Engineering Evolution

Nigel Goldenfeld, Ph.D.

Chancellor's Distinguished Professor of Physics

University of California, San Diego


Seminar Information

Seminar Date
March 17, 2023 - 2:00 PM

Location
The FUNG Auditorium - PFBH

Nigel Goldenfeld

Abstract

Evolution frequently complicates efforts to engineer complex biological systems, leading to unpredictable system-scale response.  Examples include context-dependence in synthetic biology, the emergence of antibiotic resistance, herbicide resistance, insecticide resistance, chemotherapy resistance and the difficulties in controlling the global pandemic of COVID-19.  In this talk, I will try to be provocative and suggest some first steps towards revisiting these problems from a fundamental perspective grounded in dynamical systems theory and statistical physics (although of course, I won't be able to get very far).

In the first part of the talk, I will present recent results on the large-scale structure of evolution, showing that despite their apparent complexity, phylogenetic trees exhibit two unexplained broad structural features which are consistent across evolutionary time. The first is that phylogenetic trees exhibit scale-invariant topology, which quantifies the fact that their branching lies in between the two extreme cases of balanced binary trees and maximally unbalanced ones. The second is that the backbones of phylogenetic trees exhibit bursts of diversification on all timescales. I present a minimal model of ecological niche construction coupled to a simple model of speciation and use renormalization group arguments to show that the statistical scaling properties of the resultant phylogenetic trees recapitulate both the scale-invariant topology and the bursty pattern of diversification in time. These results can be thought of as a statistical outcome of context-dependence and show in principle how dynamical scaling laws of phylogenetic trees on long time-scales may emerge from generic aspects of the interplay between ecological and evolutionary processes. 

In the second part of the talk, I will discuss two questions in engineering evolution where we do not have even a good qualitative understanding let alone a quantitative one: (1) the spontaneous emergence of the open-ended growth of complexity, where I present the first computational model that demonstrably gives rise to open-endedness; and (2) some remarks about the response of evolving systems to perturbations and the implications for their control. 

This talk will be at an accessible level, and no prior knowledge of modern statistical mechanics is assumed.

 

Speaker Bio

Nigel holds the Chancellor's Distinguished Professorship in Physics and joined UCSD in Fall 2021 after being at the University of Illinois at Urbana-Champaign from 1985-2021.  Nigel's research spans condensed matter theory, the theory of living systems, hydrodynamics and non-equilibrium statistical physics.  

Nigel received his Ph.D. from the University of Cambridge (U.K.) in 1982, and for the years 1982-1985 was a postdoctoral fellow at the Institute for Theoretical Physics, University of California at Santa Barbara. From 1985-2021, Nigel was in the Department of Physics at the University of Illinois at Urbana-Champaign, where he eventually held the position of Swanlund Endowed Chair and Center for Advanced Study Professor of Physics.  He also led the Biocomplexity Group at the Institute for Genomic Biology, and founded the Institute for Universal Biology there, which was part of the NASA Astrobiology Institute from 2013-2019.  In 1996, Nigel co-founded NumeriX, a company that provides high-performance software for the derivatives marketplace. Selected honours include: Alfred P. Sloan Foundation Fellow, University Scholar of the University of Illinois, the A. Nordsieck award for excellence in graduate teaching and the American Physical Society's Leo P. Kadanoff Prize (2020). Nigel is a Fellow of the American Physical Society, a Fellow of the American Academy of Arts and Sciences and a Member of the US National Academy of Sciences.