Learning from Life: Understanding and Design of Complex Biophysical Systems through Multiscale Modeling and Machine Learning

*To request access to the videoconference, email dsivak@sfu.ca

Synopsis

What do hybrid antibiotics and venomous snail proteins have in common? Both are complex biological systems ripe for computational physics design. In this talk, I will explain my research interests in physics-based design of complex biophysical problems and briefly situate them in the context of three recent projects. First, I will show how developing a new fragment-based machine learning algorithm in conjunction with coarse-grained molecular dynamics ("Hunting FOX" for "Hunting Fragments Of X") led to understanding the physical mechanisms by which different chemical submolecules contribute to antibiotic permeation into Gram-negative bacteria. This represents a step towards hybrid antibiotic design to combat antimicrobial drug resistance. Second, I will demonstrate how I am using nonlinear dimensionality reduction and atomistic molecular dynamics simulations to map out the folding landscape and eventually design space of disulfide-rich peptides. Finally, I will briefly discuss the insights molecular dynamics and a simple Ising model have given for understanding of the SARS-CoV-2 Spike trimer.  Overall, I will give a broad overview of my interests and where I foresee those interests taking me and my lab.