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Creation of Three-Dimensional Liver Tissue Models from Experimental Images for Systems Medicine. Methods Mol Biol 2018; 1506:319-362. [PMID: 27830563 DOI: 10.1007/978-1-4939-6506-9_22] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
In this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine.
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Magnusson R, Mariotti GP, Köpsén M, Lövfors W, Gawel DR, Jörnsten R, Linde J, Nordling TEM, Nyman E, Schulze S, Nestor CE, Zhang H, Cedersund G, Benson M, Tjärnberg A, Gustafsson M. LASSIM-A network inference toolbox for genome-wide mechanistic modeling. PLoS Comput Biol 2017. [PMID: 28640810 PMCID: PMC5501685 DOI: 10.1371/journal.pcbi.1005608] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naïve Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases. There are excellent methods to mathematically model time-resolved biological data on a small scale using accurate mechanistic models. Despite the rapidly increasing availability of such data, mechanistic models have not been applied on a genome-wide level due to excessive runtimes and the non-identifiability of model parameters. However, genome-wide, mechanistic models could potentially answer key clinical questions, such as finding the best drug combinations to induce an expression change from a disease to a healthy state. We present LASSIM, which is a toolbox built to infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the network inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces computation time.
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Affiliation(s)
- Rasmus Magnusson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Guido Pio Mariotti
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mattias Köpsén
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - William Lövfors
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Danuta R. Gawel
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Rebecka Jörnsten
- Mathematical Sciences, Chalmers University of Technology, University of Gothenburg, Gothenburg, Sweden
| | - Jörg Linde
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
- Research Group PiDOMICS, Leibniz Institute for Natural Product Research and Infection Biology -Hans Knöll Institute, Jena, Germany
| | - Torbjörn E. M. Nordling
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan
- Stockholm Bioinformatics Center, Science for Life Laboratory, Solna, Sweden
| | - Elin Nyman
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Sylvie Schulze
- Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knoell-Institute, Research Group Systems Biology and Bioinformatics, Jena, Germany
| | - Colm E. Nestor
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Huan Zhang
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Integrative Systems Biology, Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Cell Biology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Mikael Benson
- Centre for Personalised Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden
| | - Andreas Tjärnberg
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Mika Gustafsson
- Bioinformatics Unit, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- * E-mail:
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Hebecker B, Vlaic S, Conrad T, Bauer M, Brunke S, Kapitan M, Linde J, Hube B, Jacobsen ID. Dual-species transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions. Sci Rep 2016; 6:36055. [PMID: 27808111 PMCID: PMC5093689 DOI: 10.1038/srep36055] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/11/2016] [Indexed: 11/15/2022] Open
Abstract
Candida albicans is a common cause of life-threatening fungal bloodstream infections. In the murine model of systemic candidiasis, the kidney is the primary target organ while the fungal load declines over time in liver and spleen. To better understand these organ-specific differences in host-pathogen interaction, we performed gene expression profiling of murine kidney, liver and spleen and determined the fungal transcriptome in liver and kidney. We observed a delayed transcriptional immune response accompanied by late induction of fungal stress response genes in the kidneys. In contrast, early upregulation of the proinflammatory response in the liver was associated with a fungal transcriptome resembling response to phagocytosis, suggesting that phagocytes contribute significantly to fungal control in the liver. Notably, C. albicans hypha-associated genes were upregulated in the absence of visible filamentation in the liver, indicating an uncoupling of gene expression and morphology and a morphology-independent effect by hypha-associated genes in this organ. Consistently, integration of host and pathogen transcriptional data in an inter-species gene regulatory network indicated connections of C. albicans cell wall remodelling and metabolism to the organ-specific immune responses.
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Affiliation(s)
- Betty Hebecker
- Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany.,Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| | - Sebastian Vlaic
- Department of General, Visceral and Vascular Surgery, Experimental Transplantation Surgery, Jena University Hospital, Jena, Germany.,Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany.,Department of Bioinformatics, Friedrich-Schiller-University Jena, Germany
| | - Theresia Conrad
- Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany
| | - Michael Bauer
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.,Department of Anaesthesiology and Intensive Care Therapy, Jena University Hospital, Jena, Germany
| | - Sascha Brunke
- Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany
| | - Mario Kapitan
- Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany
| | - Jörg Linde
- Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany
| | - Bernhard Hube
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.,Department of Microbial Pathogenicity Mechanisms, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany.,Friedrich-Schiller-University, Jena, Germany
| | - Ilse D Jacobsen
- Research Group Microbial Immunology, Leibniz Institute for Natural Product Research and Infection Biology (Hans Knöll Institute), Jena, Germany.,Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany.,Friedrich-Schiller-University, Jena, Germany
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Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers. Clin Microbiol Infect 2016; 22:600-6. [PMID: 27113568 DOI: 10.1016/j.cmi.2016.04.014] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 04/13/2016] [Accepted: 04/15/2016] [Indexed: 02/06/2023]
Abstract
In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.
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Fronczuk M, Raftery AE, Yeung KY. CyNetworkBMA: a Cytoscape app for inferring gene regulatory networks. SOURCE CODE FOR BIOLOGY AND MEDICINE 2015; 10:11. [PMID: 26566394 PMCID: PMC4642660 DOI: 10.1186/s13029-015-0043-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2014] [Accepted: 10/31/2015] [Indexed: 12/31/2022]
Abstract
Background Inference of gene networks from expression data is an important problem in computational biology. Many algorithms have been proposed for solving the problem efficiently. However, many of the available implementations are programming libraries that require users to write code, which limits their accessibility. Results We have developed a tool called CyNetworkBMA for inferring gene networks from expression data that integrates with Cytoscape. Our application offers a graphical user interface for networkBMA, an efficient implementation of Bayesian Model Averaging methods for network construction. The client-server architecture of CyNetworkBMA makes it possible to distribute or centralize computation depending on user needs. Conclusions CyNetworkBMA is an easy-to-use tool that makes network inference accessible to non-programmers through seamless integration with Cytoscape. CyNetworkBMA is available on the Cytoscape App Store at http://apps.cytoscape.org/apps/cynetworkbma. Electronic supplementary material The online version of this article (doi:10.1186/s13029-015-0043-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Maciej Fronczuk
- Institute of Technology, University of Washington, Tacoma, 98402 WA USA
| | - Adrian E Raftery
- Department of Statistics, University of Washington, Seattle, 98195 WA USA
| | - Ka Yee Yeung
- Institute of Technology, University of Washington, Tacoma, 98402 WA USA
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Altwasser R, Baldin C, Weber J, Guthke R, Kniemeyer O, Brakhage AA, Linde J, Valiante V. Network Modeling Reveals Cross Talk of MAP Kinases during Adaptation to Caspofungin Stress in Aspergillus fumigatus. PLoS One 2015; 10:e0136932. [PMID: 26356475 PMCID: PMC4565559 DOI: 10.1371/journal.pone.0136932] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Accepted: 08/11/2015] [Indexed: 12/14/2022] Open
Abstract
Mitogen activated protein kinases (MAPKs) are highly conserved in eukaryotic organisms. In pathogenic fungi, their activities were assigned to different physiological functions including drug adaptation and resistance. Aspergillus fumigatus is a human pathogenic fungus, which causes life-threatening invasive infections. Therapeutic options against invasive mycoses are still limited. One of the clinically used drugs is caspofungin, which specifically targets the fungal cell wall biosynthesis. A systems biology approach, based on comprehensive transcriptome data sets and mathematical modeling, was employed to infer a regulatory network and identify key interactions during adaptation to caspofungin stress in A. fumigatus. Mathematical modeling and experimental validations confirmed an intimate cross talk occurring between the cell wall-integrity and the high osmolarity-glycerol signaling pathways. Specifically, increased concentrations of caspofungin promoted activation of these signalings. Moreover, caspofungin affected the intracellular transport, which caused an additional osmotic stress that is independent of glucan inhibition. High concentrations of caspofungin reduced this osmotic stress, and thus decreased its toxic activity. Our results demonstrated that MAPK signaling pathways play a key role during caspofungin adaptation and are contributing to the paradoxical effect exerted by this drug.
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Affiliation(s)
- Robert Altwasser
- Department of Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
| | - Clara Baldin
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- Department of Microbiology and Molecular Biology, Institute of Microbiology, Friedrich Schiller University Jena, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
| | - Jakob Weber
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- Department of Microbiology and Molecular Biology, Institute of Microbiology, Friedrich Schiller University Jena, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
| | - Reinhard Guthke
- Department of Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
| | - Olaf Kniemeyer
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- Department of Microbiology and Molecular Biology, Institute of Microbiology, Friedrich Schiller University Jena, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital, 07747, Jena, Germany
| | - Axel A. Brakhage
- Department of Molecular and Applied Microbiology, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- Department of Microbiology and Molecular Biology, Institute of Microbiology, Friedrich Schiller University Jena, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
| | - Jörg Linde
- Department of Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- * E-mail: (JL); (VV)
| | - Vito Valiante
- Leibniz Junior Research Group—Biobricks of Microbial Natural Product Syntheses, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Adolf-Reichwein-Str. 23, 07745, Jena, Germany
- * E-mail: (JL); (VV)
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Drasdo D, Bode J, Dahmen U, Dirsch O, Dooley S, Gebhardt R, Ghallab A, Godoy P, Häussinger D, Hammad S, Hoehme S, Holzhütter HG, Klingmüller U, Kuepfer L, Timmer J, Zerial M, Hengstler JG. The virtual liver: state of the art and future perspectives. Arch Toxicol 2015; 88:2071-5. [PMID: 25331938 DOI: 10.1007/s00204-014-1384-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Dirk Drasdo
- Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau - Rocquencourt, Paris, France
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Linde J, Schulze S, Henkel SG, Guthke R. Data- and knowledge-based modeling of gene regulatory networks: an update. EXCLI JOURNAL 2015; 14:346-78. [PMID: 27047314 PMCID: PMC4817425 DOI: 10.17179/excli2015-168] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 02/10/2015] [Indexed: 02/01/2023]
Abstract
Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.
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Affiliation(s)
- Jörg Linde
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
| | - Sylvie Schulze
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
| | | | - Reinhard Guthke
- Research Group Systems Biology / Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Beutenbergstr. 11a, 07745 Jena, Germany
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Schulze S, Henkel SG, Driesch D, Guthke R, Linde J. Computational prediction of molecular pathogen-host interactions based on dual transcriptome data. Front Microbiol 2015; 6:65. [PMID: 25705211 PMCID: PMC4319478 DOI: 10.3389/fmicb.2015.00065] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 01/19/2015] [Indexed: 11/13/2022] Open
Abstract
Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.
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Affiliation(s)
- Sylvie Schulze
- Department of Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute Jena, Germany
| | | | | | - Reinhard Guthke
- Department of Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute Jena, Germany
| | - Jörg Linde
- Department of Systems Biology and Bioinformatics, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knoell-Institute Jena, Germany
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10
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Meyer-Baese A, Wildberger J, Meyer-Baese U, Nilsson CL. Data analysis techniques in phosphoproteomics. Electrophoresis 2014; 35:3452-62. [DOI: 10.1002/elps.201400219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 09/24/2014] [Accepted: 09/25/2014] [Indexed: 11/08/2022]
Affiliation(s)
- Anke Meyer-Baese
- Department of Scientific Computing; Florida State University; FL USA
| | - Joachim Wildberger
- Department of Radiology; Maastricht University Medical Center; Maastricht The Netherlands
| | - Uwe Meyer-Baese
- Department of Electrical and Computer Engineering; Florida State University; FL USA
| | - Carol L. Nilsson
- Departments of Pharmacology and Toxicology and Biochemistry and Molecular Biology; UTMB; and UTMB Cancer Center; University of Texas; Medical Branch at Galveston; TX USA
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Weber M, Sotoca AM, Kupfer P, Guthke R, van Zoelen EJ. Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation. BMC SYSTEMS BIOLOGY 2013; 7:124. [PMID: 24219887 PMCID: PMC4225824 DOI: 10.1186/1752-0509-7-124] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2013] [Accepted: 10/30/2013] [Indexed: 01/12/2023]
Abstract
Background Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network. Results Time series experiments were performed to measure mRNA and miRNA abundances of TGF-beta1+BMP2 stimulated hMSCs. Network nodes were identified by analysing temporal expression changes, miRNA target gene predictions, time series correlation and literature knowledge. Network inference was performed using NetGenerator to reconstruct a dynamical regulatory model based on the measured data and prior knowledge. The resulting model is robust against noise and shows an optimal trade-off between fitting precision and inclusion of prior knowledge. It predicts the influence of miRNAs on the expression of chondrogenic marker genes and therefore proposes novel regulatory relations in differentiation control. By analysing the inferred network, we identified a previously unknown regulatory effect of miR-524-5p on the expression of the transcription factor SOX9 and the chondrogenic marker genes COL2A1, ACAN and COL10A1. Conclusions Genome-wide exploration of miRNA-mRNA regulatory relationships is a reasonable approach to identify miRNAs which have so far not been associated with the investigated differentiation process. The NetGenerator tool is able to identify valid gene regulatory networks on the basis of miRNA and mRNA time series data.
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Affiliation(s)
- Michael Weber
- Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstr, 11a, 07745 Jena, Germany.
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12
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Osada J. The use of transcriptomics to unveil the role of nutrients in Mammalian liver. ISRN NUTRITION 2013; 2013:403792. [PMID: 24967258 PMCID: PMC4045299 DOI: 10.5402/2013/403792] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2013] [Accepted: 08/04/2013] [Indexed: 01/03/2023]
Abstract
Liver is the organ primarily responding to diet, and it is crucial in determining plasma carbohydrate, protein, and lipid levels. In addition, it is mainly responsible for transformation of xenobiotics. For these reasons, it has been a target of transcriptomic analyses. In this review, we have covered the works dealing with the response of mammalian liver to different nutritional stimuli such as fasting/feeding, caloric restriction, dietary carbohydrate, cholesterol, fat, protein, bile acid, salt, vitamin, and oligoelement contents. Quality of fats or proteins has been equally addressed, and has the influence of minor dietary components. Other compounds, not purely nutritional as those represented by alcohol and food additives, have been included due to their relevance in processed food. The influence has been studied not only on mRNA but also on miRNA. The wide scope of the technology clearly reflects that any simple intervention has profound changes in many metabolic parameters and that there is a synergy in response when more compounds are included in the intervention. Standardized arrays to systematically test the same genes in all studies and analyzing data to establish patterns of response are required, particularly for RNA sequencing. Moreover, RNA is a valuable, easy-screening ally but always requires further confirmation.
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Affiliation(s)
- Jesús Osada
- Departamento de Bioquímica y Biología Molecular y Celular, Facultad de Veterinaria, Instituto de Investigación Sanitaria de Aragón, Universidad de Zaragoza, 50013 Zaragoza, Spain ; CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain
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13
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Vlaic S, Hoffmann B, Kupfer P, Weber M, Dräger A. GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML. Bioinformatics 2013; 29:2216-7. [PMID: 23803467 DOI: 10.1093/bioinformatics/btt370] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
UNLABELLED GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage. AVAILABILITY GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490. SUPPLEMENTARY INFORMATION General information on GRN2SBML, examples and tutorials are available at the tool's web page.
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Affiliation(s)
- Sebastian Vlaic
- Leibnitz Institute for Natural Product Research and Infection Biology-Hans-Knöll-Institute, Beutenbergstrasse 11a, Jena, Germany.
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