Adrian W.R. Serohijos

This study tracks microbiome succession in mice, showing a shift from strong to weak interactions linked to colonizer-dependent antimicrobial resistance.

The paper identifies how protein stability, turnover, and abundance influence the connectivity of protein-protein interaction networks in yeast.

ANYI integrates yeast PPI data with sixteen biophysical and regulatory annotations to enable cross-layer interactome analysis.

The paper analyzes gene interaction networks across polygenic traits, finding higher connectivity in trait-associated genes and weaker selective constraints in highly connected networks, and develops an online portal for hypothesis generation.

This paper shows that expressing HP1 homologues in yeast increases chromatin compaction, reducing mutation and recombination rates, and improving the stability of synthetic metabolic circuits.

The paper introduces a CRISPR-based method to tag human protein-coding genes, achieving 36% tagging efficiency in HEK 293T cells.

The paper shows that genetic interactions correlate with protein-protein interactions in yeast and human genomes, and that GI and PPI networks share overlapping topological features.

Doblin identifies dominant clonal lineages from DNA barcoding time-series data, showing accurate recovery of clone identity and fitness in simulations and applications to E. coli and gut microbiome experiments.

The paper maps genetic variation's effect on protein-protein interactions in yeast, finding trans-acting SNPs have larger effects than cis-acting ones and SNPs in non-coding regions are significant.

The paper shows that IFNβ and TNFα together induce a STAT1-independent antiviral program via non-canonical STAT2 and IRF9 pathways.

The paper estimates how much folding stability contributes to epistasis in protein evolution, finding that ~30-60% of amino acid substitutions involve epistasis.

SodaPop simulates asexual population dynamics with flexible fitness landscapes, integrating biochemical properties for protein evolution studies.

The paper estimates the mutant-selection window for antimicrobial resistance using a biophysics-based fitness model and deep mutational scanning data.