First Studies of the Influence of Single Gene Perturbations on the Inference of Genetic Networks

Inferring the network structure from time series data is a hard problem, especially if the time series is short and noisy. DNA microarray is a technology allowing to monitor the mRNA concentration of thousands of genes simultaneously that produces data of these characteristics. In this study we try to investigate the influence of the experimental design on the quality of the result. More precisely, we investigate the influence of two different types of random single gene perturbations on the inference of genetic networks from time series data. To obtain an objective quality measure for this influence we simulate gene expression values with a biologically plausible model of a known network structure. Within this framework we study the influence of single gene knock-outs in opposite to linearly controlled expression for single genes on the quality of the infered network structure.

Authors:

• Frank Emmert-Streib
• Matthias Dehmer

Keywords:

• Dynamic Bayesian networks
• microarray data
• structure learning
• Markov chain Monte Carlo.

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