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  • Signal Transduction
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  • 1
    In: PLoS ONE, 2013, Vol.8(6)
    Description: Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior knowledge from existing sources of biological information can address this low signal to noise problem by biasing the network inference towards biologically plausible network structures. Although integrating various sources of information is desirable, their heterogeneous nature makes this task challenging. We propose two computational methods to incorporate various information sources into a probabilistic consensus structure prior to be used in graphical model inference. Our first model, called Latent Factor Model (LFM), assumes a high degree of correlation among external information sources and reconstructs a hidden variable as a common source in a Bayesian manner. The second model, a Noisy-OR, picks up the strongest support for an interaction among information sources in a probabilistic fashion. Our extensive computational studies on KEGG signaling pathways as well as on gene expression data from breast cancer and yeast heat shock response reveal that both approaches can significantly enhance the reconstruction accuracy of Bayesian Networks compared to other competing methods as well as to the situation without any prior. Our framework allows for using diverse information sources, like pathway databases, GO terms and protein domain data, etc. and is flexible enough to integrate new sources, if available.
    Keywords: Research Article ; Biology ; Computer Science
    E-ISSN: 1932-6203
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  • 2
    Language: English
    In: PLoS ONE, 01 January 2015, Vol.10(11), p.e0142646
    Description: Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 3
    In: PLoS ONE, 2017, Vol.12(2)
    Description: Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell. Causal network structure inference has been approached using different methods in the past. Most causal network inference techniques, such as Dynamic Bayesian Networks and ordinary differential equations, are limited by their computational complexity and thus make large scale inference infeasible. This is specifically true if a Bayesian framework is applied in order to deal with the unavoidable uncertainty about the correct model. We devise a novel Bayesian network reverse engineering approach using ordinary differential equations with the ability to include non-linearity. Besides modeling arbitrary, possibly combinatorial and time dependent perturbations with unknown targets, one of our main contributions is the use of Expectation Propagation, an algorithm for approximate Bayesian inference over large scale network structures in short computation time. We further explore the possibility of integrating prior knowledge into network inference. We evaluate the proposed model on DREAM4 and DREAM8 data and find it competitive against several state-of-the-art existing network inference methods.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Computer And Information Sciences ; Biology And Life Sciences ; Physical Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Physical Sciences ; Computer And Information Sciences ; Physical Sciences ; Biology And Life Sciences ; Biology And Life Sciences
    E-ISSN: 1932-6203
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  • 4
    In: PLoS ONE, 2014, Vol.9(4)
    Description: We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 5
    In: PLoS ONE, 2015, Vol.10(8)
    Description: Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 6
    Language: English
    In: PLOS ONE, 2013, Vol.8(10), pp.urn:issn:1932-6203
    Description: Transforming growth factor-beta 1 (TGF-[beta]1) stimulates a broad range of effects which are cell type dependent, and it has been suggested to induce cellular senescence. On the other hand, long-term culture of multipotent mesenchymal stromal cells (MSCs) has a major impact on their cellular physiology and therefore it is well conceivable that the molecular events triggered by TGF-[beta]1 differ considerably in cells of early and late passages. In this study, we analyzed the effect of TGF-[beta]1 on and during replicative senescence of MSCs. Stimulation with TGF-[beta]1 enhanced proliferation, induced a network like growth pattern and impaired adipogenic and osteogenic differentiation. TGF-[beta]1 did not induce premature senescence. However, due to increased proliferation rates the cells reached replicative senescence earlier than untreated controls. This was also evident, when we analyzed senescence-associated DNA-methylation changes. Gene expression profiles of MSCs differed considerably at relatively early (P 3 - 5) and later passages (P 10). Nonetheless, relative gene expression differences provoked by TGF-[beta]1 at individual time points or in a time course dependent manner (stimulation for 0, 1, 4 and 12 h) were very similar in MSCs of early and late passage. These results support the notion that TGF-[beta]1 has major impact on MSC function, but it does not induce senescence and has similar molecular effects during culture expansion.
    Keywords: Methylation -- Analysis ; Bone Morphogenetic Proteins -- Analysis ; Stem Cells -- Analysis ; Genes -- Analysis ; Transforming Growth Factors -- Analysis ; Gene Expression -- Analysis;
    ISSN: 1932-6203
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  • 7
    Language: English
    In: Journal of the Royal Society, Interface, June 2017, Vol.14(131)
    Description: Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid -Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data.
    Keywords: Dynamic Elastic-Net ; Modelling ; Ordinary Differential Equations ; Systems Biology ; Models, Chemical
    ISSN: 17425689
    E-ISSN: 1742-5662
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  • 8
    In: Bioinformatics, 2011, Vol. 27(2), pp.238-244
    Description: Motivation: Targeted interventions in combination with the measurement of secondary effects can be used to computationally reverse engineer features of upstream non-transcriptional signaling cascades. Nested effect models (NEMs) have been introduced as a statistical approach to estimate the upstream signal flow from downstream nested subset structure of perturbation effects. The method was substantially extended later on by several authors and successfully applied to various datasets. The connection of NEMs to Bayesian Networks and factor graph models has been highlighted. Here, we introduce a computationally attractive extension of NEMs that enables the analysis of perturbation time series data, hence allowing to discriminate between direct and indirect signaling and to resolve feedback loops. The implementation (R and C) is part of the Supplement to this article. 〈p〉〈bold〉Contact:〈/bold〉 〈email〉frohlich@bit.uni-bonn.de〈/email〉〈/p〉 are available at online.
    Keywords: Nanoelectromechanical Systems ; Computation ; Mathematical Models ; Upstream ; Perturbation Methods ; Feedback ; Bioinformatics ; Cascades ; Life and Medical Sciences (Ci);
    ISSN: 1367-4803
    E-ISSN: 1460-2059
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  • 9
    In: PLoS ONE, 2013, Vol.8(10)
    Description: Predicting protein functional classes such as localization sites and modifications plays a crucial role in function annotation. Given a tremendous amount of sequence data yielded from high-throughput sequencing experiments, the need of efficient and interpretable prediction strategies has been rapidly amplified. Our previous approach for subcellular localization prediction, PSLDoc, archives high overall accuracy for Gram-negative bacteria. However, PSLDoc is computational intensive due to incorporation of homology extension in feature extraction and probabilistic latent semantic analysis in feature reduction. Besides, prediction results generated by support vector machines are accurate but generally difficult to interpret. In this work, we incorporate three new techniques to improve efficiency and interpretability. First, homology extension is performed against a compact non-redundant database using a fast search model to reduce running time. Second, correspondence analysis (CA) is incorporated as an efficient feature reduction to generate a clear visual separation of different protein classes. Finally, functional classes are predicted by a combination of accurate compact set (CS) relation and interpretable one-nearest neighbor ( 1 -NN) algorithm. Besides localization data sets, we also apply a human protein kinase set to validate generality of our proposed method. Experiment results demonstrate that our method make accurate prediction in a more efficient and interpretable manner. First, homology extension using a fast search on a compact database can greatly accelerate traditional running time up to twenty-five times faster without sacrificing prediction performance. This suggests that computational costs of many other predictors that also incorporate homology information can be largely reduced. In addition, CA can not only efficiently identify discriminative features but also provide a clear visualization of different functional classes. Moreover, predictions based on CS achieve 100% precision. When combined with 1 -NN on unpredicted targets by CS, our method attains slightly better or comparable performance compared with the state-of-the-art systems.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 10
    Language: English
    In: Blood, 26 January 2017, Vol.129(4), pp.460-472
    Description: Epithelial-to-mesenchymal-transition (EMT) is critical for normal embryogenesis and effective postnatal wound healing, but is also associated with cancer metastasis. SNAIL, ZEB, and TWIST families of transcription factors are key modulators of the EMT process, but their precise roles in adult hematopoietic development and homeostasis remain unclear. Here we report that genetic inactivation of Zeb2 results in increased frequency of stem and progenitor subpopulations within the bone marrow (BM) and spleen and that these changes accompany differentiation defects in multiple hematopoietic cell lineages. We found no evidence that Zeb2 is critical for hematopoietic stem cell self-renewal capacity. However, knocking out Zeb2 in the BM promoted a phenotype with several features that resemble human myeloproliferative disorders, such as BM fibrosis, splenomegaly, and extramedullary hematopoiesis. Global gene expression and intracellular signal transduction analysis revealed perturbations in specific cytokine and cytokine receptor-related signaling pathways following Zeb2 loss, especially the JAK-STAT and extracellular signal-regulated kinase pathways. Moreover, we detected some previously unknown mutations within the human Zeb2 gene (ZFX1B locus) from patients with myeloid disease. Collectively, our results demonstrate that Zeb2 controls adult hematopoietic differentiation and lineage fidelity through widespread modulation of dominant signaling pathways that may contribute to blood disorders.
    Keywords: Cytokines -- Genetics ; Epithelial-Mesenchymal Transition -- Genetics ; Hematopoiesis, Extramedullary -- Genetics ; Homeodomain Proteins -- Genetics ; Primary Myelofibrosis -- Genetics ; Repressor Proteins -- Genetics ; Splenomegaly -- Genetics
    ISSN: 00064971
    E-ISSN: 1528-0020
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