Translational Cross Talk in Gene Networks
W. H. Mather, J. Hasty, L. S. Tsimring and R. J. Williams
ABSTRACT
It
has been shown experimentally that competition for
limited translational resources by upstream mRNAs can lead to an
anticorrelation between protein counts. Here, we investigate a stochastic model
for this phenomenon, in which gene transcripts of different types compete for a
finite pool of ribosomes. Throughout, we utilize concepts from the theory of
multiclass queues to describe a qualitative shift in protein count statistics
as the system transitions from being underloaded (ribosomes exceed transcripts in number)
to being overloaded (transcripts exceed ribosomes in number). The exact analytical
solution of a simplified stochastic model, in which the numbers of competing
mRNAs and ribosomes are fixed, exhibits weak positive correlations
between steady-state protein counts when total transcript count slightly
exceeds ribosome count, whereas the solution can exhibit strong negative
correlations when total transcript count significantly exceeds ribosome count.
Extending this analysis, we find approximate but reasonably accurate
solutions for a more realistic model, in which abundances of mRNAs and
ribosomes are allowed to fluctuate randomly. Here, ribosomal fluctuations
contribute positively and mRNA fluctuations contribute negatively to
correlations, and when mRNA fluctuations dominate ribosomal fluctuations,
a strong anticorrelation extremum reliably occurs near the transition
from the underloaded to the overloaded regime.
Published in
Biophysical Journal, 104 (2013), 2564-2572.
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Last updated: June 5, 2013