Computer modelling articles from across Nature Portfolio

Von Economo neurons are linked to higher cognition but remain poorly understood. A study now combines Patch-seq and transcriptomics in macaques to define their molecular identity, diversity and signal-processing properties, identifying convergent specialization of projection neurons.

Multiplexed biosensing is often limited by crosstalk between sensors. Here, authors distribute sensing across microbial communities and use timer-resolved modelling and machine learning to infer multiple chemical concentrations from cross-reactive and indirect responses.

Understanding how core promoter elements influence transcription is fundamental to modeling bacterial gene regulation. Here, the authors perform a systematic and quantitative dissection showing that, while the –10 element is essential for promoter emergence from random sequences, the −35 element primarily fine-tunes promoter strength. They further demonstrate how these elements are optimized in transcription factor-regulated promoters to maximize fold change between the basal and induced states.

Building genome-scale metabolic models for less-characterized organisms remains challenging due to limited methods for reliable functional annotation. Here, the authors present AlphaGEM, a toolbox that integrates structural alignment and deep-learning approaches to improve protein relationship detection and expand metabolic networks, producing reconstructions that align with curated models across diverse species.

Bacillus subtilis biofilms locally disperse by ejecting motile cells through osmotic pressure generated by their poly-γ-glutamic acid hydrogel, a mechanism resembling that through which Cnidaria eject nematocysts.

Liu, Huang, Shen, Shao, Jing and collaborators combine single-cell transcriptomics with Patch-seq and metabolomics to dissect primate anterior insular cortex. They identify two von Economo neuron (VEN) subtypes and a VEN signal-processing strategy.

Von Economo neurons are linked to higher cognition but remain poorly understood. A study now combines Patch-seq and transcriptomics in macaques to define their molecular identity, diversity and signal-processing properties, identifying convergent specialization of projection neurons.

Artificial intelligence approaches that link patient data with chemical-induced kidney injury patterns are revolutionizing nephrotoxicity risk assessment. Substantial progress has been made in the development of integrated approaches that leverage big data, molecular profiles and toxicological understanding to identify at-risk patients, provide insights into molecular mechanisms and advance predictive nephrology.

With biomedical sciences quickly outgrowing many other application areas in terms of data generation, there is a unique opportunity for life sciences to become one of the greatest beneficiaries of research in machine learning and AI, and also inspire foundational developments in it.

In this Tools of the Trade article, Amir Giladi describes the development and use of PIC-seq, which enables the direct enrichment and analysis of conjugates of physically interacting cells in the tumour microenvironment.