Astromech, a startup founded by entrepreneur Ben Lamm and Harvard geneticist George Church, has raised $20 million to develop AI models that predict how biological systems will change over time by analyzing 3.8 billion years of evolutionary history.
Unlike existing biotech approaches that study organisms as they are now, Astromech forecasts future biological trajectories—such as disease progression and drug resistance—using a dual-engine model that identifies patterns across species and reverse-engineers biological systems backward through history before projecting them forward.
The company is using human longevity as its first proving ground, having already mapped 46 longevity-associated genes to understand aging mechanisms by studying long-lived species like the bowhead whale.
What happened
Astromech, an AI startup co-founded by Ben Lamm and geneticist George Church, raised $20 million in funding led by biotechnology investor Bob Nelsen, bringing total funding to $60 million and valuation to $3.8 billion. The company develops predictive models of biology that use genomic, evolutionary, and functional data to forecast how organisms and diseases will change over time.
Why it matters
Rather than analyzing organisms as they exist today, Astromech aims to predict future biological trajectories—including genetic bottlenecks, disease progression, drug resistance, and species responses to environmental change. The company is focusing on human longevity as a proving ground, having mapped 46 longevity-associated genes to understand aging mechanisms; this approach may eventually support precision longevity research by learning from long-lived species such as the bowhead whale, which typically lives for more than 200 years.
What to watch
Astromech plans to expand research teams, scale its comparative genomic infrastructure, and study more species to gather more evolutionary data. Once complete, the company aims to launch pilot projects with health and biosecurity partners to apply its vulnerability forecasting models to real-world challenges.
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Astromech was spun out of Colossal Biosciences Inc., a genetic engineering firm working on de-extinction of animals including the woolly mammoth and Tasmanian tiger. That heritage shaped the company's core asset: 3.8 billion years of evolutionary history as a primary training signal. The dual-engine architecture—one using deep learning to spot patterns across species, the other to reverse-engineer and forward-project biological systems—reflects a conviction that evolution itself is the richest source of data for predicting biological change.
Co-founder Church's emphasis on ancestral regulatory states rather than just proteins addresses a technical limitation in existing comparative genomics: most genetic variations affecting complex traits like morphology and longevity are regulatory, not coding. By reconstructing ancestral regulatory states, Astromech claims to recover explanatory power that protein-only comparisons miss. The focus on longevity—studying species like the Asian elephant's cancer-suppression mechanism and the bowhead whale's extreme lifespan—is framed as a near-term validation target, not a near-term product. Lamm's weather-forecasting analogy positions the startup as solving an inference problem (predict future states from historical data and current conditions) rather than a protein-design problem, which may appeal to investors and partners seeking forecasting rather than engineering.
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