We read what long-lived species kept. Then we write the mRNA to match it.
caVos uses AI to find genetic variants conserved across long-lived species — and designs mRNA sequences that safely upregulate the longevity proteins they encode. Targeting neurodegeneration at the RNA level.
Longevity proteins already exist. We decode where to find them.
Certain species defy what we expect from biology. Naked mole rats suppress cancer. Bowhead whales outlive the people who study them. Greenland sharks reach 400 years without apparent age-related disease. What do they share at the sequence level?
caVos applies a systematic comparative genomics screen across 40+ long-lived species. We identify genetic variants conserved across these lineages — variants under positive selection that correlate with longevity phenotypes. Then we ask: which of the proteins these variants encode can be safely upregulated via mRNA?
The result is a ranked list of mRNA therapeutic candidates grounded in evolutionary evidence, not speculation.
Three integrated systems. One coherent pipeline.
Comparative genomics feeds AI sequence design. Sequence design feeds LNP formulation prediction. The output is a ranked candidate ready for in vitro validation.
Comparative Genomics
Multi-species variant alignment across 40+ exceptionally long-lived species. Conservation scoring identifies which sequence variants are under evolutionary pressure in longevity-relevant contexts.
Read the method →AI Sequence Design
ML model trained on protein upregulation literature predicts which mRNA sequences will safely increase target protein levels — with off-target prediction integrated at the design stage.
CVX Candidate series identifierLNP Formulation Prediction
Computational search through lipid composition parameter space to rank formulations predicted to cross the blood-brain barrier. In silico first — wet-lab validation is the confirmation step.
CNS delivery approach →Naked mole rats don't get cancer. Bowhead whales live 200 years. Greenland sharks reach 400. We asked: what do they share at the sequence level?
The comparative genomics approach to longevity isn't new. Applying AI-guided variant prioritization systematically across 40+ long-lived species — and coupling that output directly to mRNA sequence design and LNP formulation modeling — is what we built. Evolution already ran the experiment. We are reading the data it left behind.
Read the science →
Computational biology background in protein interaction networks and aging-related pathway analysis. Founded caVos in 2023 after identifying the gap between available longevity genomics data and systematic mRNA therapeutic hypothesis generation.
Built from a research question, not a term sheet.
In 2023, Ohad Tarcic was analyzing aging-related protein network dysregulation when the same question kept appearing in the literature: exceptional longevity across distantly-related species — naked mole rats, bowhead whales, Greenland sharks — was well-documented, but the genetic variants underlying those phenotypes were not being systematically converted into mRNA therapeutic hypotheses. That gap became caVos.
mRNA is the right modality for longevity targets. Transient expression, controllable dosing, no genomic integration. It lets us ask "what happens if this specific protein is upregulated?" without the irreversibility of gene therapy. The LNP delivery platform — validated for CNS-adjacent applications — gives us a credible path to neurodegeneration. Tel Aviv's computational biology culture gave us the right scientific environment to build this way.
We build computationally and partner for experimental validation. The hypotheses are grounded in evolutionary evidence; the wet-lab data will confirm or falsify them. That is the honest model we are building on.
We started this with conviction, not a term sheet.