Open Seminars of the Research Group on

 

Stochasticity and Control in the Dynamics and Diversity of Immune Repertories: an Example of Multi-Cellular Co-Operation

 

Wednesday, May 3, 2017, 12:00

at the Israel Institute for Advanced Studies, Room 128

 

 

Aspects of stochastic population dynamics in gene expression and cellular decision making

 

Michael Assaf (HUJI)

 

Cellular processes do not follow deterministic rules; even in identical environments, genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations, both intrinsic and extrinsic, present in the biomolecular interaction networks. While for low-copy-number biomolecules intrinsic noise dominates, for high copy-numbers, extrinsic noise has been experimentally shown to dominate the variation. In this talk, I will review some recent works dealing with the stochastic dynamics of simple gene network motifs, focusing on those motifs that can be viewed as the building blocks of genetic switches.

I will dedicate the first part of the talk to presenting an analytical method for calculating statistics of large deviations in such systems, under intrinsic noise only. A notable example is the mean switching time between different (metastable) phenotypic states. The method will be demonstrated on two prototypical examples of genetic switches: a positive-feedback-based self-regulating gene, and a negative-feedback-based genetic toggle switch.

The second part of the talk will focus on the combined effect of intrinsic and extrinsic noise on simple gene expression motifs. I will present a theoretical framework that allows incorporating extrinsic noise to these systems by modeling bounded extrinsic noise as an auxiliary species in the master equation. The role of the extrinsic noise properties (magnitude, correlation time, and distribution) on the statistics of interest will be explored, and the effect of fluctuations in different reaction rates will be compared. Due to its analytical nature, our formalism can be used to improve the interpretation of data from single-cell gene expression experiments.

 

 

Related Research Questions

 

  1. Why do individual identical cells behave differently under the same environment?
  2. How accurate are cellular decisions in a given environment?
  3. How can a cell population increase its robustness?

 

Suggested Reading

 

Stochastic gene expression in a single cell. 
Elowitz, MB, Levine, AJ, Siggia, ED, Swain, PS (2002).
Science 297:1183–6.
 
Quantifying E coli proteome and transcriptome with single-molecule sensitivity in single cells. 
Taniguchi, Y, Choi, PJ, Li, GW, Chen, H, Babu, M, Hearn, J, Emili, A, Xie, XS (2010).
Science 329:533–8.
 
Colored extrinsic fluctuations and stochastic gene expression.
Shahrezaei, V, Ollivier, JF, Swain, PS (2008). 
Mol Syst Biol 4:196

 

 

> back to seminars