Furthering genome design using models and algorithms

AbstractMotivationComputational 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.ResultsHere 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.AvailabilityThe genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source [email protected], [email protected], and [email protected].

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Designing Minimal Genomes Using Whole-Cell Models

10.1101/344564 ◽

2018 ◽

Cited By ~ 4

Author(s):

Joshua Rees ◽

Oliver Chalkley ◽

Sophie Landon ◽

Oliver Purcell ◽

Lucia Marucci ◽

...

Keyword(s):

In Silico ◽

Genome Engineering ◽

Cell Model ◽

Mycoplasma Genitalium ◽

Computational Design ◽

Functional Assay ◽

Proof Of Concept ◽

Computational Tools ◽

Whole Cell ◽

Genome Design

AbstractIn the future, entire genomes tailored to specific functions and environments could be designed using computational tools. However, computational tools for genome design are currently scarce. Here we present algorithms that enable the use of design-simulate-test cycles for genome design, using genome minimisation as a proof-of-concept. Minimal genomes are ideal for this purpose as they have a simple functional assay, the cell either replicates or not. We used the first (and currently only published) whole-cell model, for the bacterium Mycoplasma genitalium. Our computational design-simulate-test cycles discovered novel in-silico minimal genomes smaller than JCVI-Syn3.0, a bacteria with, currently, the smallest genome that can be grown in pure culture. In the process, we identified 10 low essentiality genes, 18 high essentiality genes, and produced evidence for at least two Mycoplasma genitalium in-silico minimal genomes. This work brings combined computational and laboratory genome engineering a step closer.

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Minimal Genome Design Algorithms Using Whole-Cell Models

Methods in Molecular Biology - Computational Methods in Synthetic Biology ◽

10.1007/978-1-0716-0822-7_14 ◽

2020 ◽

pp. 183-198

Author(s):

Joshua Rees-Garbutt ◽

Oliver Chalkley ◽

Claire Grierson ◽

Lucia Marucci

Keyword(s):

Cell Models ◽

Whole Cell ◽

Minimal Genome ◽

Design Algorithms ◽

Genome Design

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