Predicting Mutational Pathways of Influenza A H1N1 Virus using Q-learning

Influenza is a seasonal viral disease affecting over 1 billion people annually around the globe, as reported by the World Health Organization (WHO). The influenza virus has been around for decades causing multiple pandemics and encouraging researchers to perform extensive analysis of its evolutionary patterns. Current research uses phylogenetic trees as the basis to guide population genetics and other phenotypic characteristics when describing the evolution of the influenza genome. Phylogenetic trees are one form of representing the evolutionary trends of sequenced genomes, but that do not capture the multidimensional complexity of mutational pathways. We suggest representing antigenic drifts within influenza A/H1N1 hemagglutinin (HA) protein as a graph, $G = (V, E)$, where $V$ is the set of vertices representing each possible sequence and $E$ is the set of edges representing single amino acid substitutions. Each transition is characterized by a Malthusian fitness model incorporating the genetic adaptation, vaccine similarity, and historical epidemiological response using mortality as the metric where available. Applying reinforcement learning with the vertices as states, edges as actions, and fitness as the reward, we learn the high likelihood mutational pathways and optimal policy, without exploring the entire space of the graph, $G$. Our average predicted versus actual sequence distance of $3.6 \pm 1.2$ amino acids indicates that our novel approach of using naive Q-learning can assist with influenza strain predictions, thus improving vaccine selection for future disease seasons.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2021

Author dc:creator

• Raghuraman, Aarathi

Chair dc:contributor.committeechair

• Heath, Lenwood S.

Committee members dc:contributor.committeemember

• Vinatzer, Boris A.
• Murali, T. M.

• Fitness
• Mutational Pathways
• Mutational Paths
• Reinforcement Learning
• Q-Learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:32165

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/113672