Ben D. MacArthur

Self-renewal without niche instruction, feedback or fine-tuning

This paper demonstrates that stem cell self-renewal can occur without niche feedback or fine-tuning through a dimerization cycle mechanism.

Charting cellular differentiation trajectories with Ricci flow

This paper applies Ricci flow to model network rewiring during cellular differentiation using single-cell RNA-seq data, showing that network entropy and Ricci curvature provide complementary insights.

An information-theoretic approach to single cell sequencing analysis

The paper quantifies gene expression heterogeneity in single-cell sequencing data and shows it relates to cell type, decomposing it into inter- and intra-cluster components.

Universal principles of lineage architecture and stem cell identity in renewing tissues

This paper develops a framework to understand lineage architecture and stem cell identity using network science, showing that lineage structures arise from homeostasis and that stemness is context-dependent.

TNF signalling fine-tunes Langerhans cell transcriptional programmes mediating adaptive immunity.

The paper identifies TNF-driven transcriptional programs in Langerhans cells that regulate immunogenic and tolerogenic responses through IRF1 and other transcription factors.

Morse-clustering of a Topological Data Analysis Network Identifies Phenotypes of Asthma Based on Blood Gene Expression Profiles

The paper uses a discrete Morse theory clustering algorithm on a TDA network of gene expression data to identify nine molecular phenotypes associated with asthma and glucocorticoid receptor signaling differences.

Mapping biology from mouse to man using transfer learning

This paper uses transfer learning to map biology from mouse to human bone marrow using single-cell RNA-sequencing data, showing improved accuracy with retraining and zero-shot learning.

Single cell transcriptomic analysis identifies Langerhans cells immunocompetency is critical for ability to induce tolerogenic T cells.

The paper identifies that Langerhans cells (LCs) upregulate a tolerogenic gene module during migration, which is critical for inducing regulatory T cells.

GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data

GenePy estimates gene-level pathogenicity from whole-exome sequencing data by combining mutational burden and biological impact, demonstrating improved sensitivity in identifying causal genes in common diseases.

Machine learning of stem cell identities from single-cell expression data via regulatory network archetypes

The paper uses machine learning to identify regulatory network archetypes in single-cell data, revealing distinct cell states in mouse embryonic stem cells.

Stem cell differentiation is a stochastic process with memory

This study shows that stem cell differentiation involves stochastic transitions through hidden microstates, revealing epigenetic memory and a statistical mechanics framework for cellular identity.

Noise-processing by signaling networks

This study develops a mathematical framework to analyze how signaling network structures influence noise processing and finds that sparse, directional networks are better at distinguishing signals from noise.