Biology subjects

Grierson, C.

Publications and source records attributed to Grierson, C..

8 recordsLinked to original sources

bioRxiv2023

In-vivo multicellular control of gene expression in microbial consortia

This paper presents an innovative approach in synthetic biology, focusing on the engineering of biomolecular feedback control systems within microbial consortia to achieve robust and precise regulation of desired phenotypes. Traditional biomolecular control strategies, while effective, are predominantly confined to single-cell applications, limiting their complexity and adaptability due to metabolic constraints. We propose a novel methodology that distributes control functionalities across different cell populations within a microbial consortium. This approach leverages the division of labor and cooperative interactions among microbial populations, significantly enhancing flexibility and robustness. We build on the foundation of synthetically engineered microbial consortia, known for their applications in complex compound production and advanced computational processes. However, the challenge of implementing a distributed feedback control loop for precise phenotype regulation in synthetic communities has remained unaddressed. Our work closes this gap by developing and validating an in-vivo feedback control loop distributed across a bacterial consortium, specifically designed using two Escherichia coli strains. These strains are engineered to perform the three essential control functions: computation, sensing, and actuation. We employ orthogonal quorum sensing signaling molecules for communication between the controller and target cell populations. Through in-vivo experiments, we compare closed-loop and open-loop configurations to demonstrate the effectiveness of our approach. The results show that our distributed control system can regulate gene expression levels in target populations with high precision and robustness, even in the presence of consortium composition variations. This feature is particularly vital for practical applications in synthetic biology where stability and adaptability are crucial. Our study not only addresses a significant gap in synthetic biology literature but also opens avenues for more complex and reliable synthetic biology applications.

synthetic biology↗

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↗

bioRxiv2020

ChipSeg: an automatic tool to segment bacteria and mammalian cells cultured in microfluidic devices

Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. While such image segmentation applications have been previously reported, there is in the literature a lack of open-source and documented code for the community. We describe ChipSeg, a computational tool to segment bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy. The method is based on thresholding and uses the same core functions for both cell types. It allows to segment individual cells in high cell-density microfluidic devices, to quantify fluorescence protein expression over a time-lapse experiment and to track individual cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customised for other experimental settings and research aims.

synthetic biology↗

bioRxiv2020

Testing Theoretical Minimal Genomes Using whole-cell Models

The minimal gene set for life has often been theorised, with at least ten produced for Mycoplasma genitalium (M. genitalium). Due to the difficulty of using M. genitalium in the lab, combined with its long replication time of 12 - 15 hours, none of these theoretical minimal genomes have been tested, even with modern techniques. The publication of the M. genitalium whole-cell model provided the first opportunity to test them, simulating the genome edits in-silico. We simulated eight minimal gene sets from the literature, finding that they produced in-silico cells that did not divide. Using knowledge from previous research, we reintroduced specific essential and low essential genes in-silico; enabling cellular division. This reinforces the need to identify species-specific low essential genes and their interactions. Any genome designs created using the currently incomplete and fragmented gene essentiality information, will very likely require in-vivo reintroductions to correct issues and produce dividing cells.

synthetic biology↗

bioRxiv2020

In vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli using Microfluidics

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parameterize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

synthetic 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↗

bioRxiv2019

A low cost, open source Turbidostat design for in-vivo control experiments in Synthetic Biology

To characterise the dynamics of new engineered systems in Synthetic biology, continuous culture platforms are required. In this paper, after a brief review of the existing machines present in literature, we describe the design and the implementation of a new flexible and low cost turbidostat for in-vivo control experiments. Then, the results of a 3 hours long experiment of control of the Optical Density is reported. Since the foundation of our design is flexibility, in this work we also discuss some possible extensions of our design, with particular attention to their application to validate in-vivo multicellular control design.

synthetic 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↗