What I built / investigated. The project asks whether the rules of collective cell behavior can be recovered from snapshots alone. Cells respond to stimuli collectively — disordered colonies transition to aligned fates, and small localized inputs can redirect an entire population. Given only snapshots in time, can a model learn a rule that reproduces the observed population change and predicts a held-out snapshot — and crucially, does letting each cell's motion depend on the whole population (an interaction-aware rule) beat a cell-autonomous one? The working hypothesis: a model that has captured the real governing principles should be able to interpolate and extrapolate between snapshots of cell state. I built two things toward this. First, FateFlow, a model set up to interpolate/extrapolate between cell-fate snapshots, evaluated on scRNA-seq of hematopoiesis (Weinreb et al., Science 2020) across Day 2 → 4 → 6, where an intermediate time point is held out and reconstructed. Second — and this is the methodological contribution as a solo hackathon participant — I used Claude not as a single generalist, nor split by software function (reviewer / bug-fixer / planner), but split by domain expertise: four specialist personas — Cellular Automata, Optimal Transport, Differential Equations, and Reinforcement Learning — each grounded in a distinct family of prior work, made to research in parallel and to challenge and rebut one another. A "School of Athens for agents," inspired by the AI-Berkshire idea, giving one person a panel of strong, disagreeing discussants instead of one voice. What I found. Framing each prior-work family as an adversarial specialist produced sharper reasoning than a single model: the panel surfaced the key design axis — cell-autonomous vs. population-coupled dynamics — and let me weigh how CA, OT, ODE/SDE, and multi-agent RL each handle (or omit) cell–cell interaction, which is what motivated the FateFlow formulation and the held-out-snapshot evaluation protocol. The quantitative interpolation/extrapolation results on the held-out hematopoiesis time point are still being populated and are the immediate next deliverable. Why it matters. If we can learn a generative rule of collective cell behavior from snapshots, we can simulate how a population responds to stimuli and perturbation in silico, and test whether interaction-aware rules genuinely predict better than cell-autonomous ones — a question that sits under perturbation-response modeling broadly. The natural next step is to track individual cell trajectories and, using the learned interaction term, identify which cells are "influencer" cells (the sheep-dog cells) that redirect the population — moving from predicting snapshots to explaining who drives the collective decision.
Team
Placement
Participant
