Biology subjects

Blackwell, R.

Publications and source records attributed to Blackwell, R..

6 recordsLinked to original sources

bioRxiv2025

The Plankton Imager (Pi-10): An imaging instrument for automated and continuous zooplankton data collection

The Pi-10 is the latest iteration of the Plankton Imager: a high-speed colour line-scan camera that images particles in a flow-through system. The Pi-10 is a cost-effective, easy to install and low maintenance automated instrument that can be used on any platform with access to water and power supply. We tested the Pi-10 on the research vessel Cefas Endeavour, connected to a continuous water supply pumping water at 34 L min-1. The instrument collected images of particles, within the size range of 180 {micro}m - 3.5 cm, automatically and continuously, alongside other vessel operations, in all weathers over a period of 18 days. The Pi-10 successfully captured and saved up 5000 images per minute, translating into a 46 GB of digital storage per day. When particle density exceeded 147 per litre, the instrument stopped saving all images, while still recording the number of particles that passed through the system. This is akin to subsampling, with more sub-sampling required in areas or times of high particle density (e.g at the time of spring plankton bloom or in turbid waters). The Pi-10 collects high volumes of data in a continuous manner, thus providing unprecedented fine spatial data. The high frequency nature of the instrument opens the door to new areas of research. These include, in particular, the observation of fine scale processes, the move towards real-time sampling, and the increased capability to build a digital twin of the oceans. As technologies continue evolving the Pi-10 performance will increase, being able to collect and save more and more images.

ecology↗

bioRxiv2024

Heterogeneity in ligand-bound TRPV1: A comparison of methods in cryo-EM and molecular dynamics simulation

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for resolving the atomistic details of cellular components. In recent years, several computational methods have been developed to study the heterogeneity of molecules in single-particle cryo-EM. In this study, we analyzed a publicly available single-particle dataset of TRPV1 using five of these methods: 3D Flexible Refinement, 3D Variability Analysis, cryoDRGN, ManifoldEM, and Bayesian ensemble reweighting. Beyond what we initially expected, we have found that this dataset contains significant heterogeneity-- indicating that single particle datasets likely contain a rich spectrum of biologically relevant states. Further, we have found that different methods are best suited to studying different kinds of heterogeneity, with some methods being more sensitive to either compositional or conformational heterogeneity. We also apply a combination of Bayesian ensemble reweighting and molecular dynamics as supporting evidence for the presence of these rarer states within the sample. Finally, we developed a quantitative metric based on the analysis of the singular value decomposition and power spectra to compare the resulting volumes from each method. This work represents a detailed view of the variable outcomes of different heterogeneity methods used to analyze a single real dataset and presents a pathway to a deeper understanding of the biology of complex macromolecules like the TRPV1 ion channel.

biophysics↗

bioRxiv2023

Self-organized intracellular twisters

Life in complex systems, such as cities and organisms, comes to a standstill when global coordination of mass, energy, and information flows is disrupted. Global coordination is no less important in single cells, especially in large oocytes and newly formed embryos, which commonly use fast fluid flows for dynamic reorganization of their cytoplasm. Here, we combine theory, computing, and imaging to investigate such flows in the Drosophila oocyte, where streaming has been proposed to spontaneously arise from hydrodynamic interactions among cortically anchored microtubules loaded with cargo-carrying molecular motors. We use a fast, accurate, and scalable numerical approach to investigate fluid-structure interactions of 1000s of flexible fibers and demonstrate the robust emergence and evolution of cell-spanning vortices, or twisters. Dominated by a rigid body rotation and secondary toroidal components, these flows are likely involved in rapid mixing and transport of ooplasmic components.

biophysics↗

bioRxiv2022

TM-Vec: template modeling vectors for fast homology detection and alignment

Exploiting sequence-structure-function relationships in molecular biology and computational modeling relies on detecting proteins with high sequence similarities. However, the most commonly used sequence alignment-based methods, such as BLAST, frequently fail on proteins with low sequence similarity to previously annotated proteins. We developed a deep learning method, TM-Vec, that uses sequence alignments to learn structural features that can then be used to search for structure-structure similarities in large sequence databases. We train TM-Vec to accurately predict TM-scores as a metric of structural similarity for pairs of structures directly from sequence pairs without the need for intermediate computation or solution of structures. For remote homologs (sequence similarity [≤] 10%) that are highly structurally similar (TM-score ? 0.6), we predict TM-scores within 0.026 of their value computed by TM-align. TM-Vec outperforms traditional sequence alignment methods and performs similar to structure-based alignment methods. TM-Vec was trained on the CATH and SwissModel structural databases and it has been tested on carefully curated structure-structure alignment databases that were designed specifically to test very remote homology detection methods. It scales sub-linearly for search against large protein databases and is well suited for discovering remotely homologous proteins.

bioinformatics↗

bioRxiv2020

Protein Structural Alignments From Sequence

Computing sequence similarity is a fundamental task in biology, with alignment forming the basis for the annotation of genes and genomes and providing the core data structures for evolutionary analysis. Standard approaches are a mainstay of modern molecular biology and rely on variations of edit distance to obtain explicit alignments between pairs of biological sequences. However, sequence alignment algorithms struggle with remote homology tasks and cannot identify similarities between many pairs of proteins with similar structures and likely homology. Recent work suggests that using machine learning language models can improve remote homology detection. To this end, we introduce DeepBLAST, that obtains explicit alignments from residue embeddings learned from a protein language model integrated into an end-to-end differentiable alignment framework. This approach can be accelerated on the GPU architectures and outperforms conventional sequence alignment techniques in terms of both speed and accuracy when identifying structurally similar proteins.

bioinformatics↗

bioRxiv2019

Mechanisms of chromosome biorientation and bipolar spindle assembly analyzed by computational modeling

The essential functions required for mitotic spindle assembly and chromosome biorientation and segregation are not fully understood, despite extensive study. To illuminate the combinations of ingredients most important to align and segregate chromosomes and simultaneously assemble a bipolar spindle, we developed a computational model of fission-yeast mitosis. Robust chromosome biorientation requires progressive restriction of attachment geometry, destabilization of misaligned attachments, and attachment force dependence. Large spindle length fluctuations can occur when the kinetochore-microtubule attachment lifetime is long. The primary spindle force generators are kinesin-5 motors and crosslinkers in early mitosis, while interkinetochore stretch becomes important after biorientation. The same mechanisms that contribute to persistent biorientation lead to segregation of chromosomes to the poles after anaphase onset. This model therefore provides a framework to interrogate key requirements for robust chromosome biorientation, spindle length regulation, and force generation in the spindle.

cell biology↗