Identifying genes of gene regulatory networks using formal concept analysis.

In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g., in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application, our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.

Modeling feedback loops in the H-NS-mediated regulation of the Escherichia coli bgl operon.

The histone-like nucleoid-associated protein H-NS is a global transcriptional repressor that controls approximately 5% of all genes in Escherichia coli and other enterobacteria. H-NS binds to DNA with low specificity. Nonetheless, repression of some loci is exceptionally specific. Experimental data for the E. coli bgl operon suggest that highly specific repression is caused by regulatory feedback loops. To analyze whether such feedback loops can account for the observed specificity of repression, here a model was built based on expression data. The model includes several regulatory interactions, which are synergy of repression by binding of H-NS to two regulatory elements, an inverse correlation of the rate of repression by H-NS and transcription, and a threshold for positive regulation by anti-terminator BglG, which is encoded within the operon. The latter two regulatory interactions represent feedback loops in the model. The resulting system of equations was solved for the expression level of the operon and analyzed with respect to different promoter activities. This analysis demonstrates that a small (3-fold) increase of the bgl promoter activity results in a strong (80-fold) enhancement of bgl operon expression. Thus, the parameters included into the model are sufficient to simulate specific repression by H-NS.

Sensory gating revisited: relation between brain oscillations and auditory evoked potentials in schizophrenia.

Disturbances of auditory information processing have repeatedly been shown in schizophrenia. To contribute to a better understanding of the neurophysiological underpinnings of habituation in auditory processing and its disturbance in schizophrenia we used three different approaches to analyze auditory evoked responses, namely phase-locking (PL) analyses, single trial amplitudes, and averaged event-related potentials (P50 and N100). Given that brain oscillations reflect the neuronal correlates of information processing we hypothesized that PL and amplitudes reflect even more essential parts of auditory processing than the averaged ERP responses. In 32 schizophrenia patients and 32 matched controls EEG was continuously recorded using an auditory paired click paradigm. PL of the lower frequency bands (alpha and theta) was significantly reduced in patients whereas no significant differences were present in higher frequencies (gamma and beta). Alpha and theta PL and amplitudes showed a marked increase after the first click and to a minor degree after the second one. This habituation was more prominent in controls whereas in schizophrenia patients the response to both clicks differed only slightly. N100 suppression was significantly reduced in schizophrenia patients whereas no group differences were present with respect to the P50. This corresponded to the finding that gamma mostly contributed to the prediction of the P50 response and theta mostly to the N100 response. Our data showed that analyzing phase and amplitude in single trials provides more information on auditory information processing and reflects differences between schizophrenia patients and controls better than analyzing the averaged ERP responses.

CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data.

MOTIVATION: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction. RESULTS: Validity of the method is demonstrated on two publicly available datasets on diffuse large B-cell lymphoma (DLBCL) and on adenocarcinoma of the lung. The method successfully identifies long and short survivors, with high sensitivity and specificity. We compare our method with two alternative methods from the literature, demonstrating superior results of our approach. In addition, we show that CASPAR can further refine predictions made using clinical scoring systems such as the International Prognostic Index (IPI) for DLBCL and clinical staging for lung cancer, thus providing an additional tool for the clinician. An analysis of the genes identified confirms previously published results, and furthermore, new candidate genes correlated with survival are identified.