GeNGe: systematic generation of gene regulatory networks

Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments

Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network

<p>Abstract</p> <p>Background</p> <p>Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs.</p> <p>Results</p> <p>We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data.</p> <p>Conclusion</p> <p>The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.</p

Simulation of DNA array hybridization experiments and evaluation of critical parameters during subsequent image and data analysis

BACKGROUND: Gene expression analyses based on complex hybridization measurements have increased rapidly in recent years and have given rise to a huge amount of bioinformatic tools such as image analyses and cluster analyses. However, the amount of work done to integrate and evaluate these tools and the corresponding experimental procedures is not high. Although complex hybridization experiments are based on a data production pipeline that incorporates a significant amount of error parameters, the evaluation of these parameters has not been studied yet in sufficient detail. RESULTS: In this paper we present simulation studies on several error parameters arising in complex hybridization experiments. A general tool was developed that allows the design of exactly defined hybridization data incorporating, for example, variations of spot shapes, spot positions and local and global background noise. The simulation environment was used to judge the influence of these parameters on subsequent data analysis, for example image analysis and the detection of differentially expressed genes. As a guide for simulating expression data real experimental data were used and model parameters were adapted to these data. Our results show how measurement error can be balanced by the analysis tools. CONCLUSIONS: We describe an implemented model for the simulation of DNA-array experiments. This tool was used to judge the influence of critical parameters on the subsequent image analysis and differential expression analysis. Furthermore the tool can be used to guide future experiments and to improve performance by better experimental design. Series of simulated images varying specific parameters can be downloaded from our web-site: http://www.molgen.mpg.de/~lh_bioinf/projects/simulation/biotech

ConsensusPathDB—a database for integrating human functional interaction networks

ConsensusPathDB is a database system for the integration of human functional interactions. Current knowledge of these interactions is dispersed in more than 200 databases, each having a specific focus and data format. ConsensusPathDB currently integrates the content of 12 different interaction databases with heterogeneous foci comprising a total of 26 133 distinct physical entities and 74 289 distinct functional interactions (protein–protein interactions, biochemical reactions, gene regulatory interactions), and covering 1738 pathways. We describe the database schema and the methods used for data integration. Furthermore, we describe the functionality of the ConsensusPathDB web interface, where users can search and visualize interaction networks, upload, modify and expand networks in BioPAX, SBML or PSI-MI format, or carry out over-representation analysis with uploaded identifier lists with respect to substructures derived from the integrated interaction network. The ConsensusPathDB database is available at: http://cpdb.molgen.mpg.d

High-Throughput miRNA and mRNA Sequencing of Paired Colorectal Normal, Tumor and Metastasis Tissues and Bioinformatic Modeling of miRNA-1 Therapeutic Applications

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options

Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins

The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects

Modeling of miRNA and Drug Action in the EGFR Signaling Pathway

MicroRNAs have gained significant interest due to their widespread occurrence and diverse functions as regulatory molecules, which are essential for cell division, growth, development and apoptosis in eukaryotes. The epidermal growth factor receptor (EGFR) signaling pathway is one of the best investigated cellular signaling pathways regulating important cellular processes and its deregulation is associated with severe diseases, such as cancer. In this study, we introduce a systems biological model of the EGFR signaling pathway integrating validated miRNA-target information according to diverse studies, in order to demonstrate essential roles of miRNA within this pathway. The model consists of 1241 reactions and contains 241 miRNAs. We analyze the impact of 100 specific miRNA inhibitors (anit-miRNAs) on this pathway and propose that the embedded miRNA-network can help to identify new drug targets of the EGFR signaling pathway and thereby support the development of new therapeutic strategies against cancer

Experimental validation of computerised models of clustering of platelet glycoprotein receptors that signal via tandem SH2 domain proteins

The clustering of platelet glycoprotein receptors with cytosolic YxxL and YxxM motifs, including GPVI, CLEC-2 and PEAR1, triggers activation via phosphorylation of the conserved tyrosine residues and recruitment of the tandem SH2 (Src homology 2) domain effector proteins, Syk and PI 3-kinase. We have modelled the clustering of these receptors with monovalent, divalent and tetravalent soluble ligands and with transmembrane ligands based on the law of mass action using ordinary differential equations and agent-based modelling. The models were experimentally evaluated in platelets and transfected cell lines using monovalent and multivalent ligands, including novel nanobody-based divalent and tetravalent ligands, by fluorescence correlation spectroscopy. Ligand valency, receptor number, receptor dimerisation, receptor phosphorylation and a cytosolic tandem SH2 domain protein act in synergy to drive receptor clustering. Threshold concentrations of a CLEC-2-blocking antibody and Syk inhibitor act in synergy to block platelet aggregation. This offers a strategy for countering the effect of avidity of multivalent ligands and in limiting off-target effects

International Journal of Cancer / Synergistic crosstalk of hedgehog and interleukin6 signaling drives growth of basal cell carcinoma

Persistent activation of hedgehog (HH)/GLI signaling accounts for the development of basal cell carcinoma (BCC), a very frequent nonmelanoma skin cancer with rising incidence. Targeting HH/GLI signaling by approved pathway inhibitors can provide significant therapeutic benefit to BCC patients. However, limited response rates, development of drug resistance, and severe side effects of HH pathway inhibitors call for improved treatment strategies such as rational combination therapies simultaneously inhibiting HH/GLI and cooperative signals promoting the oncogenic activity of HH/GLI. In this study, we identified the interleukin6 (IL6) pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. Mechanistically, we provide evidence that signal integration of IL6 and HH/GLI occurs at the level of cisregulatory sequences by cobinding of GLI and STAT3 to common HHIL6 target gene promoters. Genetic inactivation of Il6 signaling in a mouse model of BCC significantly reduced in vivo tumor growth by interfering with HH/GLIdriven BCC proliferation. Our genetic and pharmacologic data suggest that combinatorial HHIL6 pathway blockade is a promising approach to efficiently arrest cancer growth in BCC patients.(VLID)301234

Modellierung und Simulation biologischer Systeme und Laborverfahren

Mathematical modeling and simulation techniques have turned out to be valuable tools for the understanding of complex systems in different areas of research and engineering. In recent years this approach came to application frequently also in biology resulting in the establishment of the research area systems biology. Systems biology tries to understand the behavior of complex biological systems by means of mathematical approaches. This requires the integration of qualitative and quantitative experimental data into coherent models. Currently, systems biology usually investigates biochemical reaction networks of cellular systems. A challenging task is the construction of large models that requires computer-assisted data integration, simulation and evaluation. In this work I have elaborated technical bases for the computer- assisted modeling of biological systems and experimental techniques. For this I have developed the program PyBioS that provides a user-friendly Web application and brings in automation for several important tasks required for the development, implementation, and simulation of cellular models. For the description of cellular reaction systems PyBioS makes use of object-oriented programming, well established methods for the mathematical description of biochemical reaction systems based on ordinary differential equation systems, and novel interfaces to biochemical pathway databases (e.g., Reactome, KEGG). In addition PyBioS provides several different functions for the analysis and visualization. The benefit obtained by mathematical modeling of biological systems using PyBioS is illustrated for segmentation of the body (somitogenesis) as, e.g., taking place during embryogenesis. The parameterized somitogenesis model I have developed comprises three signaling pathways, namely Notch, Wnt, and FGF that are known to be relevant for somitogenesis. The model shows a regular oscillation controlled by extracellular Wnt3a. Below a critical threshold concentration of Wnt3a the oscillation that is controlled by Wnt signaling arrests and approaches a steady state. These findings are conform to experimental observations found during determination of somite boundaries. Besides the analysis of biological systems, modeling strategies can also be used for the evaluation of biotechnological experimental techniques. To study this I have perfomed simulations of DNA array hybridization experiments for the evaluation of critical parameters during subsequent image and data analysis. Therefore I have carried out simulation studies on several error parameters arising in complex hybridization experiments, such as spot shape, spot position and background noise. My results show how measurement errors can be balanced by the analysis tools.In verschiedenen Bereichen der Natur- und Ingenieurswissenschaften hat sich die mathematische Modellierung als ein geeignetes Werkzeug erwiesen, um komplexe Systeme besser zu verstehen. Dieser Ansatz findet auch immer häufiger Anwendung in der Biologie und führte zur Etablierung der Systembiologie. Die Systembiologie versucht mit Hilfe mathematischer Ansätze das komplexe Verhalten biologischer Systeme besser zu verstehen. Dies erfordert die Integration qualitativer und quantitativer Daten in kohärente Modelle. Derzeit werden in der Systembiologie häufig biochemische Reaktionsnetzwerke zellulärer Systeme betrachtet. Eine besondere Herausforderung stellt dabei die Modellierung grosser Systeme dar, die eine massive, computergestützte Datenintegration, Simulation und Auswertung erfordert. In dieser Arbeit habe ich Grundlagen für die computergestützte Modellierung biologischer Systeme und experimenteller Verfahren erarbeitet. Das von mir hierfür entwickelte Programm PyBioS bietet eine benutzerfreundliche Web-Schnittstelle und automatisiert viele Schritte, die für die Erstellung, Implementierung und Simulation zellulärer Modelle erforderlich sind. Für die Beschreibung der Modelle wurden dabei objektorientierte Ansätze der Informatik, etablierte Methoden der Modellierung biochemischer Reaktionssysteme basierend auf gewöhnlichen Differentialgleichungssystemen, sowie neuartige Schnittstellen zu Datenbanken biochemischer Reaktionswege (z.B. Reactome, KEGG) genutzt bzw. implementiert. Zudem bietet PyBioS verschiedene Funktionalitäten für die Analyse und Visualisierung. Unter Verwendung von PyBioS wird am Beispiel der embryonalen Segmentierung (Somitogenese) gezeigt, wie mathematische Modellierung zum Verständnis biologischer Systeme beitragen kann. Das von mir entwickelte parametrisierte Modell umfasst die Signalwege Notch, Wnt und FGF, von denen bekannt ist, dass sie an der Determinierung der Somitenbildung beteiligt sind. Das Modell zeigt eine von extrazellulärem Wnt3a kontrollierte Oszillation. Unterhalb einer kritischen Wnt3a Konzentration bricht die vom Wnt Signalweg kontrollierte Oszillation ab und geht in einen stationären Zustand über, der den Beobachtungen für die Determination einer Somitengrenze entspricht. Neben der Analyse biologischer Systeme kann Modellierung auch für die Evaluation biotechnologischer, experimenteller Methoden genutzt werden. Dies wurde für DNA-Array Hybridisierungsexperimente genauer untersucht. Anhand simulierter Daten wurden kritische Parameter der anschliessenden Bild- und Datenanlayse bewertet. Hierfür habe ich Simulationsstudien verschiedener experimenteller Parameter komplexer Hybridisierungsexperimente, wie z.B. der Spot-Form und Spot-Position, oder dem Hintergrundrauschen, durchgeführt. Meine Ergebnisse zeigen, wie Messfehler anhand geeingeter Analyseprogramme kompensiert werden können