Picture a bowl of spaghetti and meatballs. There are pristine noodles, drenched in rich tomato sauce, topped with savory meatballs. Now imagine you’re only allowed to eat just one noodle, and one meatball. You’re tasked with finding the very best, the most interesting bite out of this bowl of spaghetti. It might sound absurd, but replace spaghetti with ‘edges’ and meatballs with ‘nodes’ and you’ve got a network.
Computational biologists like our guest this week use networks to uncover meaningful relationships, or the tastiest spaghetti noodle and meatball, between biological entities.
Joining us this week is Nolan Newman, a PhD candidate in the College of Pharmacy under PI Andriy Morgun. Nolan’s research lies at the intersection of math, statistics, computer science, and biology. He’s looking at how networks, such as covariation networks, can be used to look for relationships and correlations between genes, microbes, and other factors from massive datasets which compare thousands or even of biological entities. With datasets this large and complex, it can be difficult to pare down just the important or interesting relationships – like trying to scoop a single bowl of spaghetti from a giant tray at a buffet, and then further narrowing it down to pick just one interesting noodle.
Nolan is further interested in how different statistical thresholds and variables contribute to how the networks ‘look’ when they are changed. If only noodles covered in sauce are considered ‘interesting’, then all of the sauce-less noodles are out of the running. But what if noodles are only considered ‘sauce-covered’ if they are 95% or more covered? Could you be missing out on perfectly delicious, interesting noodles by applying this constraint?
If you’re left scratching your head and a little hungry, fear not. We’ll chat about all things computational biology, networks, making meaning out of chaos, and why hearing loss prompted Nolan to begin a career in science, all on this week’s episode of Inspiration Dissemination. Catch the episode live at 7 PST at 88.7 FM or https://kbvrfm.orangemedianetwork.com/, or catch the podcast after the episode on any podcast platform.
