Built in Tel Aviv. Rooted in computational biology.
Founded 2023, bootstrapped. Four researchers applying comparative genomics and mRNA sequence design to the question of why some species age so much more slowly than others — and what that means for neurodegeneration.
Why We Started caVos
In 2023, Ohad Tarcic was mapping protein interaction networks involved in aging-related pathway dysregulation when a specific gap became hard to ignore: exceptional longevity across phylogenetically distant species — naked mole rats, bowhead whales, Greenland sharks — had been well-documented, but the genetic variants underlying those phenotypes were not being systematically translated into therapeutic hypotheses. The comparative genomics data existed. The mRNA delivery infrastructure existed. No one was connecting them with a structured computational pipeline.
Comparative genomics as a drug discovery method is not novel. The PCSK9 story is the canonical example: loss-of-function variants identified in human populations with protected cardiovascular phenotypes became one of the most successful target classes in modern cardiology. We are applying the same logic in the opposite direction — looking for gain-of-function conservation signals in long-lived species, and asking which proteins those signals nominate for upregulation via mRNA.
caVos was incorporated in Tel Aviv in 2023, bootstrapped, without external funding. Building computationally-first was a deliberate scientific position: computation lets us work through the hypothesis space quickly and prioritize rigorously before committing to in vitro studies. We bring designed candidates; we partner for experimental validation.
mRNA is the right modality for upregulation targets. It is transient, controllable, and does not require genomic integration. The LNP platform, validated by CNS-adjacent applications in academic and clinical settings, provides a credible path to neurodegeneration targets. That combination — upregulation-focused biology plus a tractable delivery route — defines our scope.
The caVos Team
Four researchers. Computational biology, mRNA sequence design, and LNP formulation chemistry — the three domains this problem requires.
Computational biology background in protein interaction networks and aging-related pathway dysregulation. Spent three years building ML models for biological network analysis before founding caVos in 2023 around the comparative genomics longevity hypothesis.
Molecular biology training with a focus on RNA secondary structure prediction and 5′/3′ UTR engineering for translational control. Worked in an academic RNA therapeutics lab on mRNA stability optimization before joining caVos to lead sequence design for the CVX series.
Dual background in computer science and molecular biology. Built multi-species variant alignment tools and conservation scoring pipelines during graduate research in bioinformatics. At caVos, he leads both the genomics analysis pipeline and the ML-guided sequence design system.
Chemistry and biophysics background with a focus on lipid nanoparticle characterization — particle sizing, encapsulation efficiency, and ionizable lipid pKa optimization. Trained in physical chemistry before moving into pharmaceutical formulation research. Leads the CVX-003 computational formulation program at caVos.
Why Tel Aviv
Israel's output in computational and structural biology is disproportionate to its population. The Weizmann Institute of Science has produced foundational work in protein structure prediction, systems biology, and gene regulation. The Technion's AI and bioinformatics research programs are among the strongest in the region. Hebrew University's Faculty of Medicine has contributed substantially to translational genetics. These institutions are not our affiliates — they are the scientific culture that shapes the talent pool we hire from and the intellectual environment we operate in.
The Israeli biotech ecosystem has produced computationally-sophisticated companies — Compugen pioneered computational drug target discovery in the late 1990s; more recently, companies like MeMed and Immunai have combined biological insight with machine learning. The pattern is consistent: strong AI/CS training, deep biological domain knowledge, willingness to work hypothesis-first before scaling wet-lab operations. That culture aligns precisely with the caVos model.
We are not affiliated with Weizmann, the Technion, Hebrew University, Compugen, MeMed, or Immunai. We reference them as context for the ecosystem in which caVos was built.