DMS/NIGMS 2: Statistical Network Models for Protein Aggregation

This project centers on the development of statistical network models for understanding the formation of
protein aggregates associated with disease states as well as critical biological processes. Systems of this
type include amyloid fibrils and toxic oligomers, amorphous protein aggregates, and the large, dynamic
complexes formed by small heat shock proteins. Our work combines modeling techniques from the
mathematical social sciences with theoretical and experimental methods from biophysical chemistry,
enabling us to approach biological problems in novel ways. Our technical innovations are focused on
Hamiltonian-driven network models, extending methods originally developed for social networks to capture
interactions among individual proteins in solution over time scales of hours to days.
The project team comprises an established collaboration between a mathematical social scientist and
statistician with expertise in computational statistics and network analysis, and an experimental biophysical
chemist with relevant expertise in protein structure and function. Essential components of this research
include both the creation of modeling techniques that can be used effectively with existing experimental
data, and the collection of new data to validate our modeling work. This work will result in a collection of
novel methods for the study of protein aggregation that are both statistically principled and empirically
grounded, as well as biologically relevant empirical data.

Grant Number: 5R01GM144964-04
NIH Institute/Center: NIH
Principal Investigator: Carter Butts