The lab has been awarded an R01 grant (“Measuring and perturbing metabolic rhythms and the cell division cycle in single cells”) by the NIGMS within the NIH.  This builds upon our expertise in single cell analysis and the yeast metabolic cycle.

Efficient analysis of stochastic gene dynamics in eukaryotic cells

screen-shot-2018-01-31-at-8-56-49-amMolecular events arise from random collisions between molecules, which cause cell-to-cell variability in gene expression over time.  This is especially true for processes involving small numbers of molecules (e.g. DNA) or slow processes (e.g. protein binding to DNA), where the systems cannot average over a large ensemble or over time.  Here, we present a method of analysis, known as a piecewise deterministic Markov process, that accurately describes stochastic gene dynamics in the limit of large mRNA and protein levels (i.e. eukaryotic cells).  We use this method to provide modeling insights on several titration-based oscillators commonly found in circadian clocks and immune signaling. This has been a wonderful long-distance collaboration with Yen Ting Lin, a Fellow at the Center for Nonlinear Studies at Los Alamos National Laboratory.  Read more about his research here!

Lin YT, Buchler NE. Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processesJ. R. Soc. Interface (2018)

nsf_logoThe lab has been awarded an NSF grant (“Determine mechanisms of rewiring of the eukaryotic cell cycle by a virus without disrupting network function”) by the Developmental cluster within the Division of Integrated Organismal Systems (IOS).  This builds upon and continues a multi-PI effort with Raluca Gordan, whose lab is adjacent to ours in the Duke Center for Genomic and Computational Biology.