Biology subjects

Towers, S.

Publications and source records attributed to Towers, S..

5 recordsLinked to original sources

bioRxiv2025

Microscopic PhotoSelection (MiPS) of single cells in mother machine microfluidic devices

Techniques for selecting or sorting single cells within large populations of genetic variants are central to synthetic biology and biotechnology. Widely-used methods such as Fluorescence Activated Cell Sorting (FACS) enable rapid processing of large libraries, but are restricted to low-dimensional measurements taken at a single time point. As a result, sorting based on dynamic or multi-trait phenotypes--such as transient properties and properties that occur on fast timescales or in response to dynamic actuating signals--remains fundamentally challenging. Here we introduce Microscopic PhotoSelection (MiPS) which employs an automated robotic platform for single-cell selection based on multiple dynamic criteria, directly on microfluidic mother machine devices. The system couples long-term single-cell imaging with real-time analysis and selective optical targeting, allowing fully automated enrichment using high-intensity UV light or alternative wavelengths with the addition of photosensitisers. By targeting many cells in parallel, our platform overcomes throughput limitations of existing microfluidic-based selection technologies such as optical tweezers or droplet-based methods and provides a direct approach to select cells based on time-resolved, multi-trait phenotypes. We demonstrate the ability to perform in vivo selection and outline how iterative, feedback-based selection strategies can refine enrichment across multiple rounds. Taken together, our work establishes a high-throughput selection framework integrated into microfluidic devices, enabling applications in directed evolution, biosensor optimisation, circuit engineering, and diagnostics, where selection based on dynamic, multi-trait phenotypes is essential.

bioengineering↗

bioRxiv2025

Sequence-free landscape inference for directed evolution

Directed evolution is a method for engineering biological systems or components, such as proteins, wherein desired traits are optimised through iterative rounds of mutagenesis and selection of fit variants. The process of protein directed evolution can be envisaged as navigation over high-dimensional landscapes with numerous local maxima. The performance of any strategy in navigating such a landscape is dependent on the ruggedness of that landscape. However, this information is generally unavailable at the outset of an experiment, and cannot currently be computed using analytical methods. Here we propose SLIDE, Sequence-free Landscape Inference for Directed Evolution, which consists of two parts. First, SLIDE provides an estimation for landscape ruggedness from a mutating population using only population-level phenotypic data and an estimation of mutation rate. Ruggedness information in itself is valuable in protein design, for instance in predicting evolutionary stability. Second, SLIDE offers a framework for using the estimated ruggedness metric to select high-performing parameters for directed evolution control. Using theoretical NK landscapes and four real-world protein fitness landscapes, we demonstrate improvement upon the performance of standard selection strategies, particularly on rugged landscapes, using a pipeline that could also be combined with emerging AI-based methods for driving direction evolution.

synthetic biology↗

bioRxiv2024

Optimisation strategies for directed evolution without sequencing

Directed evolution can enable engineering of biological systems with minimal knowledge of their underlying sequence-to-function relationships. A typical directed evolution process consists of iterative rounds of mutagenesis and selection that are designed to steer changes in a biological system (e.g. a protein) towards some functional goal. Much work has been done, particularly leveraging advancements in machine learning, to optimise the process of directed evolution. Many of these methods, however, require DNA sequencing and synthesis, making them resource-intensive and incompatible with developments in targeted in vivo mutagenesis. Operating within the experimental constraints of established sorting-based directed evolution techniques (e.g. Fluorescence-Activated Cell Sorting, FACS), we explore approaches for optimisation of directed evolution that do not require sequencing information. We then expand our methods to the context of emerging experimental techniques in directed evolution, which allow for single-cell selection based on fitness objectives defined from any combination measurable traits. Finally, we validate the developed selection strategies on the GB1 empirical landscape, demonstrating that they can lead to up to a 7.5 fold increase in the probability of attaining the global fitness peak. Author summaryThe standard approach to sorting-based selection in directed evolution is to take forward only the top-performing variants from each generation of a single population. In this work, we begin to explore alternative selection strategies within a simulated directed evolution framework. We propose "selection functions", which allow us to tune the balance of exploration and exploitation of a fitness landscape, and we demonstrate that splitting a population into sub-populations can improve both the likelihood and magnitude of a successful outcome. We also propose strategies to leverage emerging selection methods that can implement single-cell selection based on any combination of measurable traits. We validate our optimised directed evolution approaches on the empirical fitness landscape of the GB1 immunoglobulin protein.

bioengineering↗

bioRxiv2024

Learning-Based Estimation of Fitness Landscape Ruggedness for Directed Evolution

Directed evolution is a method for engineering biological systems or components, such as proteins, wherein desired traits are optimised through iterative rounds of mutagenesis and selection of fit variants. The process of protein directed evolution can be envisaged as navigation over high-dimensional landscapes with numerous local maxima, mapping every possible variant of a protein to its fitness. The performance of any strategy in navigating such a landscape is dependent on several parameters, including its ruggedness. However, this information is generally unavailable at the outset of an experiment, and cannot be computed using analytical methods. Here we propose a learning-based method for estimating landscape ruggedness from a mutating population, using only population average performance data. This method uses a short period of exploration at the beginning of an experiment to predict the ruggedness, subsequently guiding the choice of high-performing parameters for directed evolution control. We then simulate this approach on two real-world protein fitness landscapes, demonstrating an improvement upon the performance of standard strategies, particularly on rugged landscapes. In addition to improving the overall outcomes of directed evolution, this method has the advantage of being readily deployable in laboratory settings, even in configurations that exclusively capture average population measures. Given the rapidly expanding application space of engineered proteins, the products of improved directed evolution are relevant in medicine, agriculture and manufacturing.

bioengineering↗

bioRxiv2019

The Rising Prevalence of Weapons in Unsafe Arming Configurations Discovered in American Airports: the Increasing Practice of Storage and Carry of Firearms with a Round Chambered

BackgroundRecent studies have shown that Americans appear to be increasingly owning and carrying firearms for personal protection, and are increasingly storing their firearms loaded. However, the prevalence of firearm carry and/or storage with a round chambered has not hitherto been studied; in this arming configuration the weapon is loaded, with a bullet resting against the firing pin, ready to fire if the trigger is pulled. Ostensibly this decreases reaction time in a threat situation, but carries an increased risk of accidental discharge. It is unknown what fraction of firearm owners typically carry or store their firearms in this configuration, and if there have been significant temporal trends in the practice.\n\nObjectiveWe examine firearms detected at Transportation Security Administration (TSA) airport security checkpoints between 2012 to 2017, to examine geospatial and temporal trends in the prevalence of unsafe arming configuration in detected firearms.\n\nResultsThe fraction of detected firearms found to be loaded has risen significantly since 2012 (Beta Binomial logistic regression p = 0.011). States with firearm child access prevention laws have significantly fewer firearms found by the TSA to be loaded (p = 0.039).\n\nThe fraction of loaded firearms found by the TSA to also have a round chambered has also risen significantly since 2012 (Beta Binomial logistic regression p < 0.001). By 2017, 36% of firearms found loaded were also found to have a round chambered, representing an apparent concerning trend in unsafe arming configurations during carry and storage.\n\nConclusionsAmericans appear to be increasingly using and storing firearms in unsafe arming configurations. TSA firearm detection data can potentially provide a key source of data when researching trends in firearm injury.

epidemiology↗