Volume 1 Issue 5, May 2021

The unique challenges associated with imaging a black hole motivated the development of new computational imaging algorithms. As the Event Horizon Telescope continues to expand, these algorithms will need to evolve to keep pace with the increasingly demanding volume and dimensionality of the data.

A uniform mathematical framework based on probabilistic graphical models drives the digital twin technologies towards dynamical control with real-time data.

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.

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’.

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.

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.

Raven is designed to democratize genome assembly, being a simple and efficient tool while keeping high accuracy. Using a method for detection of false overlaps based on graph drawing, it can be employed for various genome sizes.

This work proposes a probabilistic graphical model as a formal mathematical foundation for digital twins, and demonstrates how this model supports principled data assimilation, optimal control and end-to-end uncertainty quantification.

A dynamic organ-resolved model is developed by integrating metabolic and regulatory processes in type 1 diabetes, providing a depiction of network dynamics, regulation and response to perturbations in relation to variability in insulin response.

Predicting binding specificity of T-cell receptors (TCRs) and putative antigens can help improve cancer immunotherapy. Lin et al. propose RACER, which efficiently makes use of supervised machine learning to learn important molecular interactions contributing to TCR–peptide binding.

A manifold-preserving feature selection method was developed for single-cell data analysis, which selects non-redundant features to help detect rare cell populations, design follow-up studies and create targeted panels.