Weini Huang

This paper introduces ecEvol, a method to infer ecDNA emergence time and other parameters from bulk whole-genome sequencing data, finding that ecDNA typically emerges early and is associated with poorer survival.

This study uses a stochastic gene expression model to quantify mRNA heterogeneity in cancer cells with extrachromosomal DNA and shows similar transcriptional parameters across different cell lines and amplification types.

This study investigates how genotype-to-phenotype architecture influences biodiversity dynamics in multilayer networks, showing that correlated and modular architectures affect diversity under different ecological conditions.

This study models prostate cancer emergence using compartmental stochastic simulations to show how luminal compartment dynamics drive rapid tumor growth, while basal compartment tumors are more aggressive and persistent.

This study investigates the spatial-temporal evolution of extrachromosomal DNA (ecDNA) in glioblastoma, showing that EGFR ecDNA drives tumor evolution and identifies a potential therapeutic window for early intervention.

This study shows that ecDNA mis-segregation leads to micronuclei formation, reduced oncogene transcription, and improved patient survival in neuroblastoma.

This study develops computational models to show that mixed ecDNA subpopulations in tumour cells require variant switching and a positive fitness of ecDNA+ cells, not direct co-selection.

The paper quantifies joint fixation events of neutral mutations in constant populations, introducing the concept of mutation waves and deriving the Wave Frequency Distribution.

This study presents an individual-based model of cancer-immune coevolution, showing how mutation accumulation and immune interactions influence tumour progression outcomes.

This study uses an individual-based model to show that parasite-mediated selection influences species coexistence and genetic diversity in predator-prey-parasite systems.

LiquidCNA tracks subclonal evolution from liquid biopsies using somatic copy number alterations, revealing new subpopulations in metastatic lung cancer.

This paper shows that spatial tumour structure affects mutation patterns in sequencing data and introduces a framework to account for these effects in cancer genomic analysis.

This study measures single-cell division rates and survival in human cancers using multi-region sequencing data, revealing per-cell mutation and survival rates across different cancer types and healthy tissues.