Biology subjects

Grierson, C. S.

Publications and source records attributed to Grierson, C. S..

6 recordsLinked to original sources

bioRxiv2025

Characterization of recombinase-based genetic parts and circuits using nanopore sequencing

Recombinases are versatile enzymes able to perform the precise insertion, deletion, and rearrangement of DNA and can act as a foundation for programmable genetic logic and memory. Fundamental to their use are accurate measurements of function. However, these are often laborious, time-consuming, and costly to collect. To address this, we developed a semi-automated workflow that combines low-cost liquid handling robotics, multiplexed long-read nanopore sequencing, and a supporting computational analysis tool to enable the high-throughput and detailed characterization of recombinase parts and circuits when used in a variety of contexts and organisms. Our approach overcomes the limitations of typically used fluorescence-based assays and is able to monitor temporal dynamics, observe structural changes at a nucleotide resolution, and unravel the internal workings of complex multi-state circuits. The ability to scale-up and automate genetic circuit characterization is an essential step towards more rigorous biological metrology that can support the construction of predictive models for efficiently engineering biology.

synthetic biology↗

bioRxiv2023

Accelerated design of Escherichia coli genomes with reduced size using a whole-cell model and machine learning surrogate

Whole-cell models (WCMs) are multi-scale computational models that aim to simulate the function of all genes and processes within a cell. This approach is promising for designing genomes tailored for specific tasks. However, a limitation of WCMs is their long runtime. Here, we show how machine learning (ML) surrogates can be used to address this limitation by training them on WCM data to accurately predict cell division. Our ML surrogate achieves a 95% reduction in computational time compared to the original WCM. We then show that the surrogate and a genome-design algorithm can generate an in silico reduced E. coli cell, where 40% of the genes included in the WCM were removed. The reduced genome is validated using the WCM and interpreted biologically using gene ontology analysis. This approach illustrates how the holistic understanding gained from a WCM can be leveraged for synthetic biology tasks while reducing its runtime.

bioengineering↗

bioRxiv2020

Harnessing the central dogma for stringent multi-level control of gene expression

Strictly controlled inducible gene expression is crucial when engineering biological systems where even tiny amounts of a protein have a large impact on function or host cell viability. In these cases, leaky protein production must be avoided at all costs, but ideally without affecting the achievable range of expression. Here, we demonstrate how the central dogma offers a simple way to effectively address this challenge. By simultaneously regulating both transcription and translation, we show how relative basal expression of an inducible system can be greatly reduced, with minimal impact on the maximum induced expression rate. Using this approach, we create several stringent expression systems displaying >1000-fold change in their output after induction in vivo and up to a 350-fold change when used in a cell-free expression system. Furthermore, we find that multi-level regulation is able to suppress transcriptional noise and creates a digital-like switch when transitioning between on and off states. This work provides foundational knowledge and a genetic toolkit of parts to create multi-level gene expression controllers for those working with toxic genes or requiring precise regulation and propagation of cellular signals. It also demonstrates the value of exploring more complex and diverse regulatory designs for synthetic biology.

synthetic biology↗

bioRxiv2020

Cheetah: a computational toolkit for cybergenetic control

Advances in microscopy, microfluidics and optogenetics enable single-cell monitoring and environmental regulation and offer the means to control cellular phenotypes. The development of such systems is challenging and often results in bespoke setups that hinder reproducibility. To address this, we introduce Cheetah - a flexible computational toolkit that simplifies the integration of real-time microscopy analysis with algorithms for cellular control. Central to the platform is an image segmentation system based on the versatile U-Net convolutional neural network. This is supplemented with functionality to robustly count, characterise and control cells over time. We demonstrate Cheetahs core capabilities by analysing long-term bacterial and mammalian cell growth and by dynamically controlling protein expression in mammalian cells. In all cases, Cheetahs segmentation accuracy exceeds that of a commonly used thresholding-based method, allowing for more accurate control signals to be generated. Availability of this easy-to-use platform will make control engineering techniques more accessible and offer new ways to probe and manipulate living cells.

synthetic biology↗

bioRxiv2020

Identifying root traits affecting root-substrate adhesion in Arabidopsis using a novel centrifugation technique

The physical presence of roots and the compounds they release affect the cohesion between roots and their environment. However, the plant traits that are important for these interactions are unknown and most methods that quantify the contributions of these traits are time-intensive and require specialist equipment and complex substrates. Our lab developed an inexpensive, high-throughput phenotyping assay that quantifies root-substrate adhesion in Arabidopsis thaliana. We now report that this method has high sensitivity and versatility for identifying different types of traits affecting root-substrate adhesion including root hair morphology, vesicle trafficking pathways and root exudate composition. We describe a practical protocol for conducting this assay and introduce its use in a forward genetic screen to identify novel genes affecting root-substrate interactions. This assay is a powerful tool for identifying and quantifying genetic contributions to cohesion between roots and their environment.

plant biology↗

bioRxiv2017

Organisation of feed-forward loop motifs reveals architectural principles in natural and engineered networks

Network motifs are significantly expressed sub-graphs that have been proposed as building blocks for natural and engineered networks. Detailed functional analysis has been performed for many types of motif in isolation, but less is known about how motifs work together to perform complex tasks. To address this issue we measure the aggregation of network motifs via methods that extract precisely how these structures are connected. Applying this approach to a broad spectrum of networked systems and focusing on the widespread feed-forward loop motif, we uncover striking differences in motif organisation. The types of connection are often highly constrained, differ between domains, and clearly capture architectural principles. We show how this information can be used to effectively predict functionally important nodes in the metabolic network of Escherichia coli. Our findings have implications for understanding how networked systems are constructed from motif parts and elucidates constraints that guide their evolution.

bioinformatics↗