Why high-throughput bio research needs better tools immediately

Riskgaming

There have been data revolutions in most areas of human activity, and biological research is no exception. The rapidly shrinking cost of collecting data like DNA sequences means that there has been an exponential growth in the amount of data that bio researchers have at their disposal. Yet, most biologists still operate on top of general purpose cloud compute platforms, which don’t offer a native environment for them to engage in research at the cutting edge of the field.

On the Riskgaming podcast today, Lux’s Tess van Stekelenburg interviews Alfredo Andere and Kenny Workman, the co-founders of LatchBio who are on a quest to rapidly accelerate the progress of biology’s tooling. The big challenge — even for big pharma — is a lack of access to top-flight AI/ML developers in the ferocious talent wars they face against even bigger Big Tech companies. As Workman says, “They just don't have world's best machine learning talent … And then they're working with usually 5- to 10-year-old machine learning technology, except for a small handful of outliers.” LatchBio and other startups are pioneering new ways of delivering those tools to biologists, today.

In this episode, the trio discuss the changing data economy of biological research, the lack of infrastructure for conducting laboratory and clinical work, why AstraZeneca has improved its pharma output over the past decade, what the ground truth is around AI and bio, the flaws of open-source software, and finally, how academia and commercial research will fit together in the future.