Computational models

  • Article
    | Open Access

    The effectiveness of digital contact tracing for COVID-19 control remains uncertain. Here, the authors use data from the Smittestopp app, used in Norway in spring 2020, and estimate that 80% of nearby devices were detected by the app, and at least 11% of close contacts were not visible to manual contact tracing.

    • Ahmed Elmokashfi
    • , Joakim Sundnes
    •  & Olav Lysne
  • Article
    | Open Access

    The number of single-cell RNA-seq datasets generated is increasing rapidly, making methods that map cell types to well-curated references increasingly important. Here, the authors propose an accurate method for mapping single cells onto a reference atlas in seconds.

    • Joyce B. Kang
    • , Aparna Nathan
    •  & Soumya Raychaudhuri
  • Article
    | Open Access

    Lassa Fever is a rodent-borne viral haemorrhagic fever that is a public health problem in West Africa. Here, the authors develop a spatiotemporal model of the socioecological drivers of disease using surveillance data from Nigeria, and find evidence of climate sensitivity.

    • David W. Redding
    • , Rory Gibb
    •  & Chikwe Ihekweazu
  • Article
    | Open Access

    Understanding the potential impacts of new variants of SARS-CoV-2 is important for pandemic planning. Here, the authors develop a model incorporating hypothetical new variants with varying transmissibility and immune evasion properties, and use it to project possible future epidemic waves in the UK.

    • Louise Dyson
    • , Edward M. Hill
    •  & Matt J. Keeling
  • Article
    | Open Access

    In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates. Here, the authors propose a continual learning approach to deal with such domain shifts occurring at unknown time points.

    • Matthias Perkonigg
    • , Johannes Hofmanninger
    •  & Georg Langs
  • Article
    | Open Access

    Developing interpretable models is a major challenge in single cell deep learning. Here we show that the VEGA variational autoencoder model, whose decoder wiring mirrors gene modules, can provide direct interpretability to the latent space further enabling the inference of biological module activity.

    • Lucas Seninge
    • , Ioannis Anastopoulos
    •  & Joshua Stuart
  • Article
    | Open Access

    Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new folds.

    • Rahil Taujale
    • , Zhongliang Zhou
    •  & Natarajan Kannan
  • Article
    | Open Access

    Rift Valley fever is a zoonotic haemorrhagic fever with complex transmission dynamics influenced by environmental variables and animal movements. Here, the authors develop a metapopulation model incorporating these factors and use it to identify the main drivers of transmission in the Comoros archipelago.

    • Warren S. D. Tennant
    • , Eric Cardinale
    •  & Raphaëlle Métras
  • Article
    | Open Access

    Scalable trajectory inference for multi-omic single cell datasets is challenging in terms of capturing non-tree complex topologies. Here the authors present a method, VIA, that scales to millions of cells across multiple omic modalities using lazy-teleporting random walks.

    • Shobana V. Stassen
    • , Gwinky G. K. Yip
    •  & Kevin K. Tsia
  • Article
    | Open Access

    Image-based simulation for obtaining physical quantities is limited by the uncertainty in the underlying image segmentation. Here, the authors introduce a workflow for efficiently quantifying segmentation uncertainty and creating uncertainty distributions of the resulting physics quantities.

    • Michael C. Krygier
    • , Tyler LaBonte
    •  & Scott A. Roberts
  • Article
    | Open Access

    Little is known about how human parental relatedness varied across ancient populations. Runs of homozygosity (ROH) in the offspring’s genome can give clues. Here, the authors present a method to identify ROH in ancient genomes and infer low rates of close kin unions across most ancient populations.

    • Harald Ringbauer
    • , John Novembre
    •  & Matthias Steinrücken
  • Article
    | Open Access

    Newly emerged pathogens are inherently difficult to forecast, due to many unknowns about their biology early in an epidemic. Here, the authors assess forecasts of a suite of models during the Zika epidemic in Colombia, finding that the models that performed best changed over the course of the epidemic.

    • Rachel J. Oidtman
    • , Elisa Omodei
    •  & T. Alex Perkins
  • Article
    | Open Access

    The intrinsic disorder of histone tails poses challenges in their characterization. Here the authors apply extensive molecular dynamics simulations of the full nucleosome to show reversible binding to DNA with specific binding modes of different types of histone tails, where charge-altering modifications suppress tail-DNA interactions and may boost interactions between nucleosomes and nucleosome-binding proteins.

    • Yunhui Peng
    • , Shuxiang Li
    •  & Anna R. Panchenko
  • Article
    | Open Access

    Forecasting models have been used extensively to inform decision making during the COVID-19 pandemic. In this preregistered and prospective study, the authors evaluated 14 short-term models for Germany and Poland, finding considerable heterogeneity in predictions and highlighting the benefits of combined forecasts.

    • J. Bracher
    • , D. Wolffram
    •  & Frost Tianjian Xu
  • Article
    | Open Access

    Mass gathering events represent a risk for transmission of SARS-CoV-2. Here, the authors describe an experimental indoor test event in which individual contacts were measured and use aerosol and epidemiological modelling to evaluate transmission risks of different types of restrictions in the arena.

    • Stefan Moritz
    • , Cornelia Gottschick
    •  & Rafael Mikolajczyk
  • Article
    | Open Access

    Reopening of universities to students following COVID-19 restrictions risks increased transmission due to high numbers of social contacts and the potential for asymptomatic transmission. Here, the authors use a mathematical model with social contact data to estimate the impacts of reopening a typical non-campus based university in the UK.

    • Ellen Brooks-Pollock
    • , Hannah Christensen
    •  & Leon Danon
  • Article
    | Open Access

    Lineage tracing and snapshots of transcriptional state at the single-cell level are powerful, complementary tools for studying development. Here, the authors propose a mathematical method combining lineage tracing with trajectory inference to improve our understanding of development.

    • Aden Forrow
    •  & Geoffrey Schiebinger
  • Article
    | Open Access

    Formulating metabolic networks mathematically can help researchers study metabolic diseases and optimize the production of industrially important molecules. Here, the authors propose a framework that allows to model eukaryotic metabolism considering gene expression and thermodynamic constraints.

    • Omid Oftadeh
    • , Pierre Salvy
    •  & Vassily Hatzimanikatis
  • Article
    | Open Access

    The interplay between human diet and the gut microbiome is complex. Here, the authors present a model of human-microbiome interaction that can predict how phenolic compounds are metabolized by the human gut microbiome, identifying diet-specific metabolites in children of varied clinical conditions.

    • Telmo Blasco
    • , Sergio Pérez-Burillo
    •  & Francisco J. Planes
  • Article
    | Open Access

    Existing methods for non-invasively monitoring water flow in plants have limited spatial/temporal resolution. Here, the authors report that Raman microspectroscopy, complemented by hydrodynamic modelling, can monitor hydrodynamics within living root tissues at cell- and sub-second-scale resolutions.

    • Flavius C. Pascut
    • , Valentin Couvreur
    •  & Kevin F. Webb
  • Article
    | Open Access

    Crossover numbers and positions are tightly controlled but the mechanism involved is still obscure. Here, the authors, using quantitative super-resolution cytogenetics and mathematical modelling, show that diffusion mediated coarsening of HEI10, an E3-ligase domain containing protein, may explain meiotic crossover positioning in Arabidopsis.

    • Chris Morgan
    • , John A. Fozard
    •  & Martin Howard
  • Article
    | Open Access

    Quantitative methods to assess the quality of hPSC-derived organoids have not been developed. Here they present a prediction algorithm to assess the transcriptomic similarity between hPSC-derived organoids and the corresponding human target organs and perform validation on lung bud organoids, antral gastric organoids, and cardiomyocytes.

    • Mi-Ok Lee
    • , Su-gi Lee
    •  & Hyun-Soo Cho
  • Article
    | Open Access

    Here the authors decode how core promoter elements regulate rate limiting steps of transcription using quantitative live imaging, genetics and modeling in early Drosophila embryos. TATA-driven promoters require one rate-limiting step while INR promoters need an extra step associated with Pol II pausing.

    • Virginia L. Pimmett
    • , Matthieu Dejean
    •  & Mounia Lagha
  • Article
    | Open Access

    Currently many of the time resolved serial femtosecond (SFX) crystallography experiments are done with light driven protein systems, whereas the reaction initiation for non-light triggered enzymes remains a major bottle neck. Here, the authors present an expanded Drop-on-Tape system, where picoliter-sized droplets of a substrate or inhibitor are turbulently mixed with nanoliter sized droplets of microcrystal slurries, and they use it for time-resolved SFX measurements of inhibitor binding to lysozyme and secondly, binding of a β-lactam antibiotic to a bacterial serine β-lactamase.

    • Agata Butryn
    • , Philipp S. Simon
    •  & Allen M. Orville
  • Article
    | Open Access

    Vo’, Italy, is a unique setting for studying SARS-CoV-2 antibody dynamics because mass testing was conducted there early in the pandemic. Here, the authors perform two follow-up serological surveys and estimate seroprevalence, the extent of within-household transmission, and the impact of contact tracing.

    • Ilaria Dorigatti
    • , Enrico Lavezzo
    •  & Andrea Crisanti
  • Article
    | Open Access

    RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.

    • Zitao Song
    • , Daiyun Huang
    •  & Jia Meng
  • Article
    | Open Access

    To benchmark single cell bioinformatics tools, data simulators can provide a robust ground truth. Here the authors present dyngen, a multi-modal simulator, and apply it to aligning cell developmental trajectories, cell-specific regulatory network inference and estimation of RNA velocity.

    • Robrecht Cannoodt
    • , Wouter Saelens
    •  & Yvan Saeys
  • Article
    | Open Access

    Despite the consensus that mass vaccination against SARS-CoV-2 will ultimately end the pandemic, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. Here, the authors investigate relaxation scenarios using an age-structured transmission model that has been fitted to data for Portugal.

    • João Viana
    • , Christiaan H. van Dorp
    •  & Ganna Rozhnova
  • Article
    | Open Access

    People can infer unobserved causes of perceptual data (e.g. the contents of a box from the sound made by shaking it). Here the authors show that children compare what they hear with what they would have heard given other causes, and explore longer when the heard and imagined sounds are hard to discriminate.

    • Max H. Siegel
    • , Rachel W. Magid
    •  & Laura E. Schulz
  • Article
    | Open Access

    Disentangling the impacts of non-pharmaceutical interventions on COVID-19 transmission is challenging as they have been used in different combinations across time and space. This study shows that, early in the epidemic, school/daycare closures and stopping nursing home visits were associated with the biggest reduction in transmission in the United States.

    • Bingyi Yang
    • , Angkana T. Huang
    •  & Derek A. T. Cummings
  • Article
    | Open Access

    Population-based surveys are the gold standard for estimating seroprevalence but are expensive and often only capture a small geographic area or window of time. This study describes a new platform, SCALE-IT, for serosurveillance based on algorithmic sampling of electronic health records, and uses it to estimate the seroprevalence of SARS-CoV-2 in San Francisco.

    • Isobel Routledge
    • , Adrienne Epstein
    •  & Isabel Rodriguez-Barraquer
  • Article
    | Open Access

    Technical advancements have significantly improved early diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various practical factors. Here, the authors develop an artificial intelligence assistive diagnostic solution to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria in a large multicenter study.

    • Xiaohui Zhu
    • , Xiaoming Li
    •  & Yanqing Ding
  • Article
    | Open Access

    Non-coding RNA function is poorly understood, partly due to the challenge of determining RNA secondary (2D) structure. Here, the authors present a framework for the reproducible prediction and visualization of the 2D structure of a wide array of RNAs, which enables linking RNA sequence to function.

    • Blake A. Sweeney
    • , David Hoksza
    •  & Anton I. Petrov
  • Article
    | Open Access

    Many job sectors classified as ‘essential’ have continued operating with limited restrictions during the COVID-19 pandemic, potentially placing workers at higher risk of infection. Here, the authors show that seropositivity rates in workers vary widely across and between job sectors in Geneva, Switzerland.

    • Silvia Stringhini
    • , María-Eugenia Zaballa
    •  & Idris Guessous
  • Article
    | Open Access

    Allele-specific expression in diploid organisms can be quantified by RNA-seq and it is common practice to rely on a single library. Here, the authors show that the standard approach has variable error rate and present Qllelic as a tool to improve reproducibility of allele-specific RNA-seq analysis.

    • Asia Mendelevich
    • , Svetlana Vinogradova
    •  & Alexander A. Gimelbrant
  • Article
    | Open Access

    Single-cell RNA-Seq allows us to observe snapshots of how biological systems change over time at cellular resolution. Here, the authors develop a generative framework that uses time-resolved single-cell data to model how cells change in physical time, including in response to perturbations.

    • Grace Hui Ting Yeo
    • , Sachit D. Saksena
    •  & David K. Gifford
  • Article
    | Open Access

    Multi-layered epigenetic regulation in higher eukaryotes makes it challenging to disentangle the individual effects of modifications on chromatin structure and function. Here, the authors expressed mammalian DNA methyltransferases in yeast, which have no DNA methylation, to show that methylation has intrinsic effects on chromatin structure.

    • Diana Buitrago
    • , Mireia Labrador
    •  & Modesto Orozco
  • Article
    | Open Access

    Association analyses that capture rare and noncoding variants in whole genome sequencing data are limited by factors like statistical power. Here, the authors present KnockoffScreen, a statistical method using the knockoff framework to detect, localise and prioritise rare and common risk variants at genome-wide scale.

    • Zihuai He
    • , Linxi Liu
    •  & Iuliana Ionita-Laza