Reviews & Analysis

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  • In this issue, a large and multiscale whole-body model of organ-specific regulation and metabolism for type 1 diabetes is developed, providing important details on glucose and insulin dynamics.

    • Jiao Zhao
    • Hao Xu
    • Laurence Yang
    News & Views
  • Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.

    • Steven A. Niederer
    • Michael S. Sacks
    • Karen Willcox
  • A uniform mathematical framework based on probabilistic graphical models drives the digital twin technologies towards dynamical control with real-time data.

    • Omer San
    News & Views
  • The field of biomolecular modeling has thrived by exploiting state-of-the-art technological advances. In this Perspective, the role of software and hardware advances, and the disparity and synergy between knowledge-based and physics-based methods are discussed and explored.

    • Tamar Schlick
    • Stephanie Portillo-Ledesma
  • Mapping X-ray diffraction patterns to crystal structures is a comprehensive and time-consuming task for chemists and materials scientists. In a recent work, researchers developed a machine-learning tool to make this job more ‘self-driving’.

    • Wenhao Sun
    • Michael F. Toney
    News & Views
  • In this issue, a protocol for querying criminal DNA databases is developed to prevent profile discovery, therefore reducing the potential for racial bias.

    • Denise Syndercombe Court
    News & Views
  • Analyzing cellular barcoding data is challenging due to the absence of a flexible, complete and easy-to-use set of tools that can help scientists derive biological meaning from these datasets. barcodetrackR is a promising package for filling this gap in the field.

    • Jennifer E. Adair
    • Mark R. Enstrom
    News & Views
  • A limitation of data obtained from RNA-seq experiments is the presence of different types of cell expression, making it difficult to identify the contribution of cell-type composition or cell-type-specific expression. A new study addresses this problem by proposing a method for cell-type-aware analysis of RNA-seq data.

    • Dvir Aran
    News & Views
  • The computational complexity of deep neural networks is a major obstacle of many application scenarios driven by low-power devices, including federated learning. A recent finding shows that random sketches can substantially reduce the model complexity without affecting prediction accuracy.

    • Shiqiang Wang
    News & Views
  • Sharing of genomic data poses a problem due to privacy concerns and lack of individual’s choice in the matter. In this issue, researchers propose a framework for sharing data where the control lies with the individual.

    • Fida K. Dankar
    News & Views
  • A model for the electrical double layer at solid-state electrochemical interfaces is reported, shedding some light on the design and optimization of future all-solid-state Li-ion batteries.

    • Sokseiha Muy
    • Nicola Marzari
    News & Views
  • The advent of STED microscopy, which allows observation at a sub-diffraction resolution, raises a challenge in studying spatial proximities of biomolecules’ distributions. In this issue, researchers have attempted to study colocalization of molecules by employing optimal transport.

    • Shulei Wang
    • Ming Yuan
    News & Views
  • Massive datasets have been made available to enable systematic studies of gene regulation and its control via epigenetic mechanisms. In this Review, state-of-the-art computational methods used to effectively extract knowledge from these datasets are presented and discussed.

    • Michael Scherer
    • Florian Schmidt
    • Markus List
    Review Article
  • The identification of genes that are associated with developmental trajectories of cells is an important focus in single-cell transcriptomics. This issue presents Scellnetor, a resource that facilitates this task.

    • Florian Klimm
    News & Views
  • Understanding and quantifying the uncertainty of predictions from COVID-19 pandemic models is essential to inform public health decision making. This issue presents one such examination using the influential CovidSim model.

    • Kathy Leung
    • Joseph T. Wu
    News & Views
  • Obtaining a consistent taxonomy of neuron types is challenging mainly because of the high dimensionality of the datasets. Coupled autoencoders are a step forward in achieving this goal.

    • Stephane Bugeon
    News & Views
  • Quantum computing has the potential to assist with myriad tasks in science. In this Perspective, the applicability and promising directions of quantum computing in computational biology, genetics and bioinformatics is evaluated and discussed.

    • A. K. Fedorov
    • M. S. Gelfand
  • There have been substantial developments in weather and climate prediction over the past few decades, attributable to advances in computational science. The rise of new technologies poses challenges to these developments, but also brings opportunities for new progress in the field.

    • Peter Bauer
    • Peter D. Dueben
    • Nils P. Wedi
  • The mechanisms facilitating evolutionary adaptation to future challenges are difficult to establish experimentally. Recent computational simulations of 200 cell populations indicate how evolution can hide useless genetic switches with capacity for later use.

    • Gábor Balázsi
    News & Views
  • The nature of biological networks still brings challenges related to computational complexity, interpretable results and statistical significance. Recent work proposes a new method that paves the way for addressing these issues when analyzing cancer genomic data.

    • Hanna Najgebauer
    • Umberto Perron
    • Francesco Iorio
    News & Views