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RNA Polymerase Allocation

Also known as: RNAP partitioning, transcriptional resource allocation

The distribution of a finite RNA polymerase pool across all active promoters in a cell, determining global transcription rates.

RNA Polymerase Allocation describes how a cell’s finite pool of RNA polymerase molecules is partitioned among all active promoters, establishing a fundamental constraint on total transcriptional capacity 1.

How It Works

A rapidly growing E. coli cell contains approximately 5,000-10,000 RNA polymerase molecules, which must service thousands of promoters across the chromosome and any plasmids. Each RNAP molecule that initiates transcription at one promoter is unavailable to other promoters for the duration of the transcription event. Strong promoters that bind RNAP with high affinity capture a disproportionate share of the pool.

The competition for RNAP is mediated by sigma factors, which direct the RNAP core enzyme to specific promoter classes. Different sigma factors compete for binding to core RNAP, creating an additional layer of resource allocation. Stress-responsive sigma factors can redirect transcription from housekeeping genes to stress response genes by outcompeting the housekeeping sigma.

In synthetic biology, heterologous gene expression can consume a significant fraction of the RNAP pool, reducing transcription of host genes. Using orthogonal RNA polymerases such as T7 RNAP can partially decouple synthetic gene expression from the host transcription machinery.

Computational Considerations

Mathematical models of RNAP allocation treat promoter–RNAP binding as a competitive equilibrium, distributing a fixed pool across all promoters based on their binding affinities. These models predict how adding strong synthetic promoters will depress transcription genome-wide. Integration with growth rate models captures the feedback between RNAP allocation, ribosome synthesis, and cellular growth 2.


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Computational Angle

Resource allocation models partition RNAP among competing promoters using thermodynamic binding constants to predict system-wide transcription rate changes.

Related Terms

References

  1. Klumpp S, Hwa T. Growth-rate-dependent partitioning of RNA polymerases in bacteria . Proceedings of the National Academy of Sciences (2008) DOI
  2. Weiße AY et al.. Mechanistic links between cellular trade-offs, gene expression, and growth . Current Opinion in Biotechnology (2015) DOI