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  • 2009  (8)
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  • 2009  (8)
  • 1
    Language: English
    In: Discrete Applied Mathematics, 2009, Vol.157(10), pp.2285-2295
    Description: High-throughput techniques allow measurement of hundreds of cell components simultaneously. The inference of interactions between cell components from these experimental data facilitates the understanding of complex regulatory processes. Differential equations have been established to model the dynamic behavior of these regulatory networks quantitatively. Usually traditional regression methods for estimating model parameters fail in this setting, since they overfit the data. This is even the case, if the focus is on modeling subnetworks of, at most, a few tens of components. In a Bayesian learning approach, this problem is avoided by a restriction of the search space with prior probability distributions over model parameters. This paper combines both differential equation models and a Bayesian approach. We model the periodic behavior of proteins involved in the cell cycle of the budding yeast , with differential equations, which are based on chemical reaction kinetics. One property of these systems is that they usually converge to a steady state, and lots of efforts have been made to explain the observed periodic behavior. We introduce an approach to infer an oscillating network from experimental data. First, an oscillating core network is learned. This is extended by further components by using a Bayesian approach in a second step. A specifically designed hierarchical prior distribution over interaction strengths prevents overfitting, and drives the solutions to sparse networks with only a few significant interactions. We apply our method to a simulated and a real world dataset and reveal main regulatory interactions. Moreover, we are able to reconstruct the dynamic behavior of the network.
    Keywords: Gene Regulatory Network ; Ordinary Differential Equations ; Oscillations ; Bayesian Regularization ; Cell Cycle ; Saccharomyces Cerevisiae ; Mathematics
    ISSN: 0166-218X
    E-ISSN: 1872-6771
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  • 2
    In: The Journal of Virology, 2009, Vol. 83(20), p.10494
    Description: Human immunodeficiency virus type 1 (HIV-1) group M viruses have achieved a global distribution, while HIV-1 group O viruses are endemic only in particular regions of Africa. Here, we evaluated biological characteristics of group O and group M viruses in ex vivo models of HIV-1 infection. The replicative capacity and ability to induce CD4 T-cell depletion of eight group O and seven group M primary isolates were monitored in cultures of human peripheral blood mononuclear cells and tonsil explants. Comparative and longitudinal infection studies revealed HIV-1 group-specific activity patterns: CCR5-using (R5) viruses from group M varied considerably in their replicative capacity but showed similar levels of cytopathicity. In contrast, R5 isolates from group O were relatively uniform in their replicative fitness but displayed a high and unprecedented variability in their potential to deplete CD4 T cells. Two R5 group O isolates were identified that cause massive depletion of CD4 T cells, to an extent comparable to CXCR4-using viruses and not documented for any R5 isolate from group M. Intergroup comparisons found a five- to eightfold lower replicative fitness of isolates from group O than for isolates from group M yet a similar overall intrinsic pathogenicity in tonsil cultures. This study establishes biological ex vivo characteristics of HIV-1 group O primary isolates. The current findings challenge the belief that a grossly reduced replicative fitness or inherently impaired cytopathicity of viruses from this group underlies their low global prevalence.
    Keywords: HIV-1 -- Classification ; Leukocytes, Mononuclear -- Virology ; Palatine Tonsil -- Virology;
    ISSN: 0022-538X
    ISSN: 0022538X
    E-ISSN: 10985514
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  • 3
    Language: English
    In: BMC Bioinformatics, Dec 28, 2009, Vol.10, p.448
    Description: Background The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. Results We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. Conclusions Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.
    Keywords: Genetic Regulation -- Research ; Statistical Models -- Usage ; Stochastic Processes -- Usage
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: BMC Bioinformatics, Dec 28, 2009, Vol.10, p.448
    Description: Background The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. Results We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. Conclusions Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.
    Keywords: Genetic Regulation ; Gene Expression
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 5
    Language: English
    In: BMC Bioinformatics, 01 December 2009, Vol.10(1), p.448
    Description: Abstract Background The reconstruction of gene regulatory networks from time series gene expression data is one of the most difficult problems in systems biology. This is due to several reasons, among them the combinatorial explosion of possible network topologies, limited information content of the experimental data with high levels of noise, and the complexity of gene regulation at the transcriptional, translational and post-translational levels. At the same time, quantitative, dynamic models, ideally with probability distributions over model topologies and parameters, are highly desirable. Results We present a novel approach to infer such models from data, based on nonlinear differential equations, which we embed into a stochastic Bayesian framework. We thus address both the stochasticity of experimental data and the need for quantitative dynamic models. Furthermore, the Bayesian framework allows it to easily integrate prior knowledge into the inference process. Using stochastic sampling from the Bayes' posterior distribution, our approach can infer different likely network topologies and model parameters along with their respective probabilities from given data. We evaluate our approach on simulated data and the challenge #3 data from the DREAM 2 initiative. On the simulated data, we study effects of different levels of noise and dataset sizes. Results on real data show that the dynamics and main regulatory interactions are correctly reconstructed. Conclusions Our approach combines dynamic modeling using differential equations with a stochastic learning framework, thus bridging the gap between biophysical modeling and stochastic inference approaches. Results show that the method can reap the advantages of both worlds, and allows the reconstruction of biophysically accurate dynamic models from noisy data. In addition, the stochastic learning framework used permits the computation of probability distributions over models and model parameters, which holds interesting prospects for experimental design purposes.
    Keywords: Biology
    ISSN: 1471-2105
    E-ISSN: 1471-2105
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  • 6
    In: Bioinformatics, 2009, Vol. 25(5), pp.678-679
    Description: Summary: We present RNAither, a package for the free statistical environment R which performs an analysis of high-throughput RNA interference (RNAi) knock-down experiments, generating lists of relevant genes and pathways out of raw experimental data. The library provides a quality assessment of the signal intensities, as well as a broad range of options for data normalization, different statistical tests for the identification of significant siRNAs, and a significance analysis of the biological processes involving corresponding genes. The results of the analysis are presented as a set of HTML pages. Additionally, all values and plots are available as either text files or pdf and png files. 〈p〉〈bold〉Availability:〈/bold〉 〈ext-link ext-link-type="uri" xlink_href="http://bioconductor.org/"〉http://bioconductor.org/〈/ext-link〉〈/p〉 〈p〉〈bold〉Contact:〈/bold〉 〈email〉RNAither@gmx.de〈/email〉〈/p〉
    Keywords: Genes ; Quality Assessment ; Ribonucleic Acids ; Lists ; Hypertext Markup Language ; Biological ; Texts ; Contact ; Life and Medical Sciences (Ci) ; Article;
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 7
    Language: English
    In: Cancer Letters, 2009, Vol.282(1), pp.55-62
    Description: Neuroblastoma is the most common extracranial childhood tumor, comprising 15% of all childhood cancer deaths. In an initial study, we used Affymetrix oligonucleotide microarrays to analyse gene expression in 68 primary neuroblastomas and compared different data mining approaches for prediction of early relapse. Here, we performed re-analyses of the data including prolonged follow-up and applied support vector machine (SVM) algorithms and outer cross-validation strategies to improve reliability of expression profiling based predictors. Accuracy of outcome prediction was significantly improved by the use of innovative SVM algorithms on the updated data. In addition, CASPAR, a hierarchical Bayesian approach, was used to predict survival times for the individual patient based on expression profiling data. CASPAR reliably predicted event-free survival, given a cut-off time of three years. Differential expression of genes used by CASPAR to predict patient outcome was validated in an independent cohort of 117 neuroblastomas. In conclusion, we show here for the first time that reanalysis of microarray data using improved methodology, state-of-the-art performance tests and updated follow-up data improves prognosis prediction, and may further improve risk stratification of individual patients.
    Keywords: Neuroblastoma ; Expression Profiling ; Reanalysis ; Svm ; Caspar ; Medicine
    ISSN: 0304-3835
    E-ISSN: 1872-7980
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  • 8
    Language: English
    In: Bioinformatics (Oxford, England), 01 September 2009, Vol.25(17), pp.2229-35
    Description: The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data. We infer pathway topologies from gene knockdown data using Bayesian networks with probabilistic Boolean threshold functions. To deal with the problem of underdetermined network parameters, we employ a Bayesian learning approach, in which we can integrate arbitrary prior information on the network under consideration. Missing observations are integrated out. We compute the exact likelihood function for smaller networks, and use an approximation to evaluate the likelihood for larger networks. The posterior distribution is evaluated using mode hopping Markov chain Monte Carlo. Distributions over topologies and parameters can then be used to design additional experiments. We evaluate our approach on a small artificial dataset, and present inference results on RNAi data from the Jak/Stat pathway in a human hepatoma cell line.
    Keywords: Models, Statistical ; RNA Interference ; Signal Transduction
    ISSN: 13674803
    E-ISSN: 1367-4811
    E-ISSN: 14602059
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