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Siemens AG Corporate Technology and Biomax have forged a strategic alliance in the area of
gene expression modeling and simulation. The companies combine their complementary and unique
technologies to allow scientists to go beyond common numeric analysis of their gene
expression data. Now, for the first time, gene expression research can be simulated in silico.
The BioSim technology, developed by Siemens, recognizes interrelated dependencies within gene
expression data, which can be used for planning experiments. Structural
learning of Bayesian networks is applied to sets of genome-wide expression
patterns. Bayesian networks are trained with the goal of inferring biological
aspects of gene function.
A two-component approach focuses on supporting the
drug discovery process by identifying genes with central roles for the network
operation, which could act as drug targets.
The first component, referred to as
scale-free analysis, uses topological measures of the network-related to a
high-traffic load of genes-as estimators for their functional importance.
The
second component, referred to as generative inverse modeling, is a method of
estimating the effect of a simulated drug treatment or mutation on the global
state of the network, as measured in the expression profile.
BioSim was integrated into Biomax Gene Expression Analysis
Tool to place correlations uncovered in the simulation in a relevant
functional and biological context. Thus, the underlying biological mechanisms can be elucidated.
The
particular strength of the new method is the possibility to simulate changes the
expression of individual genes and monitor the affect on the expression of other
genes. In this way, scientist can plan data acquisition more rationally, thereby
minimizing experimental efforts.
References:
Dejori M and Stetter M (2004) Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles. J Comput Biol 11:1135-48
Dejori M, Schuermann B and Stetter M (2004) Hunting drug targets by systems-level modeling of gene expression profiles. IEEE Trans Nanobioscience 3:180-91
Dejori M, Schwaighofer A, Tresp V and Stetter M (2004) Mining functional modules in genetic networks with decomposable graphical models. OMICS 8:176-88
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