Uncovering Patterns in Biological Complexity

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This chapter explores the use of powerful computers and large datasets in analyzing complex biological systems. It emphasizes the importance of integrating existing knowledge about biological systems into machine learning methods and highlights the challenges in understanding the behavior of cells and tissues due to the large number of components involved.

Biological organisms are paradigmatic emergent systems. That atoms of which they are made mindlessly obey the local laws of physics; even cells and organs do their individual jobs without explicitly understanding the larger whole of which they are a part. And yet the system as a whole functions beautifully, with apparent purpose and function. How do the small parts come together to form the greater whole? I talk with biophysicist Rosemary Braun about what we're learning about collective behavior within organisms from the modern era of huge biological datasets, especially crucial aspects like timekeeping (with bonus implications for dealing with jet lag).

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Blog post with transcript: https://www.preposterousuniverse.com/podcast/2023/09/25/251-rosemary-braun-on-uncovering-patterns-in-biological-complexity/

Rosemary Braun received her Ph.D. in physics from the University of Illinois at Urbana-Champaign, and an M.P.H. in biostatistics from Johns Hopkins. She is currently an associate professor of molecular biosciences, applied math, and physics at Northwestern University and external faculty at the Santa Fe Institute.

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