A game-theoretic approach to deciphering the dynamics of amyloid- β aggregation along competing pathways

Author:

Ghosh Preetam1ORCID,Rana Pratip1,Rangachari Vijayaraghavan2,Saha Jhinuk2,Steen Edward3,Vaidya Ashwin3

Affiliation:

1. Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23220, USA

2. Department of Chemistry and Biochemistry, School of Mathematics and Natural Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA

3. Department of Mathematical Science, Montclair State University, Montclair, NJ 07043, USA

Abstract

Aggregation of amyloid- β (A β ) peptides is a significant event that underpins Alzheimer's disease (AD). A β aggregates, especially the low-molecular weight oligomers, are the primary toxic agents in AD pathogenesis. Therefore, there is increasing interest in understanding their formation and behaviour. In this paper, we use our previously established results on heterotypic interactions between A β and fatty acids (FAs) to investigate off-pathway aggregation under the control of FA concentrations to develop a mathematical framework that captures the mechanism. Our framework to define and simulate the competing on- and off-pathways of A β aggregation is based on the principles of game theory. Together with detailed simulations and biophysical experiments, our models describe the dynamics involved in the mechanisms of A β aggregation in the presence of FAs to adopt multiple pathways. Specifically, our reduced-order computations indicate that the emergence of off- or on-pathway aggregates are tightly controlled by a narrow set of rate constants, and one could alter such parameters to populate a particular oligomeric species. These models agree with the detailed simulations and experimental data on using FA as a heterotypic partner to modulate the temporal parameters. Predicting spatio-temporal landscape along competing pathways for a given heterotypic partner such as lipids is a first step towards simulating scenarios in which the generation of specific ‘conformer strains’ of A β could be predicted. This approach could be significant in deciphering the mechanisms of amyloid aggregation and strain generation, which are ubiquitously observed in many neurodegenerative diseases.

Funder

Division of Chemical, Bioengineering, Environmental, and Transport Systems

National Institute of General Medical Sciences

National Center for Research Resources

Publisher

The Royal Society

Subject

Multidisciplinary

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Network Thermodynamics-Based Scalable Compartmental Model for Multi-Strain Epidemics;Mathematics;2022-09-26

2. Physics-Informed Bias Method for Multiphysics Machine Learning: Reduced Order Amyloid-β Fibril Aggregation;Recent Advances in Mechanics and Fluid-Structure Interaction with Applications;2022

3. Global fitting and parameter identifiability for amyloid-β aggregation with competing pathways;2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE);2020-10

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