Biology subjects

Chalkley, O.

Publications and source records attributed to Chalkley, O..

3 recordsLinked to original sources

bioRxiv2020

Understanding metabolic behaviour in whole-cell model output

Whole-cell modelling is a newly expanding field that has many applications in lab experiment design and predictive drug testing. Although whole-cell model output contains a wealth of information, it is complex and high dimensional, thus hard to interpret. Here, we present an analysis pipeline that combines machine learning, dimensionality reduction and network analysis to interpret and visualise metabolic reaction fluxes from a set of single gene knockouts simulated in the Mycoplasma genitalium whole-cell model. We found that the reaction behaviours show trends that correlate with phenotypic classes of the simulation output, highlighting particular cellular subsystems that malfunction after gene knockouts. From a graphical representation of the metabolic network, we saw that there is a set of reactions that can be used as markers of a phenotypic class, showing their importance within the network. Our analysis pipeline can support the understanding of the complexity of in silico cells without detailed knowledge of the constituent parts, which can help to understand the effects of gene knockouts, and, as whole-cell models become more widely built and used, aid genome design.

systems biology↗

bioRxiv2019

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

MotivationComputational 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.\n\nResultsHere 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.\n\nAvailabilityThe genome design suite is written in Python for Linux operating systems and is available from GitHub on a GPL open-source licence.\n\nContacto.chalkley@bristol.ac.uk, lacsg@bristol.ac.uk, and lucia.marucci@bristol.ac.uk.

systems biology↗

bioRxiv2018

Designing Genomes using Design-Simulate-Test Cycles

In 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 very simple functional assay, the cell can either replicate or not. We used the first (and currently only published) whole-cell model, for the bacterium Mycoplasma genitalium 1. Our computational design-simulate-test cycles discovered novel in-silico minimal genomes smaller than JCVI-Syn3.0 2, a bacterial cell with the currently known smallest genome that can be grown in pure culture. In the process, we identified 10 low essentiality genes, 18 high essentiality genes 3, and produced evidence for at least two minimal genomes for Mycoplasma genitalium in-silico. This work brings combined computational and laboratory genome design and construction a step closer.

synthetic biology↗