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.

BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

screen-shot-2017-09-12-at-10-49-05-amSingle-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution in the measurement of the abundance and localization of nascent and mature RNA transcripts in fixed, single cells. Mariana developed a computational pipeline (BayFish) to infer the kinetic parameters of gene expression from smFISH data at multiple time points after gene induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. This has been a fun and fruitful collaboration with the Anne West lab in Neuroscience at Duke!

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)