Constructing an Epistatic Network from Hepatitis C Viral Protein Sequence Data using Algorithmic Methods

Abstract

Epistasis occurs when another genetic mutation influences the effect of one. This concept is essential for understanding how organ- isms develop their traits, especially regarding disease risk and treatment responses. In hepatitis C, the virus has many interactions between mu- tations that affect its behavior and how it interacts with the host. To tackle this complexity, we propose a new computational method using an epistatic network to connect viral mutations found in different pa- tients. Our approach involves several steps: (1) creating a matrix to track specific mutations in viral sequences, (2) identifying pairs of mutations that interact with each other, and (3) building a network to visualize these relationships. We also offer an optional analysis to find dense clus- ters within the network, which may indicate groups of mutations that work together. These dense clusters could represent new viral haplo- types—unique combinations of mutations that affect the virus’s ability to infect, evade the immune system, and respond to treatments. By ex- ploring how network density relates to haplotype development, we aim to discover patterns that could lead to better treatment strategies and a deeper understanding of hepatitis C. This research not only helps us understand epistatic interactions in hepatitis C but also sets the stage for future studies on viral evolution. As we improve our computational methods, we aim to clarify how these interactions influence viral traits, ultimately leading to better patient healthcare outcomes.