Qnets

Purpose

To effectively model the evolutionary properties of viruses with a novel machine learning algorithm. The Quasinet (Qnet) framework can be used to simulate the evolution of virus strains, predict the probability of a pandemic risk, and decide better vaccine components.

Results

• Using Qnets, we successfully predicted the global Influenza pandemic in 2009.
• This method provided targets for H1N1 influenza that are much more accurate than the recommended targets from the World Health Organization.

Method

Background of Viruses for Computational Biology -

Viruses are infectious agents that latch onto a host, such as a human or animal, in order to replicate. A virus usually has multiple strains, or variations of that virus that performs similar functions. Each strain consists of proteins which originate from a specific sequence of amino acids. These amino acids can be thought of as specific letters, and the entire sequence can be thought of as a sentence. However, a strain can mutate (lose, gain, or exchange amino acids), and these mutations can be significant; changing enough amino acids may change the properties of the strain.

Qnet Framework

• Learns structural dependencies of symbols within sequences
• The Qnet is composed of many conditional inference trees
• Each tree corresponds to a specific location in a sequence
• Once the trees are trained, each tree uses all locations within the sequence to predict the probabilities of certain occurrences at other locations based on the corresponding index

Qnets and Virological Understanding

• In the case of a virus, the Qnet learns the structural dependencies between amino acids. The conditional inference decision trees use all the amino acids within a given sequence to predict the probability of an amino acid occurrence at its corresponding index.
• Within a Qnet a distance measure can be defined, called the Qdistance. The Qdistance describes how close one sequence is to another. Given a virus, we can measure the Qdistance between every pair of strains, and then construct phylogenetic trees and understand the evolutionary trajectory of the virus using these Qdistances.
• The trained Qnet model can also simulate the evolution of a strain until convergence by using the Qnet induced probabilities to decide which amino acids we want to mutate. We use this simulation process to predict risk of a pandemic, and we say such a risk exists if two following conditions are met.

1. After simulating the evolution of a strain with a non-human host, we find that the strain moves closer to the closest strain with a human host.
2. After performing another simulation, we find that the same strain fails to mutate towards the most common human strain.

• The Qnet framework can be used to choose vaccine targets for each year. Our choice for the target sequence comes from the common strain (as computed by the Qdistance) of a population from previous years.

Qnets and Coronavirus

When applied to the coronavirus, we find that the phylogenetic trees constructed from the Qdistance provides a better representation of the evolutionary process than existing methods.