2009 Math Biology Seminar - 06

  • Date: 03/19/2009
Eldon Emberly (Simon Fraser University)

University of British Columbia


Optimization of Mutual Information in Genetic Networks


Cellular decisions rely upon a cell making some measurement of its
surroundings and then regulating its behaviour based on this
measurement. For many cellular processes this decision is regulated by
the transcriptional output of a gene which is regulated by a single
input which may exist in several possible states. Given the noise
inherent in the input signal and the chemical processes coupling the
input to the output, how well can the input states be measured by the
genetic network and is the genetic network optimized to maximize the
likelihood of determining the correct input state? Recently, this
problem has been analyzed in the context of mutual information. I will
discuss the application of mutual information to the problem of
morphogen readout in developing organisms, and will compare the
predicted optimal morphogen gradient for the early drosophila factor
Bcd to its experimental profile. Lastly, I will show how optimizing
mutual information for an organism that needs to infer a two state
environment which varies, leads to an optimal transcriptional response
with biologically realistic kinetic parameters.


2:00pm, WMAX 216