Biology subjects

Golden, M.

Publications and source records attributed to Golden, M..

3 recordsLinked to original sources

bioRxiv2024

Spatio-temporal segmentation of contraction waves in the extra-embryonic membranes of the red flour beetle

In this paper, we introduce an image analysis approach for spatio-temporal segmentation, quantification and visualization of movement or contraction patterns in 2D+t or 3D+t microscopy recordings of biological tissues. The imaging pipeline is applied to time lapse images of the embryonic development of the red flour beetle Tribolium castaneum recorded with light-sheet fluorescence microscopy (LSFM). We are particularly interested in the dynamics of extra-embryonic membranes, and provide quantitative evidence of the existence of contraction waves during late stages of development. These contraction waves are a novel observation of which neither origin, nor function are yet known. The proposed pipeline relies on particle image velocimetry (PIV) for quantitative movement analysis, surface detection, tissue cartography, and an algorithmic approach to detect characteristic movement dynamics. This approach locates contraction waves in 2D+t and 3D+t reliably and efficiently and allows the automated quantitative analysis, such as the area involved in the contractile behavior, contraction wave duration and frequency, path of contractile area, or the relation to the spatio-temporal velocity distribution. The pipeline will be used in the future to conduct a large-scale characterization and quantification of contraction wave behavior in Tribolium castaneum development and can be adapted easily to the identification and segmentation of characteristic tissue dynamics in other systems of interest.

bioinformatics↗

bioRxiv2020

A Network Medicine Approach to Drug Repurposing for Chronic Pancreatitis

Despite decades of clinical investigations, there is currently no effective treatment for patients diagnosed with Chronic Pancreatitis (CP). Computational drug repurposing holds promise to rapidly identify therapeutics which may prove efficacious against the disease. Using a literature-derived knowledge graph, we train multiple machine learning models using embeddings based on i) the network topology of regulation bipartite networks, ii) protein primary structures and iii) molecule substructures. Using these models, we predict approved drugs that down-regulate the disease, and assess their proposed respective drug targets and mechanism of actions. We analyse the highest predicted drugs and find a diverse range of regulatory mechanisms including inhibition of fibrosis, inflammation, immmune response, oxidative stress and calcium homeostasis. Notably, we identify resiniferatoxin, a potent analogue of capsaicin, as a promising repurposable candidate due to its antiinflammatory properties, nociceptive pain suppression, and regulation of calcium homeostatis (through potentiation of mutant cystic fibrosis transmembrane conductance regulator (CFTR)). Resiniferatoxin may also regulate intracellular acinar Ca2+ via agonism of transient receptor potential vanilloid subfamily member 6 (TRPV6). We believe the potential of this repurposable drug warrants further in silico and in vitro testing, particularly the affect of the TRPV6 agonism on disease pathogenesis.

bioinformatics↗

bioRxiv2018

Long-read amplicon denoising

Long-read next generation amplicon sequencing shows promise for studying complete genes or genomes from complex and diverse populations. Current long-read sequencing technologies have challenging error profiles, hindering data processing and incorporation into downstream analyses. Here we consider the problem of how to reconstruct, free of sequencing error, the true sequence variants and their associated frequencies. Called \"amplicon denoising\", this problem has been extensively studied for short-read sequencing technologies, but current solutions do not appear to generalize well to long reads with high indel error rates. We introduce two methods: one that runs nearly instantly and is very accurate for medium length reads (here ~2.6kb) and high template coverage, and another, slower method that is more robust when reads are very long or coverage is lower.\n\nOn one real dataset with ground truth, and on a number of simulated datasets, we compare our two approaches to each other and to existing algorithms. We outperform all tested methods in accuracy, with competitive run times even for our slower method.\n\nFast Amplicon Denoising (FAD) and Robust Amplicon Denoising (RAD) are implemented purely in the Julia scientific computing language, and are hereby released along with a complete toolkit of functions that allow long-read amplicon sequence analysis pipelines to be constructed in pure Julia. Further, we make available a webserver to dramatically simplify the processing of long-read PacBio sequences.

bioinformatics↗