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Brain-wide recordings in mice reveal that prior expectations are distributed through recurrent loops across all levels of cortical and subcortical processing.

This Bayesian statistical method uses timeseries microbiome data to infer interaction modules and is tested using a faecal transplant experiment in mice.

In this work, the authors analyze Bayesian multimodel inference (MMI) to address the problem of making predictions when multiple mathematical models of a biological system are available. MMI combines predictions from multiple models to increase predictive certainty.

A global model compiling seabird surveys finds that 37 seabird species and around 25% of the world’s tropical seabird individuals nest on atolls, where they act as major nutrient pumps within tropical oceans.

Studying the impact of SARS-CoV-2 on organelle dynamics may shed more light on the mechanisms of viral replication. Here, the authors combine label-free holotomographic microscopy with AI to study subcellular changes and organelle dynamics upon SARS-CoV-2 infection.

SourceTracker finds the proportion and origin of contaminants in a given sample. Its database will prove useful in screening of metagenomic datasets for contaminants.

Single-cell analysis of multi-condition cohorts requires modelling the interaction between sample variables and cell states. Here, authors develop GEDI to enable integration, cluster-free differential expression analysis and regulon analysis for both gene expression and alternative splicing modalities.

An outbreak of Mpox in the UK began in May 2022 and peaked in July. In this modelling study, the authors show that the decline in cases was likely due to behavioural changes among high-risk populations, whilst vaccination could prevent a rebound.

Long-term high-resolution data on social relationships, space use and microhabitat in a wild population of mice (Apodemus sylvaticus), accompanied by sampling of the gut microbiota, show that distinct sets of microorganisms dominate social and environmental transmission routes of microbiota. Microorganisms with low oxygen tolerance are more reliant on social transmission.

A method that infers gene networks and rate parameters directly from single-molecule fluorescence in situ hybridization RNA snapshot data is proposed and demonstrated on synthetic and real data, providing insights on data from S. cerevisiae and E. coli.

The authors describe a new cephalopod from the Carboniferous (Mississippian) Bear Gulch Lagerstätte of Montana, USA. This specimen extends the fossil record of vampyropods back by ~82 million years and changes our understanding of their evolution.

Outbreaks of Crown-of-Thorn Starfish (CoTS) have caused coral cover declines across the Indo-Pacific. Here the authors analyse long-term CoTS, coral reef fish monitoring, and fisheries catch data from the Great Barrier Reef to demonstrate removal of predatory fish as a contributor to CoTS outbreaks.

While temperature impacts the function of all cellular components, it’s hard to rule out how the temperature dependence of cell phenotypes emerged from the dependence of individual components. Here, the authors develop a Bayesian genome scale modelling approach to identify thermal determinants of yeast metabolism.

Gene regulatory network inference is a topical problem in systems biology. Here, the authors presents BINGO, a powerful method for network inference from time series data.

Here the authors explore the distributional differences expected from distinct biophysical models of transcription and show how measurements from single-cell genomics experiments can shed light on the underlying biological processes.

Sixty years ago it was suggested that the sickle cell disease mutation survives because the heterozygous genotype confers resistance to malaria, resulting in correlation of the two geographical distributions. The authors use a new global assembly of sickle allele frequencies to support this hypothesis at the global scale.

Owls accurately localize sound sources near the center of gaze, but systematically underestimate peripheral source directions. Here the authors demonstrate that this behavior is predicted by statistical inference and show that the owl's map of auditory space decoded by a population vector is consistent with the behavioral model.

Changes in protein subcellular localization can be determined using mass spectrometry. Here, the authors present a statistical approach to determine relocalising proteins from spatial proteomics experiments.

A Bayesian method is presented for unbiased estimation of timescales from different types of experimental data; the method quantifies the estimation uncertainty and allows for comparing the alternative hypotheses on the underlying dynamics.

While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

This mathematical modelling study projects the dynamics of SARS-CoV-2 in England until the end of 2022 assuming that the Omicron BA.2 sublineage remains dominant. They show that booster vaccination was highly effective in mitigating severe outcomes and that future dynamics will depend greatly on assumptions about waning immunity.

Synthetic Biology often lacks the predictive power needed for efficient bioengineering. Here the authors present ART, a machine learning and probabilistic predictive tool to guide synthetic biology design in a systematic fashion.

Dengue and Zika virus are closely related flaviviruses but can have contrasting transmission dynamics in the same populations. Here, the authors use a model combining serological, surveillance and viral sequence data to explain differences in transmission dynamics in Fiji.

During the last Ebola virus outbreak in West Africa, a large amount of viral genomic data was obtained. Here, Dellicour et al. use phylodynamic approaches to assess effect of intervention strategies such as border closures.

High-throughput sequencing and analyzes of antibody repertoire provide important information on immune responses, but current methodologies are limited in sequence assembly precision and haplotype inference validity. Here the authors propose a new Bayesian haplotyping method, and attest its broad application with a large, multi-individual dataset.

Rising rates of peanut allergy pose a public health problem. Here, the authors profile blood transcriptomes during double-blind, placebo-controlled oral challenge in peanut-allergic children to identify gene and cell composition changes, and construct causal networks to detect key allergic reaction drivers.

Global patterns of gene transcription can be represented with reduced dimensionality. Here, the authors devise a method called Tradict that learns and uses 100 marker genes to predict transcriptome-wide pathway expression levels and patterns that reflect cell activity and state.

During atherosclerosis, endothelial cells release purines in response to oxidized phospholipids. Here, Hitzel et al. show that oxidized phospholipids activate an MTHFD2-regulated gene network in endothelial cells which reprograms amino acid metabolism towards production of purines and thus compensates for their loss.

Sounds are generated by interactions between objects in the world. In this Review, Winkler and Denham describe how representations of sources and actions enable humans to segment complex auditory experience into meaningful units.

A Bayesian procedure overcomes challenges in single-cell RNA-seq data normalization.

Octopus detects germline and somatic variants with high sensitivity and accuracy.

This protocol describes procedures for learning cellular and molecular processes from single-cell RNA-sequencing data using the non-negative matrix factorization algorithm Coordinated Gene Activity across Pattern Subsets. Parallel analysis is demonstrated in Python, R and GenePattern Notebook.

Eric Schadt and colleagues report a Bayesian method to predict individual SNP genotypes based on RNA expression data. Using simulations and empirical data sets, they show that it is possible to infer a genotypic barcode specific to an individual, although the identification of an individual as a participant in a study is limited by factors such as the availability of large-scale expression quantitative trait loci (eQTLs) and expression data sets.

This protocol addresses the need to define informative priors to apply ensemble modeling in systems biology. The protocol collects parameters, assesses their plausibility and creates log-normal probability distributions for use as informative priors.

Molecular changes in neurodegeneration occur much earlier than previously expected. In this study, dynamic molecular network analysis of iPSC differentiation uncovers a temporal pathway from histone to ISG15 with the earliest molecular changes of SCA1.

A network analysis on deconvoluted bulk transcriptomic data from human Alzheimer’s disease cohorts identifies several potential key disease drivers, including JMJD6.

An analysis of eye movements and EEG activity during a smooth pursuit eye movement task suggests that the process of reliability-based optimal information integration occurs in sensorimotor behaviors.