Deciphering the dynamics of amyloid-β aggregation pathways by game theoretic approach
Jhinuk Saha1, Preetam Ghosh2, Pratip Rana2, Edward Steen3, Ashwin Vaidya3, and Vijayaraghavan Rangachari1
1Department of Chemistry & Biochemistry, School of Mathematics and Natural Sciences, University of Southern Mississippi, Hattiesburg, MS
2Department of Computer Science, Virginia Commonwealth University, Richmond, VA
3Department of Mathematical Science, Montclair State University, Montclair, NJ
Low molecular weight Aβ oligomers have evolved as the primary toxic species involved in Alzheimer disease (AD). Upon generation, Aβ peptides can self-assemble into different aggregate forms along different pathways. Broadly categorized as on- or off-pathways, the two generate structurally different aggregate forms. In our laboratory, we have observed that Aβ, in presence of fatty acid micelles, generate distinct strains of low molecular weight oligomers. Based on their half-lives and conformation, these oligomers were found to form along an “off-pathway”. Using the fundamental aspects of ‘game theory’ based on Nash equilibrium, our labs sought to determine the dynamics of on- and off-pathway kinetics based on a ‘win’ or ‘lose’ model. Biophysical experiments and detailed simulation with mathematical modelling indicate that the preference of on- or off-pathway aggregation depend upon a narrow set of constant parameters and a species of oligomer can be populated by alteration of these parameters. Here, we present that steady-state switching dynamics between on pathway and off pathway aggregates of Aβ that can be modeled with the game theory approach. The models predict the preferred pathway of aggregation as a function of fatty-acid parameters and level of dilution. Experimental data and simulations support the role of fatty acid to modulate temporal parameter in Aβ aggregation pathway. Anticipating spatiotemporal landscapes is crucial for simulating physiological framework for generation of different conformeric strains of Aβ oligomers due to heterotypic interactions. This approach can be significant in understanding the underlying mechanism of oligomer generation and strain formation in AD and other neurodegenerative diseases.