Stochastic steady state gain in a gene expression process with mRNA degradation control
Hiroyuki Kuwahara1, Russell Schwartz
1The Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Journal of the Royal Society, Interface
|January 13, 2012
Summary
Gene expression noise from mRNA bursting can surprisingly increase average mRNA and protein levels. This effect, driven by asynchronous molecular reactions, offers new insights into biological system dynamics.
Area of Science:
- Molecular Biology
- Systems Biology
- Computational Biology
Background:
- Gene expression exhibits significant noise, characterized by irregular mRNA bursting across organisms.
- This transcriptional noise is hypothesized to drive phenotypic diversification in isogenetic cells.
- Protein stability can buffer mRNA noise, limiting its impact on global regulation.
Purpose of the Study:
- To investigate the constructive role of noisy mRNAs in system-level dynamics.
- To explore how post-transcriptional regulation influences the effects of transcriptional bursting.
- To understand the impact of intrinsic fluctuations on gene expression levels.
Main Methods:
- Developed computational models extending conventional transcriptional bursting models.
- Incorporated a post-transcriptional regulation step into stochastic models.
- Compared stochastic models with corresponding deterministic models.
Main Results:
- Intrinsic fluctuations were found to substantially increase the expected mRNA level.
- This increase in mRNA levels can elevate protein levels, even with slow protein degradation.
- Reaction asynchronicity was identified as the cause of this steady-state increase in mRNA levels.
Conclusions:
- Transcriptional bursting, due to reaction asynchronicity, can lead to higher steady-state mRNA and protein levels.
- These findings highlight non-intuitive effects of reaction asynchronicity on biological dynamics.
- The results have broad implications for modeling and understanding complex biological systems, emphasizing stochasticity.
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