PT  - JOURNAL ARTICLE
AU  - Oliver Chalkley
AU  - Oliver Purcell
AU  - Claire Grierson
AU  - Lucia Marucci
TI  - The genome design suite: enabling massive in-silico experiments to design genomes
AID  - 10.1101/681270
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 681270
4099  - http://biorxiv.org/content/early/2019/06/24/681270.short
4100  - http://biorxiv.org/content/early/2019/06/24/681270.full
AB  - Motivation Computational biology is a rapidly developing field, and in-silico methods are being developed to aid the design of genomes to create cells with optimised phenotypes. Two barriers to progress are that in-silico methods are often only developed on a particular implementation of a specific model (e.g. COBRA metabolic models) and models with longer simulation time inhibit the large-scale in-silico experiments required to search the vast solution space of genome combinations.Results Here we present the genome design suite (PyGDS) which is a suite of Python tools to aid the development of in-silico genome design methods. PyGDS provides a framework with which to implement phenotype optimisation algorithms on computational models across computer clusters. The framework is abstract allowing it to be adapted to utilise different computer clusters, optimisation algorithms, or design goals. It implements an abstract multi-generation algorithm structure allowing algorithms to avoid maximum simulation times on clusters and enabling iterative learning in the algorithm. The initial case study will be genome reduction algorithms on a whole-cell model of Mycoplasma genitalium for a PBS/Torque cluster and a Slurm cluster.Availability The genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source licence.Contact o.chalkley{at}bristol.ac.uk, lacsg{at}bristol.ac.uk, and lucia.marucci{at}bristol.ac.uk.