May 26, 2017
11:00 AM - 12:00 PM
Speaker: Andrew Ferguson
Assistant Professor, Materials Science and Engineering,
University of Illinois Urbana-Champaign
Title: "Machine learning in soft and biological materials: Engineering self-assembling colloids and viral phase behavior"
Data-driven modeling and machine learning have opened new paradigms and opportunities in the understanding and design of soft and biological materials. Colloidal particles with tunable anisotropic surface interactions are of technological interest in fabricating soft responsive actuators, biomimetic polyhedral encapsulants, and substrates for high-density information storage. In the first part of this talk, I will describe our applications of nonlinear manifold learning to determine low-dimensional "assembly landscapes" from computer simulations and experimental particle tracking data for self-assembling patchy colloids. These landscapes connect colloid architecture and prevailing conditions with emergent assembly behavior, informing how to engineer the stability and accessibility of desired aggregates. Empirical models of viral fitness present a means to rationally design antiviral therapeutics by revealing vulnerabilities within the viral proteome. In the second part of this talk, I will discuss the translation of clinical sequence databases into spin glass models of viral fitness that reveal an interesting connection with statistical thermodynamics in which a data-driven fitness model of HIV admits an "error catastrophe" – mutational meltdown of the viral quasispecies induced by an elevated mutation rate – isomorphic to a first order phase transition. Our work informs new antiviral control strategies and provides a rationale for why HIV can live on the precipice of the error catastrophe with impunity.