Speaker
Marek Cieplak
(Institute of Physics, Polish Academy of Sciences)
Description
A method based on the principle of entropy maximization
is used to identify the gene interaction network with the
highest probability of giving rise to experimentally
observed transcript profiles [1]. In its simplest form, the
method yields the pairwise
gene interaction network, but it can also be extended to
deduce higher
order correlations. Analysis of microarray data from genes
in Saccharomyces cerevisiae chemostat cultures exhibiting
energy metabollic oscillations identifies a gene interaction
network that reflects the intracellular communication
pathways. These pathways adjust cellular metabolic activity
and cell division to the limiting nutrient conditions that
trigger metabolic oscillations. The success of the present
approach in extracting meaningful genetic
connections suggests that the maximum entropy principle is a
useful
concept for understanding living systems, as it is for other
complex, nonequilibrium systems. The time-dependent behavior
of the genetic network is found to involve only a few
fundamental modes [2,3].
REFERENCES:
[1] T. R. Lezon, J. R. Banavar, M. Cieplak, A. Maritan, and
N. Fedoroff,
Using the principle of entropy maximization to infer genetic
interaction
networks from gene expression patterns,
Proc. Natl. Acad. Sci. (USA) 103, 19033-19038 (2006)
[2] N. S. Holter, M. Mitra, A. Maritan,
M. Cieplak, J. R. Banavar, and N. V. Fedoroff,
Fundamental patterns underlying gene expression profiles:
simplicity from
complexity, Proc. Natl. Acad. Sci. USA 97, 8409-8414 (2000)
[3] N. S. Holter, A. Maritan, M. Cieplak, N. V. Fedoroff,
and J. R.
Banavar, Dynamic modeling of gene expression data,
Proc. Natl. Acad. Sci. USA 98, 1693-1698 (2001)
