Simulation of gene expression in silico
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.
Expression data set of patients of pediatric acute lymphoblastic leukemia (ALL, right; Yeoh et al, 2002, Cancer Cell 1:133-143) was use to construct a gene expression regulation network (left). Genes are displayed as circles with diameter proportional to the number of edges. Edges reflect mutual dependencies. PBX1, exhibiting a particular high number of edges to other genes, is shown as triangle.
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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.