Bielefeld University Faculty of Technology - VANESA - A bioinformatics software application for the modeling, visualization, analysis, and simulation of biological networks in systems biology applications PhD Thesis Doctor of Engineering Author: Sebastian Jan Janowski May 13, 2013 International Graduate Program Bioinformatics of Signaling Networks PhD Thesis at the International Graduate Program Bioinformatics of Signaling Networks. Submitted to the Faculty of Technology at the Bielefeld University, Germany to obtain the doctorate Doctor of Engineering (Dr.-Ing.). Title: VANESA - A bioinformatics software application for the modeling, visualization, analysis, and simulation of biological networks in systems biology applications. Author: M.Sc. Sebastian Jan Janowski Completion of Work: May 13, 2013 Supervisors: Prof. Dr. Ralf Hofestädt Head of the Department of Bioinformatics and Medical Informatics Prof. Dr. Christian Kaltschmidt Head of the Department of Cell Biology Prof. Dr. Barbara Kaltschmidt Head of the Department of Neurobiology Prof. Dr. Jens Stoye Dean of the Faculty of Technology Head of the Research Group Genome Informatics Graduate School Responsible: Prof. Dr. Karl-Josef Dietz Head of the Graduate School Head of the Department of Cellular and Developmental Biology Dr. Kolja Henckel Coordinator of the Graduate School Address of the Responsible Institution: Bielefeld University Faculty of Technology Universitässtraße 25 33615 Bielefeld Germany Abstract This work presents VANESA, a powerful and easy-to-use modeling software. The software application is laid out to support scientists from the natural sciences in the modeling and analysis of biological systems to better understand biological processes. Therefore, it combines different fields of research, such as information fusion, modeling, analysis, simulation, and network visualization, which are some of the most important areas in bioinformatics and systems biology. Using VANESA, scientists have the possibility to automatically reconstruct important biomedi- cal systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. This results in biological mod- els, which enable scientists to focus on complex interactions and/or to investigate the role of individual components and processes within whole biological systems. Furthermore, users have the possibility to use graph theoretical approaches in VANESA to identify regulatory struc- tures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA for hypothesis generation and in silico experiments. VANESA can be applied in many different life sciences, such as fundamental biology, theoreti- cal biology, systems biology, biotechnology, and medical research, among others. The software application has already been proven useful in several biological and medical application cases [JKT+10, KHA+10, STK+10, JKH+11, PJB+12, PJHB12, KJB+12], in which the provided fea- tures were applied to an increasing number of biochemical problems such as signal transduction, cellular rhythms and cell-to-cell communication, among others. Although it is primarily ad- dressed to members of the laboratory, it can be used by any scientist. All interested people, who would like to use VANESA for their own research can download VANESA at http://vanesa.sf.net or start it via Java web start. It is platform-independent and free-of-charge. List of Contents List of Figures i List of Tables iii Abbreviations iv 1 Introduction 1 1.1 Aims and objectives . .3 1.2 Structure of the work . .5 2 Background 7 2.1 Cellular life . .8 2.2 Biological networks . 11 2.3 Graph theory . 14 2.4 Biological standards . 17 2.5 System modeling . 18 2.6 Databases . 32 2.7 Network reconstruction . 37 2.8 Discussion . 38 3 Related work 41 3.1 Competitive bioinformatics software applications . 41 3.2 Petri net analysis . 53 3.3 Centrality measurements . 56 3.4 Biological databases . 62 3.5 Data integration approaches . 65 3.6 Standard exchange formats . 67 3.7 Discussion . 71 4 Design and system architecture 73 4.1 Design requirements . 73 4.2 System architecture . 81 4.3 Discussion . 84 5 Implementation 85 5.1 Data model . 85 5.2 Network reconstruction . 90 5.3 Petri net simulation processing . 94 5.4 Petri net analysis . 98 5.5 Graph theoretical analysis . 103 5.6 Network comparison . 108 5.7 Network visualization and interaction . 114 5.8 Data exchange . 117 5.9 Feature Summary . 119 6 Application cases 123 6.1 Identification of novel cholesteatoma-related genes . 124 6.2 Investigation on the dilated cardiomyopathy disease . 128 6.3 Modeling the NF-κB system . 131 6.4 Modeling cell-to-cell communication . 138 6.5 Summary . 141 7 Summary 143 7.1 Future perspectives . 145 7.2 Discussion . 147 Bibliography 149 About the author 171 Meaning of the PhD . 171 Acknowledgements . 172 Education . 173 LIST OF FIGURES i List of Figures 1.1 VANESA’s aims and objectives . .4 2.1 Graph types . 15 2.2 Hill function . 19 2.3 Object-oriented modeling . 21 2.4 Lindenmayer system . 22 2.5 Cellular automaton . 23 2.6 Bayesian network . 25 2.7 Boolean network . 25 2.8 Petri net . 28 2.9 HFPN formalism . 29 2.10 SBGN entity relationship diagram . 30 2.11 Growth of databases from 1980 to 2010 . 35 2.12 Number of listed databases in NAR from 1999 to 2012 . 35 2.13 KEGG data structure . 39 3.1 Number of software applications providing SBML . 42 3.2 CellDesigner . 43 3.3 CellIllustrator . 44 3.4 Cytoscape plugin BioNetBuilder . 45 3.5 E-Cell . 46 3.6 Gepasi . 47 3.7 JDesigner . 48 3.8 PNlib . 49 3.9 Snoopy . 50 3.10 Petri net reachability graph . 55 3.11 Petri net coverability graph . 56 3.12 Distribution of graphs with the same average neighbor degree . 59 3.13 Distribution of graphs with the same shortest path degree . 60 3.14 Distribution of graphs with the same matching index . 61 ii LIST OF FIGURES 4.1 Network Editor . 74 4.2 VANESA’s system architecture . 82 5.1 Data integration and consulting architecture of VANESA and DAWIS-M.D. 91 5.2 Network reconstruction in VANESA . 93 5.3 Communication design between VANESA and Dymola . 95 5.4 Petri net simulation of the lac-operon system in VANESA . 96 5.5 Petri net animation in VANESA . 97 5.6 Weighted Petri net with corresponding incidence matrix . 100 5.7 Basic Petri net with initial marking . 101 5.8 Reachability graph in VANESA . 104 5.9 Biological hub detection measurement in VANESA . 108 5.10 Centrality measurement in VANESA . 109 5.11 Heat-graph approach in VANESA . 110 5.12 2.5D comparison function in VANESA . 112 5.13 Zoom-in of a 2.5D comparison function result in VANESA . 112 5.14 Comparison of networks in VANESA . 113 5.15 Neuromorphic model study on network visualization . 115 5.16 Inverted background and foreground in VANESA . 116 5.17 Visualization of selected network elements in VANESA . 117 5.18 VANESA’s webpage . 119 6.1 Application case cholesteatoma: S100 protein-protein interaction network . 127 6.2 Application case cholesteatoma: RT-PCR . ..