Cellarium

Cellarium is a tool for asking questions of the whole-cell model of E. coli from Macklin et al. (Science, 2020). That model tracks about 16,000 molecular species in a single cell, updated every second across a full cell cycle. It is very detailed, and checking whether it behaves correctly for a given perturbation normally takes an expert days. Cellarium divides the work between two agents. A Socratic Council (a proposer, a skeptic, and a judge) reads the question and writes down a specific prediction, including the result that would count as the model being wrong. It does this without seeing any of the simulation data. A second agent, Cellwright, then tests the prediction. It has 38 tools for reading real simulation output and searching PubMed, it reports the numbers it actually read and cites its sources, and it cannot start a new simulation without a person approving it. Three findings from real runs: - We asked whether knocking out a tRNA synthetase raises ppGpp, which is what the textbook stringent response predicts. The Council committed to "2 to 4 times higher" in advance. The simulations showed ppGpp falling about 90 percent (t = −27.85), so the prediction failed. Writing it down first is what makes this a real disagreement instead of an explanation added afterward. - Asked whether nitrate turns on the nitrate-reductase genes, the first answer was yes. Once we controlled for the fact that adding nitrate also removes oxygen, those genes looked like a general anaerobic response rather than a nitrate-specific one. - Deleting ribosomal RNA operons lowered ribosome content and growth rate together. That is the opposite of what Scott's efficiency law describes. A literature search during the run found work on ribosome-limited antibiotic susceptibility (Greulich and Scott, 2015), a regime that has not been reproduced in a whole-cell model. Why it matters: models like this are usually trusted or ignored as a whole. Cellarium exposes the prediction, the failure condition, and the source of each number, so a scientist can check the model against known biology and see where it disagrees. The same approach would apply to other large mechanistic simulations, and the runs we collect become a dataset others can reuse.

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