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Using Ontology and Semantic Web Services to Support Modeling in Systems Biology

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Using Ontology and Semantic Web Services to Support Modeling in Systems Biology Centre for Mathematics & Physics in the Life Sciences and EXperimental Biology University College London Using Ontology and Semantic Web Services to Support Modeling in Systems Biology Zhouyang Sun Submitted for the degree of Doctor of Philosophy At University College London December 2008 Revised for the final submission 2009 I, Zhouyang Sun, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Signature: Date: Abstract This thesis addresses the problem of collaboration among experimental biologists and modelers in the study of systems biology by using ontology and Semantic Web Services techniques. Modeling in systems biology is concerned with using experimental information and mathematical methods to build quantitative models across different biological scales. This requires interoperation among various knowledge sources and services. Ontology and Semantic Web Services potentially provide an infrastructure to meet this requirement. In our study, we propose an ontology-centered framework within the Semantic Web infrastructure that aims at standardizing various areas of knowledge involved in the biological modeling processes. In this framework, first we specify an ontology-based meta-model for building biological models. This meta-model supports using shared biological ontologies to annotate biological entities in the models, allows semantic queries and automatic discoveries, enables easy model reuse and composition, and serves as a basis to embed external knowledge. We also develop means of transforming biological data sources and data analysis methods into Web Services. These Web Services can then be composed together to perform parameterization in biological modeling. The knowledge of decision-making and workflow of parameterization processes are then recorded by the semantic descriptions of these Web Services, and embedded in model instances built on our proposed meta-model. We use three cases of biological modeling to evaluate our framework. By examining our ontology-centered framework in practice, we conclude that by using ontology to represent biological models and using Semantic Web Services to standardize knowledge components in modeling processes, greater capabilities of knowledge sharing, reuse and collaboration can be achieved. We also conclude that ontology- based biological models with formal semantics are essential to standardize knowledge in compliance with the Semantic Web vision. TABLE OF CONTENT CHAPTER 1 : INTRODUCTION ...................................................................................... 10 1.1 Background ........................................................................................................ 10 1.2 Methods, Contributions, and Originality ........................................................ 10 1.3 Thesis Outline ..................................................................................................... 11 CHAPTER 2 : MOTIVATION ......................................................................................... 13 2.1 What is Systems Biology? .................................................................................. 13 2.2 What is Involved in Modeling in Systems Biology? ........................................ 14 2.3 Semantic Web for Modeling in Systems Biology ............................................ 15 2.4 Chapter Summary ............................................................................................. 16 CHAPTER 3 : REVIEW OF TECHNIQUES AND RELATED WORK .................................. 17 3.1 Ontology .............................................................................................................. 18 3.1.1 What is Ontology? .......................................................................................... 18 3.1.2 Ontology Representation Levels ..................................................................... 20 3.1.3 Ontology Languages ....................................................................................... 22 3.1.4 Current Development of Ontology in Life Sciences ....................................... 24 3.2 Agent-based Systems and Web Service Infrastructure .................................. 28 3.2.1 Agent-based Systems ...................................................................................... 29 3.2.2 Web Service Infrastructure in the Life Sciences ............................................. 31 3.3 Summary ............................................................................................................. 34 CHAPTER 4 : CASES OF BIOLOGICAL MODELING ...................................................... 35 4.1 Hodgkin & Huxley Case .................................................................................... 36 4.1.1 Biological background .................................................................................... 36 4.1.2 Mathematical modeling .................................................................................. 39 4.2 Lewis & Hudspeth Case .................................................................................... 43 4.2.1 Biological Background ................................................................................... 43 4.2.2 Experimental Data Acquisition ....................................................................... 46 4.2.3 Mathematical Modeling .................................................................................. 47 4.2.4 Computational Simulation .............................................................................. 51 4.3 Case of Hormone-induced Calcium Oscillation Composite Model ............... 54 4.3.1 Background ..................................................................................................... 54 4.3.2 Understand Intracellular Calcium Oscillation by Model Integration ............. 55 4.3.3. Mathematical Modelling ................................................................................ 58 4.4 Chapter Summary ............................................................................................. 65 CHAPTER 5 : USING SEMANTIC WEB TECHNOLOGIES TO SUPPORT BIOLOGICAL MODELING ................................................................................................................... 67 5.1 Workflow of Modeling Processes ..................................................................... 67 5.2 Typology of Modeling Knowledge .................................................................... 76 5.3 From Modeling Knowledge to Semantic Web Components .......................... 78 5.4 Our Approach .................................................................................................... 82 5.4.1 Create abstract biological models by using ontology ..................................... 83 5.4.2 From experimental Data to Database Web Services ....................................... 83 5.4.3 From Analysing Methods to Web Services .................................................... 84 5.4.4 Use OWL-S to specify Parameterisation in Computational Models .............. 84 5.4.5 Outcome .......................................................................................................... 86 5.5 Chapter Summary ............................................................................................. 86 CHAPTER 6 : DESCRIPTION OF THE FRAMEWORK FOR BIOLOGICAL MODELING ... 87 6.1 Build Biological Models in OWL ...................................................................... 88 6.1.1 Using OWL format for the Meta-model ......................................................... 88 6.1.2 Meta-model for the Crucial Modeling Components ....................................... 91 6.1.3 Meta-model Uses Shared Biological Ontologies for Instantiation ................. 95 6.1.4 Use Meta-model to generate computational simulations ................................ 97 6.2 Transforming Experimental Data into Semantic Web Services ................... 97 6.2.1 Transform Data Source into Relational Database ........................................... 99 6.2.2 Generate Java Entity Classes from Relational Database .............................. 102 6.2.3 Generic Java Methods for Database Control ................................................ 106 6.2.4 Semantic Description for Data Web Service ................................................ 108 6.3 Transforming Analysis Methods to Semantic Web Services ....................... 113 6.4 Web Service Composition ............................................................................... 114 6.5 Generate Simulation by using Web Service Composition Models .............. 116 6.6 Summary ........................................................................................................... 117 CHAPTER 7 : FRAMEWORK EVALUATION BY CASE STUDIES ................................... 119 7.1 Model Discovery ............................................................................................... 119 7.2 Model Reuse ..................................................................................................... 127 7.3 Automatic generation of simulations ............................................................. 132 7.4 Model Composition .......................................................................................... 133 7.5 Model Configuration ....................................................................................... 136 7.6 Summary ..........................................................................................................
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