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)

Mariana defends PhD thesis

marianaMariana Gomez-Schiavon successfully defended her dissertation on “Stochastic Dynamics and Epigenetic Regulation of Gene Expression:  From Stimulus Response to Evolutionary Adaptation“. Congratulations!  Mariana is now a Computational Biology & Bioinformatics Ph.D.

Her committee was Katia Koelle (Biology), Ryan Baugh (Biology), Joshua Socolar (Physics), and Nicolas Buchler (Biology & Physics).

Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators

ATCIn theory, the unbinding rates of activators and repressors from DNA are presumed to be faster than gene expression. In practice, this assumption is not always true. Sargis Karapeytan analyzed two synthetic oscillators (activation-titration and repressor-titration) to understand the key parameters that are important for oscillations and for overcoming the molecular noise that arises from slow DNA unbinding.  Counter-intuitively, our biophysical modeling and stochastic simulations showed that slow values of DNA unbinding rate stabilized the oscillators. We also show that multiple binding sites increase the robustness of oscillations due to the buffering of DNA unbinding events. This work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, have a significant impact on the dynamics of synthetic oscillators.

Karapetyan S, Buchler NE. Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators. Phys. Rev. E 92: 062712 (2015)