Markov Biosciences
We built a world model of the cell—a single self-supervised model, trained on rankings of mRNA counts, that encodes:
- Where proteins sit in the cell — from nucleus to membrane to secreted
- Which proteins physically interact — direct substrates, scaffolds, complex partners
- How receptors signal — multi-hop kinase cascades recovered zero-shot
- Whether a target is druggable — and by which modality
- What transcription factors bind — including the sign of regulation and complex composition
State-of-the-art perturbation prediction. Monotonic scaling. No injected knowledge. No task-specific pretraining.
The model learned the cell because the cell is what generates the data. The right objective was all that was missing.
Paper: Generative ranking enables scalable pretraining on noisy biological multisets
GCTCAGAAGCGCCGAGAGCGCGGCCGGGACGGTTGGAGAAGAAGGCGGCTCCCGGAAGGGGGAGAGACAAACTGCCGTAACCTCTGCCGTTCAGGAACCCGGTTACTTATTTATTCGTTACCCTTTTTCTTCTTCCTCCCCCAAAAACCTTTTCCTTTTCCCTTCTTTTTTTTTCCTTTTTGGGAGCTGAAAAATTTCCGGTAAGGGAAAGAAGGGCTCCTTTCGCTCCTTATTTCCCCGCCTCCTTCCCTCCCCCACCTTCCCCTCCTCCGGCTTTTTCCTCCCAACTCGGGGAGGTCCTTCCCGGTGGCCGCCCTGACGAGGTCTGAGCACCTAGGCGGAGGCGGCGC
GCTCAGAAGCGCCGAGAGCGCGGCCGGGACGGTTGGAGAAGAAGGCGGCTCCCGGAAGGGGGAGAGACAAACTGCCGTAACCTCTGCCGTTCAGGAACCCGGTTACTTATTTATTCGTTACCCTTTTTCTTCTTCCTCCCCCAAAAACCTTTTCCTTTTCCCTTCTTTTTTTTTCCTTTTTGGGAGCTGAAAAATTTCCGGTAAGGGAAAGAAGGGCTCCTTTCGCTCCTTATTTCCCCGCCTCCTTCCCTCCCCCACCTTCCCCTCCTCCGGCTTTTTCCTCCCAACTCGGGGAGGTCCTTCCCGGTGGCCGCCCTGACGAGGTCTGAGCACCTAGGCGGAGGCGGCGC
TACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAACCGTAGACCAGATAGCATAGACATACCGTAGACCAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGATAGCATAGACATACCGTAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACA
TACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAACCGTAGACCAGATAGCATAGACATACCGTAGACCAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGATAGCATAGACATACCGTAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACA
TACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAACCGTAGACCAGATAGCATAGACATACCGTAGACCAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGATAGCATAGACATACCGTAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACA
TACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAACCGTAGACCAGATAGCATAGACATACCGTAGACCAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGATAGCATAGACATACCGTAATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACATACCGTAGACCAGATAGCATAGACA
Essays
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A Future History of Biomedical Progress
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