“Decoding gene expression dynamics from RNA and protein count data”

Host: Prof. Rastko Sknepnek 

Venue:  Sir Kenneth & Lady Noreen Murray Seminar Room, CITR 284

Abstract 

Counting RNA and protein molecules serves to identify cell state and its function, yet rarely this information is utilized to infer the underlying dynamical processes that produced the data. In this talk, I will present my long-term efforts to understand the dynamics of gene expression through the integration of mechanistic models with diverse count datasets. Starting with mRNA translation, I will present a computational framework designed to infer the dynamics of protein synthesis from polysome and ribosome profiling data [1]. Transitioning to RNA transcription, I will demonstrate the efficacy of employing queueing theory to effortlessly solve numerous stochastic gene expression models [2,3,4], allowing for fast and accurate inference of kinetic parameters from RNA count data [5]. 

1.  J. Szavits-Nossan and L. Ciandrini, Inferring efficiency of translation initiation and elongation from ribosome profiling, Nucleic Acids Research 48(17), 9478-9490 (2020) 
2.  J. Szavits-Nossan and R. Grima, Steady-state distributions of nascent RNA for general initiation mechanisms, Phys. Rev. Research 5, 013064 (2023) 
3.  A. G. Nicoll, J. Szavits-Nossan, M. R. Evans, R. Grima, Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression, 
   biorxiv:2023.12.30.573521 (2023) 
4.  J. Szavits-Nossan and R. Grima, Solving stochastic gene expression models using queueing theory: a tutorial review, Biophysical Journal 123(9), 1034-1057 (2024) 
5. Y. Wang, J. Szavits-Nossan, Z. Cao and R. Grima, Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference, bioRxiv 2024.04.29.591679 (2024)