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• W760780 abstract "Modern biological research aims to understand when genes are expressed andhow certain genes inuence the expression of other genes. For organizing and visualizinggene expression activity gene regulatory networks are used. The architectureof these networks holds great importance, as they enable us to identify inconsistenciesbetween hypotheses and observations, and to predict the behavior of biologicalprocesses in yet untested conditions.Data from gene expression measurements are used to construct gene regulatorynetworks. Along with the advance of high-throughput technologies for measuringgene expression statistical methods to predict regulatory networks have alsobeen evolving. This thesis presents a computational framework based on a Bayesianmodeling technique using state space models (SSM) for the inference of gene regulatorynetworks from time-series measurements.A linear SSM consists of observation and hidden state equations. The hiddenvariables can unfold effects that cannot be directly measured in an experiment, suchas missing gene expression. We have used a Bayesian MCMC approach based onGibbs sampling for the inference of parameters. However the task of determiningthe dimension of the hidden state space variables remains crucial for the accuracyof network inference. For this we have used the Bayesian evidence (or marginallikelihood) as a yardstick. In addition, the Bayesian approach also provides thepossibility of incorporating prior information, based on literature knowledge.We compare marginal likelihoods calculated from the Gibbs sampler outputto the lower bound calculated by a variational approximation. Before using thealgorithm for the analysis of real biological experimental datasets we perform validationtests using numerical experiments based on simulated time series datasetsgenerated by in-silico networks. The robustness of our algorithm can be measuredby its ability to recapture the input data and generating networks using the inferredparameters.Our developed algorithm, GBSSM, was used to infer a gene network usingE. coli data sets from the different stress conditions of temperature shift and acidstress. The resulting model for the gene expression response under temperature shiftcaptures the effects of global transcription factors, such as fnr that control the regulationof hundreds of other genes. Interestingly, we also observe the stress-inducible membrane protein OsmC regulating transcriptional activity involved in the adaptationmechanism under both temperature shift and acid stress conditions. In the caseof acid stress, integration of metabolomic and transcriptome data suggests that theobserved rapid decrease in the concentration of glycine betaine is the result of theactivation of osmoregulators which may play a key role in acid stress adaptation." @default.
• W760780 created "2016-06-24" @default.
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• W760780 date "2013-04-01" @default.
• W760780 modified "2023-09-27" @default.
• W760780 title "Reconstructing regulatory networks from high-throughput post-genomic data using MCMC methods" @default.
• W760780 hasPublicationYear "2013" @default.
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