About

Why this exists

Useful conversions -- waste to fuel, pollutant to harmless, residue to feedstock -- usually need several organisms handing intermediates to each other. That search space is combinatorial and rarely explored beyond one or two steps. Syntropa explores it computationally, from a harmonized corpus of 15,717 genome-scale metabolic models, and publishes what it finds -- including the failures.

The approach

Each model in the corpus describes one organism's full metabolic network: what it can consume, what it secretes, and what chemical transformations connect them. Syntropa assembles these into a common namespace so a fermenter's outputs can be matched against a syntrophic partner's inputs across thousands of species at once.

Discovery runs three modes. The first asks a directed question: which pairs and triples, drawing from the whole corpus, can convert a specific feedstock into a specific product that neither can make alone? The second asks for breadth: which communities simultaneously produce the widest range of products, each member uniquely necessary? The third asks about closure: which consortia recycle the most of their shared flux internally, candidates for self-sustaining nutrient loops?

Each candidate that survives the screen is tested by flux balance analysis -- stoichiometry and mass balance as the arbiter, not heuristics. Promising candidates go to a thermodynamic gate: a per-reaction free-energy check that can expose obligate dependencies that plain stoichiometry misses entirely.

What it has found

The engine's predictions are hypotheses, not facts, until a wet lab confirms them. But computational re-derivation of known biology is one way to build confidence that the search is finding real signal. Starting from the full corpus with no prior knowledge of the answer, Syntropa independently recovered a textbook methanogenic syntrophy -- a fermenter handing hydrogen to a methanogen to make methane -- and found the pairing was statistically non-random: the confirmed consortia concentrated in the structurally-plausible partner class and were nearly absent among organisms that structurally cannot participate.

Discovery reports for worked examples -- including the methane case, syngas conversion, phenol degradation, and CO₂ reduction -- are published on the Reports page with the full funnel: the pool screened, the candidates found, the flux-balance verdict, and the caveats on what the computation cannot prove.

How it stays honest

Flux balance proves stoichiometric possibility, not biological reality. The thermodynamic gate is a necessary condition on free energy, not a kinetic proof. Gap-filled reconstructions -- the majority of the corpus -- carry transporter reactions that were added computationally, not measured. When a gate lacks the data to decide, it reports unknown rather than guessing. Known limitations are published alongside the results.

Nothing here is validated by computation alone. Every candidate is a hypothesis; the point of publishing the screen is to surface the ones worth testing in a lab.

Request a discovery run

Discovery runs are compute-heavy and require setting up the feedstock, target, and constraints carefully before the engine can produce an honest result. They are handled as requests rather than self-serve, so the setup can be reviewed and the caveats can be explained alongside the output.

To make a request, describe the conversion you care about: the feedstock you have and the product you need, along with any constraints -- temperature, oxygen availability, organisms to avoid, or a purity requirement that changes what counts as a hit.

A request form will be published here. Requests are currently handled by correspondence; the intake is manual while the access model is being finalized.