An independent evaluation layer for assessing and ranking biological recovery routes.
Europe needs new ways to recover cobalt, nickel, rare earths and strategic materials from waste, water, residues and secondary streams. Biology is part of the answer — but the evidence is fragmented, unranked, and hard for engineers, investors and public agencies to trust. Bioseq grades biological recovery routes before capital is committed: we map algae–mineral interactions, score the evidence, rank viable pathways, and return a route assessment that tells industry what is worth testing, funding, or scaling.
Biological recovery routes for critical minerals exist — published, accumulating, and scattered across thousands of studies. What is missing is the layer that grades them: a structured, independent way to say which routes are viable, at what confidence, and at what cost to integrate.
Without a way to grade biological routes, they get excluded by default — or absorb years of fragmented literature review and exploratory wet-lab work that ends in no decision. The evidence exists; the means to act on it does not. The gap is not biological. It is informational.
Bioleaching already contributes around a fifth of global copper production. For cobalt, nickel and rare earths it remains under 1% — against demand rising 40–100% by 2030. Capital is arriving ahead of the layer that tells it which routes deserve it.
Bioseq is a structured knowledge graph of strain–mineral interactions, genomically characterised and evidence-classified. Every parameter carries an evidence tier and a design-use status. Every score is traceable to its source. The output is a route assessment an engineer can defend in a technical review — not a claim that "biology might work."
Bioseq does not mine, process, or produce. That independence is what lets the grading be trusted — the assessment answers to the evidence, not to a supplier's order book.
Every interaction is graded direct · replicated · analogous · inferred · simulated. The most conservative supporting evidence sets the tier. No claim outruns its proof.
Each parameter is classified for what an engineer may do with it in a stage-gate review — from concept mass balance through to exclude, not measured. Computed, not discretionary.
Every score carries a full provenance chain to its source studies and conditions. There are no black-box claims. The grading is auditable end to end.
A representative output of the architecture today — biological recovery of cobalt and rare earths from a cold, sulfate-rich acid mine drainage stream. The result is the kind of decision the layer exists to produce.
For this 12 °C freshwater stream, the acid-stable mesophilic green alga is the recommended first route — despite lower per-gram uptake than both alternatives. The highest-uptake and highest-REE strains are each outranked here by operating-envelope fit.
We gave Bioseq a real problem: a cold, acidic acid mine drainage stream, thick with competing iron and aluminium, and asked which algae could recover cobalt and rare earths from it. The sheet does what a literature search can't — it ranks three candidates, and the winner is the counterintuitive one. The two strongest organisms on paper both lose. Ulva, the highest cobalt absorber, is a saltwater species being asked to work in freshwater. Galdieria, the highest for rare earths, only functions at 40–55 °C — the stream is 12 °C. The route Bioseq recommends has lower uptake on paper, but it survives the actual conditions. That is the difference between what looks best and what works.
Each new feed stream, target element, and characterised strain extends the graph. The evaluation surface widens with every addition — from a focused algal start toward the full landscape of biological recovery.
Every real-world test result feeds back as a validation event, sharpening every downstream score. The layer grows more confident and cheaper to run over time — and the validated dataset beneath it grows harder to replicate with each event added.
Author of Investing in AI, Vol. 2: The Critical Material Stack (2026), on the intersection of artificial intelligence, strategic materials, and supply-chain resilience. Through ICMO, Martin works with private-equity and infrastructure investors on qualification risk in critical-materials processing assets — and brings the commercial architecture and market strategy behind Bioseq.
Professor of Semantic Databases at the Technical University of Berlin, with extensive research in knowledge-graph systems, ontology engineering, and large-scale data inference. Danh leads the ontology architecture and knowledge-graph build for Bioseq, with a research team spanning semantic-web technologies and biological data systems.
We are in active build and in early conversations with research institutions, grant funders, and industrial partners working on critical-minerals processing and biological route evaluation.
martin@bioseq.io