological problem in the field: given a movie of MTs in a real cell, infer what mechanistic rules generated it. This is a simulation-based inference problem (SBI, ABC, neural posterior estimation). The ML community has tooling for this — sbi library, normalizing flows, etc. — that the plant MT community is largely not using. Bringing modern SBI to bear on the "which model explains this microscopy data" question is a clean, high-value contribution.
5. The AI safety / interpretability connection that's actually deep. Self-organization in MT arrays is mathematically the same family of problem as: how do emergent capabilities arise in trained networks? How do mesa-objectives form? You have a system of many local interactions producing global structure, and you want to predict and control the global structure from the local rules. The plant MT case has the enormous advantage that the local rules are known (it's a designed simulator), so it's a sandbox for developing interp methods that you then carry back to neural nets where local rules are emergent. I would actually pitch this to your AI safety colleagues. Cortical MTs are a beautiful "interpretability easy mode" testbed: nontrivial emergent organization, known generating rules, rich data, fast iteration. If your methods can't recover the mechanism here, they probably won't on a transformer either. How to actually engage with this community Concretely, this week (since you're at the workshop): Find the two or three people working on inference or model comparison and have coffee. The names to look for are anyone presenting on parameter fitting, anyone comparing simulators, anyone working with microscopy data quantitatively. Tell them you do mech interp, explain the analogy in one sentence, and ask what their hardest inference problem is. They will tell you, and it will probably be something you have tooling for. Don't lead with "I do AI safety." That phrase means different things to different people and you'll spend the conversation explaining instead of collaborating. Lead with "I work on understanding the internals of trained neural networks, and there's a methodological overlap with what you're doing." Then if it's relevant, mention safety. Offer something specific and small. Not "let's collaborate" — that goes nowhere. "I could spend a week training a surrogate on your Cytosim outputs and seeing if it recovers your order parameters" is concrete and useful. Or "send me 100 of your simulation trajectories and I'll run SAEs on them and tell you what features it finds." Tractable proof-of-concept first, then a real project. The right collaborator is probably a postdoc, not a PI. PIs have full plates and slow timelines. A postdoc who's stuck on an inference problem and would love a methods collaborator is your best bet for actually shipping something. The longer-term pitch If you wanted to make this a real research thread rather than a one-off contribution: there's a paper to be written titled something like "Mechanistic interpretability methods applied to biological self-organization" that takes one cortical MT simulator, treats it as the object of study, applies the full mech interp toolkit (activation patching, probing, SAEs, causal interventions), and shows which methods recover known mechanism and which don't. That paper would land well in both communities and would be a natural bridge piece for someone with your background. It would also be a real contribution to AI safety, not a detour from it — because validating interp methods on systems where ground truth is known is exactly what the field needs and is exactly what biological simulators offer. So: you have more to offer them than you might think, and they have a cleaner testbed than most things you're working with. Worth pushing on.

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MT Simulator Benchmark — Tier 1 & Tier 2 Comparison Report

Date: 2026-05-26 | Machine: nigel (RTX 4070 Ti) | Seed: 26058

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Engines

┌─────────────┬──────────────────┬──────────┬───────────────────────────────────┐
│ Engine │ Author │ Language │ Type │
├─────────────┼──────────────────┼──────────┼───────────────────────────────────┤
│ Cytosim │ François Nédélec │ C++ │ Physics, continuous forces │
├─────────────┼──────────────────┼──────────┼───────────────────────────────────┤
│ elastic-mt │ Tim │ Python │ Event-driven, zippering │
├─────────────┼──────────────────┼──────────┼───────────────────────────────────┤
│ corticalsim │ Eva │ C++ │ Cortical array, cylinder geometry │
└─────────────┴──────────────────┴──────────┴───────────────────────────────────┘

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Tier 1 — Isolated Dynamic Instability

Single MTs, no collisions. Tests whether engines agree on basic DI physics (grow/shrink/catastrophe/rescue) using BY-2 parameters.

Engines compared: Cytosim vs Tim DI wrapper (Tim's full elastic-mt can't run isolated MTs without refactoring — collision detection is baked into the main loop, so a standalone wrapper reimplementing his exact DI parameters was used.)

Results (200 MTs, seed 26058, t=1518s)

┌───────────────────────────┬─────────┬────────┬────────┬──────────┐
│ Statistic │ Cytosim │ Tim DI │ Spec │ Verdict │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Growth velocity (µm/s) │ 0.080 │ 0.080 │ 0.080 │ ✅ │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Shrink velocity (µm/s) │ 0.159 │ 0.159 │ 0.160 │ ✅ │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Mean cats/MT │ 1.7 │ 1.7 │ — │ ✅ │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Mean final length (µm) │ 64.9 │ 59.6 │ — │ ✅ │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Max final length (µm) │ 122 │ 121 │ — │ ✅ │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ Alive MTs at t=T │ 52/200 │ 63/200 │ — │ ⚠️ noise │
├───────────────────────────┼─────────┼────────┼────────┼──────────┤
│ KS p-value (length dist.) │ — │ — │ p=0.37 │ ✅ PASS │
└───────────────────────────┴─────────┴────────┴────────┴──────────┘

Verdict: PASS. Both engines agree on all four canonical DI statistics at p > 0.05.

One known artifact: Catastrophe frequency reads ~5× the input rate (0.017/s vs spec 0.003/s). This is a 1 Hz sampling artifact — fast sub-second catastrophe+rescue cycles are invisible to the logger. Both engines are equally affected so they
still agree with each other. Fix: sub-second sampling or event-log counting.

Alive MT count (52 vs 63): Within expected stochastic variance for n=1 seed. Not a bug.

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Tier 2 — Cortical Array with Collisions

MTs nucleated on a surface, MT-MT interactions enabled. Tests whether engines agree on how collisions shape the steady-state array.

Engines compared: Cytosim (3 variants) × corticalsim/Eva (3 variants).

Variants

┌──────────────────┬─────────┬───────────────────────────────────────────┐
│ Variant │ Engine │ Collision physics │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ cyto:nonsteric │ Cytosim │ No MT-MT interaction (reference) │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ cyto:crossing │ Cytosim │ Steric repulsion only │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ cyto:bundling │ Cytosim │ Steric attraction + repulsion │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ csim:nozipper │ Eva │ No zipper, MTs cross freely │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ csim:baseline │ Eva │ 40° zipper rule + 50% induced catastrophe │
├──────────────────┼─────────┼───────────────────────────────────────────┤
│ csim:alwayscatas │ Eva │ Always catastrophe on collision │
└──────────────────┴─────────┴───────────────────────────────────────────┘

▎ Geometry note: Not directly comparable in absolute numbers — Cytosim uses a 50×50 µm flat periodic patch (2,500 µm²), Eva's sim uses a cylinder (18,221 µm², ~7.3× larger) with ~36× higher nucleation flux. Qualitative trends are what matter
▎ here.

Steady-state results (last 50% of simulation time, t=900–1800s)

┌──────────────────┬──────────────────┬──────────────────┬───────────┐
│ Variant │ Density (µm/µm²) │ Mean length (µm) │ MTs alive │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ cyto:nonsteric │ 3.83 │ 29.8 │ 317 │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ cyto:crossing │ 2.51 │ 20.8 │ 301 │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ cyto:bundling │ 3.44 │ 26.1 │ 327 │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ csim:nozipper │ 20.80 │ 30.3 │ 12,383 │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ csim:baseline │ 0.86 │ 7.1 │ 2,203 │
├──────────────────┼──────────────────┼──────────────────┼───────────┤
│ csim:alwayscatas │ 0.54 │ 5.7 │ 1,718 │
└──────────────────┴──────────────────┴──────────────────┴───────────┘

Order parameter S2 (Eva's sim only — cylinder geometry required)

┌──────────────────┬───────┬──────────────────────────────────────────────────────────────────┐
│ Variant │ S2 │ Interpretation │
├──────────────────┼───────┼──────────────────────────────────────────────────────────────────┤
│ csim:nozipper │ 0.004 │ Isotropic, no preferred direction │
├──────────────────┼───────┼──────────────────────────────────────────────────────────────────┤
│ csim:baseline │ 0.101 │ Modest alignment from zipper rule │
├──────────────────┼───────┼──────────────────────────────────────────────────────────────────┤
│ csim:alwayscatas │ 0.159 │ Strongest alignment — fast turnover lets stable bundles dominate │
└──────────────────┴───────┴──────────────────────────────────────────────────────────────────┘

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Key Finding

Both engines agree qualitatively, disagree quantitatively by ~24×.

Within each engine, adding collision effects reduces density and mean length. The ordering is consistent. But the magnitude of the effect is completely different:

┌───────────┬─────────────────────────────────┬───────────────────┐
│ Engine │ No collisions → With collisions │ Density reduction │
├───────────┼─────────────────────────────────┼───────────────────┤
│ Cytosim │ 3.83 → 2.51 µm/µm² │ ~35% │
├───────────┼─────────────────────────────────┼───────────────────┤
│ Eva's sim │ 20.8 → 0.86 µm/µm² │ ~24× │
└───────────┴─────────────────────────────────┴───────────────────┘

Root cause: The engines implement different biology.

• Cytosim collision = mechanical force only (MTs push/pull, no state change)
• Eva's sim collision = 50% chance of catastrophe (MT dies on contact)

Collision-induced catastrophe is the dominant mechanism. Mechanical repulsion alone barely affects steady-state density. Any simulator without it cannot reproduce in-vivo BY-2 MT densities.

Cytosim oddity — bundling > crossing: Adding attraction (bundling) gives higher density than repulsion alone (crossing). Mechanism: co-alignment from attraction reduces crossing angles, so fewer effective collisions occur. This is actually
consistent with the biology — zippering reduces interference, it doesn't amplify it.

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Summary

┌────────┬────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────────────────────┐
│ Tier │ Question │ Answer │
├────────┼────────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤
│ Tier 1 │ Do engines agree on isolated DI? │ Yes — engines are interchangeable at the single-MT level │
├────────┼────────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤
│ Tier 2 │ Do engines agree on collision effects? │ Qualitatively yes, quantitatively no — 24× gap driven by catastrophe-on-contact physics │
└────────┴────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────────────────────┘

The benchmark is working as designed. Tier 1 establishes the floor (DI physics is shared). Tier 2 reveals that the dominant biological question is what happens when two MTs meet — and the two engines encode different answers to that question.

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Next Steps (v0.3)

1. Add collision-induced catastrophe to Cytosim — implement a custom Hand that triggers catastrophe on fiber contact; would close the engine gap and confirm the mechanism
2. Normalize geometry/nucleation — rerun with matched area and nucleation rate so absolute numbers are directly comparable
3. Run Tim's elastic-mt in tier-2 — adds a third engine with its own zippering model (already on nigel at ~/elastic-mt/)
4. Multi-seed (n=5) — Poisson CIs on density and MT count for all variants