Systems biology of interacting oscillators: Our lab studies the systems biology of interacting oscillators in yeast, where metabolic cycles interact with the cell cycle within single cells. We measure and perturb the metabolic cycle and cell cycle to understand how and why these oscillators interact in budding yeast. We also study the interaction of redox rhythms with the circadian clock in plants through collaboration.
- Burnetti AJ, Aydin M, Buchler NE. Cell cycle Start is coupled to entry into the yeast metabolic cycle across diverse strains and growth rates. Mol. Biol. Cell 27: 64-74 (2016)
- Zhou M, Wang W, Karapetyan S, Mwimba M, Marques J, Buchler NE, Dong X. Redox rhythm reinforces circadian clock to gate immune response. Nature 523: 472-476 (2015)
Evolution of the eukaryotic cell cycle: The cell cycle network and its associated dynamic properties in yeasts and animals are highly conserved, although individual proteins performing similar network roles can be unrelated. For example, our lab recently showed that a horizontally-transferred protein (SBF) integrated into the G1/S regulatory network and eventually replaced the original transcription factor (E2F) in the ancestor of most Fungi without disrupting the cell cycle. Some early-diverging Fungi have a hybrid regulatory network with both SBF and E2F transcription factors. Our lab is working with Spizellomyces punctatus, an early-diverging fungus known as a Chytrid, to understand the evolution of the eukaryotic cell cycle.
- Medina EM, Turner JJ, Gordan R, Skotheim JM, Buchler NE. Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi. eLife 5:e09492 (2016)
- Liban TJ, Medina EM, Tripathi S, Sengupta S, Henry RW, Buchler NE, Rubin SM. Conservation and divergence of C-terminal domain structure in the retinoblastoma protein family. Proc. Natl. Acad. Sci. USA 114: 4942 (2017)
Tools and methods to measure dynamics in single-cells: Transcription is stochastic and cell-to-cell variation in gene expression across a clonal population is a fact of life. We develop tools and methods to measure gene dynamics in single cells (where the action is happening) and, thus, circumvent the population-averaging and masking that occurs with standard bulk assays.
- Gomez-Schiavon M, Chen LF, West AE, Buchler NE. BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells. Genome Biology 18: 164 (2017)
- Mazo-Vargas A, Park H, Aydin M, Buchler NE. Measuring fast gene dynamics in single cells using timelapse luminescence microscopy. Mol. Biol. Cell 25: 3699-3708 (2014)