Ben D. MacArthur

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

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.

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.

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.

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

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.

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.

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

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.

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

This study uses single-cell profiling and deep learning to show that hematopoietic stem cell self-renewal declines with age due to stochastic division patterns and prior division history.

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

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.