Analyzing Complex Systems and Biological Complexity

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This chapter explores the concept of complex systems, the reductionist point of view, and the tension between targeting specific elements or considering systems-level properties in biology. It discusses the concepts of coarse graining, intervention, and recovering statistical properties of networks to understand biological complexity. The chapter also delves into the relationship between eigenvalues and eigenvectors of a graph and the function of the network, as well as the idea of 'rules of life' to bridge microscopic and macroscopic descriptions of biological systems.

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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