Campus Event Calendar
Event Entry
New for: D2, D3
What and Who
Estimating the Size and Completeness of Gene Regulatory Networks
Richard Röttger
Technische Universität München
PhD Application Talk
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Friday, 15 October 2010
09:00
90 Minutes
E1 4
024
Saarbrücken
Abstract
The National Center for Biotechnology Information (NCBI) recently
announced "1,000 prokaryotic genomes are now completed and available in
the Genome database". The increasing trend will provide us with
thousands of sequenced microbial organisms over the next years. Knowing
the genes' locations on the DNA strand and their (often predicted)
function only is just part of the whole task. Equally important is to
decipher the regulatory mechanisms that control how cells survive,
reproduce and adapt their behavior while being exposed to changing
environmental conditions. One major control mechanism is transcriptional
gene regulation. Here, striking is the direct juxtaposition of the
handful of bacterial model organisms to the one thousand prokaryotic
genomes. Next-generation sequencing technologies will further widen this
gap drastically. Even the regulatory networks for the few model
organisms, for which experimental data on its gene regulations exist,
are far away from being complete. But how much do we really know, how
much is still missing? Is the size of a regulatory network a measure for
the complexity of an organism rather than the number of genes?
In the talk we will introduce our results to answers these questions. We
present a model developed for estimating the size of the transcriptional
regulatory networks by means of network invariants of the known
subnetworks. The model takes different regulation mechanisms into
account. We demonstrate that the predictions are bias-free and
subsequently applied our model to several eukaryotic and prokaryotic
model organisms, among them Escherichia coli, Mus musculus, and Homo
sapiens. Furthermore, we calculated approximate bootstrap confidence
intervals to assess the variability of the estimator.
Finally, our computations suggest that even for the best studied model
organism, E. coli, we "only" know approximately 35% of its
transcriptional gene regulatory interactions. Surprisingly, we are
further able to show that the predicted network size does not correlate
with the very ratio between TF genes and non-TF genes.
announced "1,000 prokaryotic genomes are now completed and available in
the Genome database". The increasing trend will provide us with
thousands of sequenced microbial organisms over the next years. Knowing
the genes' locations on the DNA strand and their (often predicted)
function only is just part of the whole task. Equally important is to
decipher the regulatory mechanisms that control how cells survive,
reproduce and adapt their behavior while being exposed to changing
environmental conditions. One major control mechanism is transcriptional
gene regulation. Here, striking is the direct juxtaposition of the
handful of bacterial model organisms to the one thousand prokaryotic
genomes. Next-generation sequencing technologies will further widen this
gap drastically. Even the regulatory networks for the few model
organisms, for which experimental data on its gene regulations exist,
are far away from being complete. But how much do we really know, how
much is still missing? Is the size of a regulatory network a measure for
the complexity of an organism rather than the number of genes?
In the talk we will introduce our results to answers these questions. We
present a model developed for estimating the size of the transcriptional
regulatory networks by means of network invariants of the known
subnetworks. The model takes different regulation mechanisms into
account. We demonstrate that the predictions are bias-free and
subsequently applied our model to several eukaryotic and prokaryotic
model organisms, among them Escherichia coli, Mus musculus, and Homo
sapiens. Furthermore, we calculated approximate bootstrap confidence
intervals to assess the variability of the estimator.
Finally, our computations suggest that even for the best studied model
organism, E. coli, we "only" know approximately 35% of its
transcriptional gene regulatory interactions. Surprisingly, we are
further able to show that the predicted network size does not correlate
with the very ratio between TF genes and non-TF genes.
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Stephanie Jörg, 10/14/2010 14:24
Stephanie Jörg, 10/14/2010 14:03 -- Created document.
Stephanie Jörg, 10/14/2010 14:03 -- Created document.