Ryan York

Cells can be highly motile, moving in and out of a microscope’s field of view. Understanding complex life cycles is difficult without continuous observation. To overcome this challenge, we’ve developed a 3D-printed microchamber device to confine cells for long-term visualization.

Quantifying movement is a powerful window into cellular functions. However, cells can generate movement through a variety of complex mechanisms. Here, we generate a flexible framework for comparing an especially variable type of motility: cellular crawling.

Even with many tools available, categorizing species is tough. We used data from Raman spectroscopy, a form of label-free imaging, to infer phylogenetic patterns among several dozen diverse microbial taxa, offering a non-destructive and rapid way to dissect species relationships.

Constraining motile microorganisms for live imaging often requires costly microfluidics or optical traps to keep them in view. We used patterned stamps and agar to make versatile, inexpensive “microchambers” and offer a way to predict the right chamber size for a given organism.

We’re crossing C. reinhardtii and C. smithii algae for high-throughput genotype-phenotype mapping. In preparation, we’re comparing the parents to uncover unique species-specific phenotypes.

We want to find and use evolutionary innovations to solve present-day problems. We developed NovelTree, an efficient phylogenomic workflow that will empower us to decode the evolutionary traces of these innovations across the tree of life.

We investigated protein structure predictions as an alternative to protein sequence homology for comparing single-cell RNA-seq data across species.

It is commonly assumed that phenotypes arise from the cumulative effects of many independent genes. However, we show that by accounting for dependent and nonlinear biological relationships, we can generate models that predict phenotypes with great accuracy.

Genetic models of complex traits often rely on incorrect assumptions that drivers of trait variation are additive and independent. An information theoretic framework for analyzing trait variation can better capture phenomena like allelic dominance and gene-gene interaction.