Colloquium – Peter Nelson

Peter Hugo Nelson, Fisk University

Random walks in active Learning- from an Einstein misconception to the value of simple models in the age of Ai

This colloquium presents discoveries made while developing a novel active-learning approach for undergraduates interested in life sciences. The materials start with the “Marble Game” – a kMC simulation of diffusion based on Brownian motion. Students discover that Fick’s law of diffusion is explained by the unbiased random jumping in the Marble Game that simulates the Ehrenfest (dog-flea) model using a simplified Gillespie algorithm. In a similar guided-inquiry environment, students apply finite difference methods to: drug elimination; radioactive decay; osmosis; ligand binding; enzyme kinetics; the Boltzmann factor; phase equilibrium; random walks; membrane voltage, the action potential; COVID-19; and Newtonian mechanics. Students discover that science is an evidence-based endeavor with testable hypotheses supported by experiment. The approach led to the development of a new diffusive model of osmosis that remains controversial, because it’s at odds with Einstein’s (and other’s) understanding of osmotic pressure. The same approach was applied to COVID-19 and resulted in what’s called a “sloppy model,” because two of its four parameters are not uniquely determined by infection rate data. This “sloppiness” has been observed in models in – biochemical reaction networks, quantum Monte Carlo, empirical interatomic potentials, particle accelerator design, insect flight and machine learning. Thus, with the advent of AI, it’s even more important for students to understand that the purpose of mechanistic modeling is not to make a graph that goes through the points (a mere data reduction) – it’s to gain insights into why the physical system behaves in the manner that’s observed experimentally – and to be able to predict what would happen if conditions are changed. AI models can easily find a curve that goes through the points, but they don’t generate the kind of human insights that can be gained using an incremental approach to mechanistic modeling.

Bio: Dr. Pete Nelson received an MSc in Physics from VUW (NZ) in 1990 where he initially developed kMC sim techniques. He further developed them at MIT as a PhD student in ChemE. Nelson was a postdoc at UMass in PChem and was awarded an NIH Postdoc in Quantitative Biology. He’s been teaching physics and biophysics at PUIs for over 20 years. Nelson is interested in curricular reform and is publishing a textbook “Biophysics and Physiological modeling” with Cambridge.  https://circle4.com/biophysics

October 16, 2025 @ 4:10pm (CST) in 4327 Stevenson Center; light refreshments available at 3:50 PM

Host: J Wikswo