DMS/NIGMS 2: Statistical Network Models for Protein Aggregation

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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.