Pathogen genomic data are rich with information and growing exponentially. At the same time, new genomics-based technologies are transforming how we surveil and combat pathogens. Yet designing biological sequences for these technologies is still done largely by hand, without well-defined objectives and with a great deal of trial and error. We lack computational capabilities to efficiently design and optimize frontline public health and medical tools, such as diagnostics, based on emerging genomic information.
In this talk, I examine computational techniques – linked closely with biotechnologies – that enhance how we proactively prepare for and respond to pathogens comprehensively. I discuss CATCH, an algorithm that designs assays for simultaneously enriching the genomes of hundreds of viral species including all their known variation; they enable hypothesis-free viral detection and sequencing from patient samples with high sensitivity. I also discuss ADAPT, which combines a deep learning model with combinatorial optimization to design CRISPR-based viral diagnostics that are maximally sensitive over viral variation. ADAPT rapidly and fully-automatically designs diagnostics for thousands of viruses, and they exhibit lower limits of detection than state-of-the-art design strategies. The results show that principled computational design will play a vital role in an arsenal against infectious diseases. Finally, I discuss promising directions for design methods and applications to other diseases.
Hayden Metsky is a postdoctoral researcher at the Broad Institute in Pardis Sabeti’s lab. He completed his PhD, MEng, and SB in computer science at MIT. His research focuses on developing and applying computational methods that enhance the tools we use to detect and treat disease, concentrating on viruses.