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

Authors: Frank Emmert-Streib, Matthias Dehmer

Abstract:

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.

Keywords: Dynamic Bayesian networks, microarray data, structure learning, Markov chain Monte Carlo.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085343

References:

[1] Chen, T., He., H.L., Church, G.M.: Modeling gene expression with differential equations. Pac. Symp. Biocomput. 4 (1999) 29-40.
[2] Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall/CRC (1994).
[3] Friedman, N., Murphy, K., Russel, S.: Learning the Structure of Dynamic Probabilistic Networks. In Cooper, G.F. and Moral, S. (eds), Proceedings of the Fourteenth Conference on Uncertainty in Artifical Intelligence (UAI). Morgan Kaufmann Publishers, San Francisco, CA (1998).
[4] Friedman, N., Linial, M., Nachman, I., Pe-er, D.: Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 7:3/4 (2000) 601-620.
[5] Gardner, T.S., Cantor, C.R., Collins, J.J.: Construction of a genetic toggle switch in Escherichia coli. Nature 403:20 (2000) 339-342.
[6] Gardner, T.S., di Bernardo, D., Lorenz, D., Collins, J.J.: Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling. Science 301 (2003) 102-105.
[7] Hartemink, A.J., Gifford, D., Jaakkola, T., Young, R.: Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomp. 6 (2001) 422-433.
[8] Husmeier, D.: Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics 19:17 (2003) 2271-2282.
[9] Musmeier, D.: Inferring Dynamic Bayesian Networks with MCMC (DBmcmc). www.bioss.sari.ac.uk/Ôê╝dirk/software/DBmcmc/ (2003).
[10] Lee, T.I. et al.: Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 298 (2002) 799-804.
[11] Liu, J.S.: Monte Carlo Strategies in Scientific Computing. Springer- Verlag, New York (2001).
[12] Murphy, K.P.: Bayes Net Toolbox. Technical Report, MIT Artificial Intelligence Laboratory (2002).
[13] Perl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA, USA (1988).
[14] Schlitt, T., Brazma, A.: Modelling gene networks at different organizational levels. FEBS Letters 579 (2005) 1859-1866.
[15] Smith, V.A., Jarvis, E.D., Hartemink, A.J.: Evaluating functional network inference using simulations of complex biological systems. Bioinformatics 18 (2002) 164-175.
[16] Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D.: Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20:18 (2004) 3594-3603.