Big Biology Art Woods, Cam Ghalambor, and Marty Martin

We have finished Season 6 of Big Biology. Learn more about the future of the podcast.


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What is mutation bias and how can scientists study it? How does changing a population’s mutation bias influence its evolutionary trajectory?

In this episode, we talk with Deepa Agashe, an Associate Professor at the National Centre for Biological Sciences in Bangalore, India. We first talk with Deepa about mutation bias and how she uses  E. coli to understand it. We then focus on a 2023 PNAS paper about the fitness effects of experimentally changing the mutation bias in E. coli. In this research, Deepa and her team used a strain (MutY) of bacteria containing a mutation that knocks out an important DNA repair enzyme. They then isolated subsequent single mutations produced within both MutY and wildtype lines and studied the fitness effects of those mutations. Surprisingly, more than a third of mutations in the mutant lines were beneficial, and often across several different environments. Zooming out, the big picture is that shifts in mutation bias seem to generate new kinds of mutations that weren’t previously accessible to lineages, and a greater fraction of those may be beneficial in some circumstances.

Art by Keating Shahmehri. Find a transcript of this episode on our website.


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This week on Big Biology we're sharing an episode from The Naked Scientists Podcast about how humans lost their tails.

Humans, chimpanzees, gorillas and orangutans do not have tails. It sets us apart from other primates, but suggests that our shared evolutionary ancestors had them. So why did we lose them, and how? Speaking with Chris Smith, from The Naked Scientists Podcast, NYU Grossman School of Medicine's Itai Yanai explains that the way this study began was literally a pain in the "tail" for one of his colleagues...

Credit: The Naked Scientists Podcast


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At what levels does causation happen in biology? Are metaphors useful for understanding biology?

In this episode, we talk with Phil Ball, a science writer who was also an editor for the journal Nature for over 20 years. Phil has written over 25 books, but our conversation focuses on his most recent: “How Life Works: A User’s Guide to the New Biology.” In the book, Phil covers a wide-range of topics from cells to proteins to biological agency, and makes the argument that traditional ideas and simplified metaphors in biology often don’t hold up. We talk with Phil about the concept of the selfish gene and unpack what it actually means and when it’s useful. Then we dive into the paradox of how multicellular organisms are composed of multiple levels of agency, yet are complex agents themselves. Phil also discusses the biomedical implications of thinking about cancer as one in many possible states that cells can inhabit across a landscape.

Art by Keating Shahmehri. Find a transcript of this episode on our website.


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How should biologists deal with the massive amounts of population genetic data that are now routinely available? Will AIs make biologists obsolete?

In this episode, we talk with Andy Kern, an Associate Professor of Biology at the University of Oregon. Andy has spent much of his career applying machine learning methods in population genetics. We talk with him about the fundamental questions that population genetics aims to answer and about older theoretical and empirical approaches  We then turn to the promise of machine learning methods, which are increasingly being used to estimate population genetic structure, patterns of  migration, and the geographic origins of trafficked samples. These methods are powerful because they can leverage high dimensional genomic data. Andy also talks about the implications of AI and machine learning for the future of biology research. 

Cover Art by Keating Shahmehri. Find a transcript of this episode at our website.


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How should we study complex biological networks? How do cells keep time and stay in sync? What does it mean for a network to be resilient?

In this episode, we talk with Rosemary Braun, Associate Professor at Northwestern University in the Department of Molecular Biosciences and a member of the NSF-Simons Center for Quantitative Biology. Rosemary is broadly interested in learning whether “more is different” when it comes to complex molecular networks operating across different temporal and spatial scales. We talk with her about systems approaches to uncovering the “Rules of Life” and about circadian (daily) rhythms. She and her team use machine learning to understand emergent phenomena in networks, with the goal of helping medical professionals target treatments based on an individual patient’s circadian rhythm.

Cover art: Keating Shahmehri. Find a transcript of this episode on our website.


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Support this podcast: https://podcasters.spotify.com/pod/show/bigbiology/support