Campus Event Calendar
Event Entry
What and Who
Physical Network Models and Multi-source Data Integration
Chen-Hsiang Yeang
MIT
Talk
AG 1, AG 2, AG 3, AG 4
MPI Audience
Note: We use this to send email in the morning.
Date, Time and Location
Monday, 7 April 2003
11:00
-- Not specified --
46.1 - MPII
024
Saarbrücken
Abstract
We develop a new framework for inferring models of transcriptional
regulation. The models in this approach, which we call {\em physical
models}, are constructed on the basis of verifiable molecular
attributes of the underlying biological system. The attributes
include, for example, the existence of protein-protein and protein-DNA
interactions in gene regulatory processes, the directionality of
signal transduction in protein-protein interactions, as well as the
signs of the immediate effects of these interactions (e.g., whether an
upstream gen activates or represses the downstream genes). Each
attribute is included as a variable in the model, and the variables
define a collection of annotated random graphs. Possible
configurations of these variables (realizations of the underlying
biological system) are constrained by the available data sources.
Some of the data sources such as factor-binding data (location data)
involve measurements that are directly tied to the variables in the
model. Other sources such as gene knock-outs are {\em functional} in
nature and provide only indirect evidence about the (physical)
variables. We associate each knock-out effect in the deletion mutant
data with a set of causal paths (molecular cascades) that could in
principle explain the effect, resulting in aggregate constraints about
the physical variables in the model. The most likely setting of all
the variables is found by the max-product algorithm. By testing our
approach on datasets related to the pheromone response pathway in {\em
S. cerevisiae}, we demonstrate that the resulting transcriptional
models are consistent with previous studies about the
pathway. Moreover, we show that the approach is capable of predicting
gene knock-out effects with high degree of accuracy in a
cross-validation setting. The method also implicates likely molecular
cascades responsible for each observed knock-out effect. The
inference results are robust against variations in the model
parameters. We can extend the approach to include other data sources
% (solve the corresponding data association problems),
such as time course expression profiles. We also discuss coordinated
regulation and the use of automated experiment design.
regulation. The models in this approach, which we call {\em physical
models}, are constructed on the basis of verifiable molecular
attributes of the underlying biological system. The attributes
include, for example, the existence of protein-protein and protein-DNA
interactions in gene regulatory processes, the directionality of
signal transduction in protein-protein interactions, as well as the
signs of the immediate effects of these interactions (e.g., whether an
upstream gen activates or represses the downstream genes). Each
attribute is included as a variable in the model, and the variables
define a collection of annotated random graphs. Possible
configurations of these variables (realizations of the underlying
biological system) are constrained by the available data sources.
Some of the data sources such as factor-binding data (location data)
involve measurements that are directly tied to the variables in the
model. Other sources such as gene knock-outs are {\em functional} in
nature and provide only indirect evidence about the (physical)
variables. We associate each knock-out effect in the deletion mutant
data with a set of causal paths (molecular cascades) that could in
principle explain the effect, resulting in aggregate constraints about
the physical variables in the model. The most likely setting of all
the variables is found by the max-product algorithm. By testing our
approach on datasets related to the pheromone response pathway in {\em
S. cerevisiae}, we demonstrate that the resulting transcriptional
models are consistent with previous studies about the
pathway. Moreover, we show that the approach is capable of predicting
gene knock-out effects with high degree of accuracy in a
cross-validation setting. The method also implicates likely molecular
cascades responsible for each observed knock-out effect. The
inference results are robust against variations in the model
parameters. We can extend the approach to include other data sources
% (solve the corresponding data association problems),
such as time course expression profiles. We also discuss coordinated
regulation and the use of automated experiment design.
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