Michael Sheinman

This paper develops a Bayesian framework to accurately infer cell-size regulation strategies by accounting for noise and variability, showing that previous methods were biased and that adder-like regulation is more consistent across organisms.

This study improves the estimation of evolutionary distances and tree inference by accounting for substitution rate heterogeneity across prokaryotic genomes.

This study develops a framework to separate measurement noise from biological sources of growth rate variability in single-cell data, finding continuous noise in bacteria and mammals with distinct patterns of division-associated perturbations.

The paper re-examines mRNA-protein correlations in E. coli, develops a theoretical framework accounting for cell growth and division, and validates it with simulations and data.

The paper models horizontal gene transfer as a stochastic process over a bacterial genus network and uses match length distributions to infer transfer patterns and network topology.

The paper derives an analytical formula for the distribution of distances between heterozygous sites in diploid species to infer demographic history without simplifying assumptions.

The paper uses genomic profiling to show that some invasive breast cancers are clonally unrelated to their preceding DCIS, suggesting DCIS may act as both a precursor and independent risk factor.

The paper identifies over 100,000 horizontal gene transfer events across 90,000 bacterial genomes, revealing frequent long-distance genetic exchange and its functional impact.

This study examines how DNA oxidation affects mutation rates in genomic sequences across different timescales, finding that long Gn tracks experience significantly higher G→T transversion rates.

The paper compares the statistical distribution of exact sequence matches between paralogous and orthologous gene pairs, showing they contribute differently to power-law tails in genomic sequences.