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Brian Patrick Williams

Brian Patrick Williams

Dr. Brian Williams is a member of the Applied Science team. He has been at Google since 2011. He has a Masters in Physics from Imperial College and a PhD in Computer Vision from University of Oxford.

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    ProtSeq: towards high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes
    Jessica Hong
    Michael Connor Gibbons
    Ali Bashir
    Diana Wu
    Shirley Shao
    Zachary Cutts
    Mariya Chavarha
    Ye Chen
    Lauren Schiff
    Mikelle Foster
    Victoria Church
    Llyke Ching
    Sara Ahadi
    Anna Hieu-Thao Le
    Alexander Tran
    Michelle Therese Dimon
    Marc Coram
    Brian Patrick Williams
    Phillip Jess
    Marc Berndl
    Annalisa Pawlosky
    iScience, vol. 25 (2022), pp. 32
    Preview abstract We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid identifying barcodes on the chip. The end goal is that over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over 6 consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing. View details
    Evaluation of US State-Based Policy Interventions on Social Distancing Using Aggregated Mobility Data during the COVID-19 Pandemic
    Gregory Alexander Wellenius
    Swapnil Suresh Vispute
    Valeria Espinosa
    Alex Fabrikant
    Thomas Tsai
    Jonathan Hennessy
    Andrew Dai
    Brian Patrick Williams
    Krishna Kumar Gadepalli
    Adam Boulanger
    Adam Pearce
    Chaitanya Kamath
    Arran Schlosberg
    Catherine Bendebury
    Chinmoy Mandayam
    Charlotte Stanton
    Shailesh Bavadekar
    Christopher David Pluntke
    Damien Desfontaines
    Benjamin H. Jacobson
    Zan Armstrong
    Bryant Gipson
    Royce Wilson
    Andrew Philip Widdowson
    Katherine Chou
    Andrew Nathaniel Oplinger
    Tomer Shekel
    Ashish K. Jha
    Evgeniy Gabrilovich
    Nature Communications (2021)
    Preview abstract Social distancing has emerged as the primary mitigation strategy to combat the COVID-19 pandemic in the United States. However, large-scale evaluation of the effectiveness of social distancing policies are lacking. We used aggregated mobility data to quantify the impact of social distancing policies on observed changes in mobility. Declarations of states of emergency resulted in approximately a 10% reduction in time spent outside places of residence and an increase in visits to grocery stores and pharmacies. Subsequent implementation of ≥1 social distancing policies resulted in an additional 25% reduction in mobility in the following week. The seven states that subsequently ordered residents to shelter in place on or before March 23, 2020 observed an additional 29% reduction in time spent outside the residence. Our findings suggest that state-wide mandates are highly effective in achieving the goals of social distancing to minimize the transmission of COVID-19. View details
    Applying Deep Neural Network Analysis to High-Content Image-Based Assays
    Samuel J. Yang
    Scott L. Lipnick
    Nina R. Makhortova
    Subhashini Venugopalan
    Minjie Fan
    Zan Armstrong
    Thorsten M. Schlaeger
    Liyong Deng
    Wendy K. Chung
    Liadan O'Callaghan
    Anton Geraschenko
    Dosh Whye
    Marc Berndl
    Jon Hazard
    Brian Patrick Williams
    Arunachalam Narayanaswamy
    D. Michael Ando
    Philip Nelson
    Lee L. Rubin
    SLAS DISCOVERY: Advancing Life Sciences R\&D, vol. 0 (2019), pp. 2472555219857715
    Preview abstract The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control–SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes. View details
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