id,collection,dc.contributor.author,dc.contributor.contact,dc.contributor.firstReferee,dc.contributor.furtherReferee,dc.contributor.gender,dc.date.accepted,dc.date.accessioned,dc.date.available,dc.date.issued,dc.description,dc.description.abstract[de],dc.format.extent,dc.identifier.uri,dc.identifier.urn,dc.language,dc.rights.uri,dc.subject,dc.subject.ddc,dc.title,dc.title.translated[de],dc.type,dcterms.accessRights.dnb,dcterms.accessRights.openaire,dcterms.format[de],refubium.affiliation[de],refubium.mycore.derivateId,refubium.mycore.fudocsId,refubium.note.author "ee92ab84-8625-48cb-a1ed-5bc61c281761","fub188/14","Li, Jian","jli@mi.fu-berlin.de","Prof. Dr. Burghardt Wittig","Prof. Dr. Peter N. Robinson","m","2013-10-30","2018-06-07T18:50:26Z","2013-11-07T14:37:08.685Z","2013","Computational Modeling of Cancer-Associated Cellular Systems 2 Dedication..........................................................................................................3 Acknowledgements............................................................................................4 English Abstract................................................................................................ 6 German Abstract................................................................................................7 List of Tables 14 List of Figures 15 Abbreviations 16 1\. Introduction 20 1.1 Tumorigenesis 20 1.1.1 Deregulation of Cellular Regulatory Circuits 21 1.1.2 Personalized Medicine 23 1.2 MicroRNA Regulation Regarding Cancer research 25 1.2.1 MiRNA & Cancer Research 26 1.2.1 MiRNA & EGFR Signaling Pathway 27 1.3 Human Signaling Model 28 1.4 Human Metabolic Model 30 1.5 Gene-regulatory Network 33 1.6 Systems Biology & Software 34 1.6.1 CellDesigner 36 1.6.2 Copasi 37 1.6.3 PySCeS 39 1.6.4 Virtual Cell 40 1.6.5 PyBioS2 42 1.7 Objective and Focus of this Thesis 44 2\. Results 44 2.1 Establishment of an Integrated miRNA-EGFR Signaling Pathway Model 44 2.2 Validation of the Predictive Ability of the miRNA-EGFR Model 48 2.3 Simulation of Functional Ability of mir-192 and mir-181 50 2.4 Evaluation of the “One Hit – Multiple Targets” Concept using miRNA Modeling 54 2.5 Establishment of A Non-Steroidal Anti-Inflammatory Drug Model 59 2.6 Integration of Cancer Hallmarks into NSAID Model and Its Validation 62 2.7 Analysis of COX Based Synthetic Lethality for Breast, Colon and Lung Tumor 66 2.8 The challenges of Construction of A Comprehensive Signaling Model in Human 71 2.9 Components Identification of the Human Signaling (HS) Model 71 2.10 Reassembling Model Components Focused on the Systems-biological Properties 73 2.11 Analysis of Dynamic Behavior of the Biological System underlying the Human Signaling Model 80 2.12 Establishment of a Tumorigenesis Calculation Formula 90 2.13 Utilization of miRNA-expression Data to Discover and Detect the Biomarkers of Individual Cancer Cell Lines 92 2.14 Reconstruction of the Human Metabolic Model and Extension with the miRNA- Regulation Network. 94 2.15 Features and Functionalities of PyBioS2 96 2.15.1 Multiple Database-Connection 97 2.15.2 Data Integration 98 2.15.3 Graphical Interface 100 2.15.4 Model Architecture 101 2.15.5 Semi-Automatic Definition of Reaction 102 2.15.6 ODE System 104 2.15.7 Petri net 105 2.15.8 Utilization of Gene-Expression Data 107 2.15.9 Model Analysis Approaches 108 2.15.10 A Case Study: Comparison of Petri-net simulation strategy and ODE simulation strategy. 110 3\. Materials and Methods 110 3.1 Petri net Extension 110 3.2 Analysis of Significant Changes of Model Components due to the Effect of anti- miRNAs 115 3.3 Model Construction 115 3.4 Model Parameterization 116 3.5 Model Initialization with Gene-expression or miRNA-expression Data and Model Simulation Procedure 116 3.6 Comparison of Predicted Proteome and Experimental Proteome Data 117 3.7 Establishment of A Drug Database 118 3.8 Weight-Factor Estimation for the Tumorigenesis Calculation Formula 119 3.9 Cancer Cell Lines from NCI-60 Stduy and from Cancer Genome Atlas 119 3.10 Sensitivity Score Definition 120 3.11 The implementation of cancer- and process-hallmarks in the human signaling model. 120 3.12 Mathematical Implementation of COX-isoform Specific siRNA and NS-398 Drug Effect 122 4\. Discussion 122 4.1 The miRNA- EGFR (ME) Model 122 4.2 The Non-Steroidal Anti-Inflammatory Drug (NSAID) Model 127 4.3 The Human Signaling (HS) Model 128 4.4 The Human Metabolic Model (HMMA) 135 4.5 PyBioS 2 137 4.6 Conclusions and Future Outlook 139 Reference 142 Publication 156 A. Supplemental Information 159 A.1 miRNA-EGFR model 159 A.2 MicroRNA target validation references for miRNA-EGFR model 159 A.3 Pathway references for human signaling model 159 A.4 MicroRNA target validation references for human signaling model 159 A.5 Feedback loops in the human signaling model 159 A.6 PyBioS 2 Tutorial (step by step) 159 A.7 Semi- automatic reaction definition 173 A.8 Gefitinib, Imatinib and Temsirolimus Drug Association Constant 175 A.9 MicroRNA target validation references for human metabolic model (HMMA) 176 A.10 The Human Metabolic miRNA Model (HMMA) 176 A.11 The Human Signaling Model 176 A.12 The Non-Steroidal Anti- Inflammatory Drug (NSAID) Model 176 B. Supplemental Figure 177 B.1 The connectivity-degree distribution of model components with a linear regression line. 177 B.2 The correlation plot between experimental proteom data and predicted proteome data from the u-2 os cell line (attached in CD). 178 B.3 The correlation plot between experimental proteom data and transcriptome data from the u-2 os cell line (attached in CD). 178 B.4 The correlation plot between experimental proteom data and predicted proteome data from the A-431 cell line (attached in CD). 179 B.5 The correlation plot between experimental proteom data and transcriptome data from the A-431 cell line (attached in CD). 179 B.6 The correlation plot between experimental proteom data and predicted proteome data from the U-251 MG cell line (attached in CD). 180 B.7 The correlation plot between experimental proteom data and transcriptome data from the U-251 MG cell line (attached in CD). 180 B.8 The in-silico simulation experiments of human signaling pathways. 181 B.9 Comparison of ODE simulation and Petri net simulation based on a simple network. 182 B.10 The cancer cell lines from Cancer Genome Atlas.....................................183 C. Supplemental Table 183 C.1 Pathway description 183 C.2 Pathway, key component, transcriptional target and major involved microRNA 198 C.3 Pathway, Ligand, Receptor, downstream targets and crosstalk pathways 198 C.4 The simulation data of 100 specific miRNA Inhibitors 210 C.5 The simulation data of AKT and MEK inhibitor 210 C.6 The simulation data of single ligand-activations in the human signaling model. 210 C.7 The simulation data of 100 specific miRNA Inhibitors 211 C.8 The comparison result of the simulation data with the data from Nagaraj's Study 211 C.9 The drug response prediction data 211 List of Tables PyBioS2_Database_Connection 43 miRNA-EGFR_Model_Component_Summary 45 Top15_anti-miRNA_Inhibitor 58 NSAID_Model_Component_Summary 61 Human_Signaling_Model_Component_Summary 73 Statistics_Feedback_Loops 74 Four_Simulation_Conditions 75 Statistics_Bow_Tile_Structure 78 Hallmarks_Mechanism_Related_Pathways 82 Biomarker_Identification_Result 94 HMMA_Model_Component_Summary 95 Function_Comparison_Systemsbiology_Softwares 97 Semi-Automatic_Reaction_Type 103 Network_Analysis_Methods 108 Component_Concentrations_at_TimePoint_0 114 Component_Concentrations_at_TimePoint_1 114 Kinetic_Parameter_Summary 115 Cancer_Hallmarks_Implementation 121 Mathematical_Implementation_Therapeutic_Intervention 122 Gefitinib_Imatinib_Temsirolimus_Drug_Association_Constant 175 Pathway_Description 183 Pathway_Ligand_Component_Information 198 List of Figures CellDesigner Annotation Interface 37 Copasi User Interface 38 PySCeS Command Line and Output Graph 40 Visualization of Virtual Cell Interface 41 PyBioS2 User Interface and Model Visualization 43 miRNA-EGFR Model Network Overview 46 Comparison of miRNA-EGFR Model Predictions with Experimental Results. 49 Modeling of mir-192 and mir-181c Effects on the EGFR Signaling Pathway 51 Modeling the Individual Effect of 100 anti-miRNAs. 56 Histogram of miRNA/target Relationship 58 The Network Graph of COX Pathway 61 Simplified Visualization of COX-2 Inhibitions within NSAID Model Network 64 Effect of the COX-based Combined Inhibition on Cancer Hallmarks 67 Simplified Human Signaling Model Overview 72 Schematic Representation of the Concept for Functional Redundancy in the Human Signaling Model 76 Heatmap of log2-ratios from the Concentrations of Downstream Components in this Network 76 The Bow- Tile Structure 77 Functional Modularity Regarding the Relationship Between Pathways and Hallmarks 79 Ligand-Activation-Test 84 miRNA Regulation Regarding the Effect of Ligand Activations 84 Workflow of in-silico Simulation Pipeline 86 Signal Propagation Strategy for Inheriting Dynamic Properties of the Underlying Biological System 87 Visualization of the Correlation Measurements A 88 Visualization of the Correlation Measurements B 88 Drug Response Prediction of NCI-60 Cancer Cell Line A 93 Drug Response Prediction of NCI-60 Cancer Cell Line B 93 Schematic Overview of PyBioS2 Functions 98 Diagram of PyBioS2 Internal Database 99 Navigation-Frame and Content-Frame of PyBioS2 101 Internal Structure of a Model in PyBioS2 102 Visualization of Model Network under ODE System Module 104 Visualization of Model Network under Petri Nets Module 107 Transition Fire of a Petri Net 112 Drug Database Diagram 118","Cancer is the consequence of disordered cellular systems. To date, more than hundred distinct types of cancer and subtypes of tumor have been identified in the human body. This complexity does not only provoke a number of questions such as the origin of cancer and its medical treatment, but also poses a high level of challenge to battle against them. The experience gained by diverse cancer-research studies suggests that all differences among traditional cancer and tumor types can be reduced at the molecular level. Thus, it is essential to understand the interplay and relation between oncogenes and tumor suppressors within the cancerous cellular system, which could help to identify the core function of molecular pathways for effective therapeutic intervention. This study tried to realize an important system biological concept that integrates modeling of different types of biological networks including signaling-, metabolic-, gene-regulatory-, and miRNA-regulation- network from the whole organism and generate responsive computational molecular models to describe the behavior of cells (or cancer cells). The models created during this thesis are demonstrated with application-examples to be able to incorporate the experimental and observational inquiry and receive the individual patient information to reflect/predict the dynamic behavior of the underlying cellular systems. This study also tried to elucidate that models of this kind can be utilized for early tumor detection, precise diagnose of the pathological state, the identification of anti- tumor/cancer drug and drug combination effect. Therefore, models of this kind might be able to form a “Virtual Patient” model to propose, verify and predict new therapeutic strategies towards personalized medicine. In addition, a systemsbiological software has been developed during this study. Its novel properties including its internal modeling database can be used to overcome some current limitations of systemsbiology research.||Krebs ist eine Art von Krankheit, die durch funktionale Störungen innerhalb zellulärer Systeme verursacht wird. Bei Menschen und Tieren bezeichnet Krebs maligne Tumoren, die unkontrolliertes Wachstum zeigen, was auf den Verlust der Proliferationskontrolle zurückzuführen ist. Krebs kann aus allen sich teilenden Zelltypen entstehen, sodass bis heute mehr als 100 verschiedene Krebserkrankungen bekannt sind. Gegenstand der Krebsforschung heutzutage ist auf der einen Seite die Aufklärung der Krebs-Entstehung und auf der anderen Seite die Entwicklung von neuen bzw. verbesserten medizinischen Behandlungmöglichkeiten für Krebs. Die aus diversen Studien gewonnenen Erkenntnisse erwecken den Eindruck, dass alle Unterschiede zwischen traditionellen Krebs- und Tumortypen auf die molekulare Ebene zurückgeführt werden können. Daher ist es wichtig, das Zusammenspiel zwischen Onkogenen und Tumorsuppressorgenen in den molekularen Prozessen die zur einzelnen Krebszelle führen, noch genauer zu verstehen, um Krebs auf molekularer Ebene effektiver zu bekämpfen und schließlich zu besiegen. Diese Arbeit versucht, ein wichtiges systembiologisches Konzept zu verwirklichen, indem ein integratives mathematisches Computer-Modell entwickelt wurde, das unterschiedliche biologische Netzwerke (Signal-, metabolische Netzwerke, genregulatorische Netzwerke und miRNA Regulationsnetzwerke) beinhaltet. Dieses integrative Modell besteht aus verschiedene Sub-modelle, die während der Arbeit aufgebaut und validiert worden sind. Die Arbeit hat auch aufgeführt wie man Modelle dieser Art mit Anwendung/Integration von der genetischen Information der individuellen Zelllinien and experimentellen Daten verwenden kann, um das dynamische Verhalten von Krebszellen aufzuklären. Diese Arbeit versucht auch, zu erklären, dass Modelle dieser Art auch für medizinische Anwendungen, z.B. die Früherkennung von Tumoren, die Identifizierung der Wirkstoffe und Diagnose der Wirkung von Anti-Krebs-Medikamenten oder Anti-Krebs- Arzneimittelkombinationen, einsetzbar sind. Daher könnten diese Modelle in der Lage sein, als ein ""Virtueller Patient"" zu funktionieren, und neue therapeutische Strategien zur personalisierten Medizin vorschlagen, überprüfen und vorhersagen. Zusätzlich wurde eine systemsbiologische Software während der Arbeit entwickelt, derer vorteilhafte Eigenschaften, inklusive deren internale Datenbank, die dazu gedient werden kann, Beiträge zur systemsbiologische Forschung zu leisten.","211 S.","https://refubium.fu-berlin.de/handle/fub188/5487||http://dx.doi.org/10.17169/refubium-9686","urn:nbn:de:kobv:188-fudissthesis000000095449-8","eng","http://www.fu-berlin.de/sites/refubium/rechtliches/Nutzungsbedingungen","systems modeling||signaling pathways||miRNA-regulation||biomarker identification||drug reponse prediction","500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie","Computational Modeling of Cancer-Associated Cellular Systems","Computergestützte Modellierung von Krebs-assoziierten zellulären Systemen","Dissertation","free","open access","Text","Biologie, Chemie, Pharmazie","FUDISS_derivate_000000014345||FUDISS_derivate_000000014346||FUDISS_derivate_000000014347||FUDISS_derivate_000000014358","FUDISS_thesis_000000095449","Zum Betrachten der Supplement-Dateien diese erst lokal speichern, dann öffnen, sonst evtl. Fehlermeldung"