The hardest thing about computational biology isn’t computational biology
Computational biology and data science/machine learning are the vanguard of digital transformation for early discovery biotech. They enable teams to analyze and interpret data whose volume and complexity would otherwise be inaccessible to most biologists.
A number of factors make these subjects fundamentally difficult: Low signal-to-noise ratio in biological data. The subtleties of a subject with more exceptions than rules. The rapidly evolving landscape of new instruments, assays, and techniques. But the biggest headache, in practice, is something much simpler: The communications overhead of adding a second person to a previously one-person process.
