Machine-learning a virus assembly fitness landscape

01/13/2019
by   Pierre-Philippe Dechant, et al.
0

Realistic evolutionary fitness landscapes are notoriously difficult to construct. A recent cutting-edge model of virus assembly consists of a dodecahedral capsid with 12 corresponding packaging signals in three affinity bands. This whole genome/phenotype space consisting of 3^12 genomes has been explored via computationally expensive stochastic assembly models, giving a fitness landscape in terms of the assembly efficiency. Using latest machine-learning techniques by establishing a neural network, we show that the intensive computation can be short-circuited in a matter of minutes to astounding accuracy.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/27/2019

A Simple Haploid-Diploid Evolutionary Algorithm

It has recently been suggested that evolution exploits a form of fitness...
research
03/06/2022

A Crowdsourced Gameplay for Whole-Genome Assembly via Short Reads

Next-generation sequencing has revolutionized the field of genomics by p...
research
12/06/2013

How Santa Fe Ants Evolve

The Santa Fe Ant model problem has been extensively used to investigate,...
research
09/20/2023

Reachability Analysis for Lexicase Selection via Community Assembly Graphs

Fitness landscapes have historically been a powerful tool for analyzing ...
research
04/30/2018

New Methods of Studying Valley Fitness Landscapes

The word "valley" is a popular term used in intuitively describing fitne...
research
03/11/2019

Toward a fitness landscape model of firms' IT-enabled dynamic capabilities

This chapter presents, extends and integrates a complexity science persp...
research
12/15/2021

Breeding realistic D-brane models

Intersecting branes provide a useful mechanism to construct particle phy...

Please sign up or login with your details

Forgot password? Click here to reset