Designing Minimal Genomes Using Whole-Cell Models

Rees, Joshua; Chalkley, Oliver; Landon, Sophie; Purcell, Oliver; Marucci, Lucia; Grierson, Claire

doi:10.1101/344564

Cited by 4 publications

(12 citation statements)

References 29 publications

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“…The case study shows how to implement a genetic algorithm and run it on different clusters. Rees and Chalkley et al [37] also show how to implement a different algorithm. Whilst it has not been shown yet, the modularity of the code may make it possible to use different models and even different design goals.…”

Section: Resultsmentioning

confidence: 99%

“…Both job managers are commonly used for computer clusters and so these examples can be used as templates for other clusters with these job managers, otherwise they still act as an example of how to write a connection sub-class -for more information on this see the supplementary information. For an example of implementing a different algorithm using PyGDS see the guess, add, mate algorithm (GAMA) in [37] which is implemented as three separate algorithms using the PyGDS framework where the results of a previous stage are fed into the next next stagecombined these three stages act like a modified genetic algorithm that is designed to converge on a minimal genome faster.…”

Section: Pygds Frameworkmentioning

confidence: 99%

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The genome design suite: enabling massive in-silico experiments to design genomes

Chalkley

Purcell²,

Grierson³

et al. 2019

Preprint

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Motivation: Computational biology is a rapidly developing field, and insilico methods are being developed to aid the design of genomes to create cells with optimised phenotypes. Two barriers to progress are that insilico 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

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Section: Resultsmentioning

confidence: 99%

Section: Pygds Frameworkmentioning

confidence: 99%

The genome design suite: enabling massive in-silico experiments to design genomes

Chalkley

Purcell²,

Grierson³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…[8,12] This is an ecessary caveat as doubling time varies widely amonge xtant bacteria. [8,12] This is an ecessary caveat as doubling time varies widely amonge xtant bacteria.…”

Section: Core Accessory and Quasi-essential Genesmentioning

confidence: 99%

“…When designing am inimal genomet hat supports life, an arbitraryt ime limit for cell division is often set to render the cellline experimentally practical. [8,12] This is an ecessary caveat as doubling time varies widely amonge xtant bacteria. [13] Therefore, in addition to core and accessory genes, minimal genomesa lso contain quasi-essential genes (i.e.,g enes required for an organism to meet an arbitrarily set maximum doubling time, but that might not be necessaryi ft hat parameter were removed).…”

Section: Core Accessory and Quasi-essential Genesmentioning

confidence: 99%

“…[10] Currentins ilico research suggests that many more genes could be deleted from syn3.0 and still allow it to retain viability.F or example, ar ecent computational approachp roduced minimal genomes smaller than syn3.0 and identified 11 redundant essential genes. [12] Such in silico approaches will be the key to overcoming the main challenges associated with minimal genome design:q uasi-essential genes, geneticr edundancy,a nd the significant fraction of essential genes annotated as unknown function. [17] 2.3.…”

Section: The Minimal Genomeofs Yn30mentioning

confidence: 99%

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Lessons from a Minimal Genome: What Are the Essential Organizing Principles of a Cell Built from Scratch?

et al. 2019

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One of the primary challenges facing synthetic biology is reconstituting a living system from its component parts. A particularly difficult landmark is reconstituting a self‐organizing system that can undergo autonomous chromosome compaction, segregation, and cell division. Here, we discuss how the syn3.0 minimal genome can inform us of the core self‐organizing principles of a living cell and how these self‐organizing processes can be built from the bottom up. The review underscores the importance of fundamental biology in rebuilding life from its molecular constituents.

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Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

Landon

Rees-Garbutt

Marucci

et al. 2019

Essays in Biochemistry

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Producing ‘designer cells’ with specific functions is potentially feasible in the near future. Recent developments, including whole-cell models, genome design algorithms and gene editing tools, have advanced the possibility of combining biological research and mathematical modelling to further understand and better design cellular processes. In this review, we will explore computational and experimental approaches used for metabolic and genome design. We will highlight the relevance of modelling in this process, and challenges associated with the generation of quantitative predictions about cell behaviour as a whole: although many cellular processes are well understood at the subsystem level, it has proved a hugely complex task to integrate separate components together to model and study an entire cell. We explore these developments, highlighting where computational design algorithms compensate for missing cellular information and underlining where computational models can complement and reduce lab experimentation. We will examine issues and illuminate the next steps for genome engineering.

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Designing Minimal Genomes Using Whole-Cell Models

Cited by 4 publications

References 29 publications

The genome design suite: enabling massive in-silico experiments to design genomes

The genome design suite: enabling massive in-silico experiments to design genomes

Lessons from a Minimal Genome: What Are the Essential Organizing Principles of a Cell Built from Scratch?

Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering

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