Biology subjects

Barnard, L.

Publications and source records attributed to Barnard, L..

4 recordsLinked to original sources

bioRxiv2025

Brownian DNA Computing

Conventional silicon computing is constrained by energy demands, limited parallelism, and poor compatibility with living systems, motivating the exploration of molecular alternatives. Here, we realize Brownian DNA computing, a hitherto theoretical framework, for energy efficient computation in which coupled molecular balances on a DNA origami scaffold form Brownian Logic Elements (BLEs) that harnesses thermal fluctuations for computation. By encoding multiple logic gates into a programmed energy landscape, these BLEs execute all fundamental Boolean logic gates, complex circuits such as half-adders, and multi-input operations, while also enabling non-Boolean logic with multi-valued outputs in a single compact gate. Computation arises through thermally driven exploration of a near-flat configurational energy landscape, without reliance on consumable fuel strands for cascading signal propagation, making the process fast, resettable, and compatible with operation near reversible thermodynamic limits. Transient-input protocols further allow the BLE energy landscape to be controlled quasistatically, providing a route toward reversible Brownian computation. In combination with single-molecule readout, Brownian DNA computing establishes a new foundation for molecular information processing with potential applications in biocomputing, soft robotics, and energy-efficient nanodevices.

biophysics↗

bioRxiv2020

Computationally efficient whole genome regression for quantitative and binary traits

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine learning method called REGENIE for fitting a whole genome regression model that is orders of magnitude faster than alternatives, while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes, and only requires local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives which must load genomewide matrices into memory. This results in substantial savings in compute time and memory usage. The method is applicable to both quantitative and binary phenotypes, including rare variant analysis of binary traits with unbalanced case-control ratios where we introduce a fast, approximate Firth logistic regression test. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach compared to several existing methods using quantitative and binary traits from the UK Biobank dataset with up to 407,746 individuals.

genetics↗

bioRxiv2019

Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had [≥]1 predicted loss of function carrier, and most genes (>69%) had [≥]10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

genomics↗

bioRxiv2017

Mutations in the pantothenate kinase of the malaria parasite P. falciparum confer resistance or hypersensitivity to diverse pantothenate analogues

The malaria-causing blood stage of Plasmodium falciparum requires extracellular pantothenate for proliferation. The parasite converts pantothenate into coenzyme A (CoA) via five enzymes, the first being a pantothenate kinase (PfPanK). Multiple antiplasmodial pantothenate analogues, including pantothenol and CJ-15,801, kill the parasite by targeting CoA biosynthesis/utilisation. Their mechanism of action, however, remains unknown. Here, we show that parasites pressured with pantothenol or CJ-15,801 become resistant to these analogues. Whole-genome sequencing revealed mutations in one of two putative PanK genes (Pfpank1) in each resistant line. These mutations significantly alter PfPanK activity, with two conferring a fitness cost, consistent with Pfpank1 coding for a functional PanK that is essential for normal growth. The mutants exhibit a different sensitivity profile to recently-described, potent, antiplasmodial pantothenate analogues, with one line being hypersensitive. We provide evidence consistent with different pantothenate analogue classes having different mechanisms of action: some inhibit CoA biosynthesis while others inhibit CoA-utilising enzymes.

pharmacology and toxicology↗