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Preface; Contents; Contributors; Acronyms; Part I Signal Analysis; 1 Visual Analytics of Signalling Pathways Using Time Profiles; 1.1 Introduction; 1.1.1 Challenges in Visualising High-Throughput Time-Series Post-translationally Modified Proteomic Datasets; 1.1.2 Aims; 1.2 Methods; 1.2.1 Phosphorylation Dataset for Insulin Response; 1.2.1.1 Data Representation; 1.2.2 Heat Map of the Time-Series Data; 1.2.2.1 Selecting a Single Time Point for Each Phosphorylation; 1.2.3 The Minardo Layout; 1.3 Results; 1.3.1 Evaluation of the Minardo Visualisation Strategy; 1.3.1.1 Requested Features
1.3.2 Minardo in the International DREAM8 Competition1.3.2.1 Proposed Workflow; 1.4 Discussion and Further Work; 1.4.1 Minardo as a Web-Based Tool; 1.4.2 Lessons from the Usability Study; 1.4.3 Using 3D Structure Information; 1.4.4 Going Beyond Static Roadmaps; 1.4.5 Visualisation for Multiple Experiments; 1.4.6 Limitations; References; 2 Modeling of Testosterone Regulation by Pulse-ModulatedFeedback; 2.1 Introduction; 2.2 A Pulse-Modulated Mathematical Model of Testosterone Regulation; 2.3 Parameter Estimation; 2.3.1 Estimating the GnRH Impulses; 2.3.1.1 Estimating Firing Times and Weights
2.3.1.2 Estimating the Parameters2.3.2 Estimating the Testosterone Dynamics; 2.4 Experimental Results; 2.5 Simulations of the Closed-Loop System; References; 3 Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences; 3.1 Introduction; 3.2 Multiple Change-Point Problem; 3.3 Framework of the Algorithms; 3.3.1 Quickest Change-Point Detection; 3.3.2 The Cross-Entropy Method; 3.3.2.1 Bonferroni Correction for Multiple Hypothesis Testing; 3.4 Numerical Results; 3.4.1 Results on Artificially Generated Data; 3.4.2 Results on Real Data; 3.4.2.1 Fibroblast Cell Lines Data
3.4.2.2 Breast Tumor DataReferences; 4 Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease; 4.1 Introduction; 4.2 The Extraocular Muscles; 4.3 Smooth Pursuit; 4.4 Eye Tracking; 4.5 Parkinson's Disease; 4.6 Probability Density Estimation; 4.6.1 Stochastic Variables; 4.6.2 Kernel Density Estimation; 4.6.3 Orthogonal Series Approximation; 4.6.4 Finding the Outlier Region; 4.7 Non-parametric Method; 4.8 Parametric Method; 4.9 Visual Stimuli; 4.10 Experiment; 4.11 Results; 4.11.1 Non-parametric Method; 4.11.2 Parametric Method; References
5 Identification of the Reichardt Elementary Motion Detector Model5.1 Background; 5.2 Mathematical Model of EMD; 5.2.1 Single Frequency Sinusoidal Signal; 5.2.1.1 Symmetrical and Non-symmetrical EMD Model; 5.2.2 EMD Response to a L2 Pulse; 5.3 Identification Approach; 5.3.1 Identification of a Single EMD; 5.3.1.1 Pure Time-Delay Model; 5.3.2 Identification of a Layer of EMDs; 5.3.2.1 Identifiability Properties for EMD-Layer Estimation; 5.3.2.2 Spatial Excitation of a Sum of Sinusoidal Gratings: An Example; 5.3.2.3 Visualization; 5.4 Experiments; 5.4.1 Periodicity in the Experimental Data
1.3.2 Minardo in the International DREAM8 Competition1.3.2.1 Proposed Workflow; 1.4 Discussion and Further Work; 1.4.1 Minardo as a Web-Based Tool; 1.4.2 Lessons from the Usability Study; 1.4.3 Using 3D Structure Information; 1.4.4 Going Beyond Static Roadmaps; 1.4.5 Visualisation for Multiple Experiments; 1.4.6 Limitations; References; 2 Modeling of Testosterone Regulation by Pulse-ModulatedFeedback; 2.1 Introduction; 2.2 A Pulse-Modulated Mathematical Model of Testosterone Regulation; 2.3 Parameter Estimation; 2.3.1 Estimating the GnRH Impulses; 2.3.1.1 Estimating Firing Times and Weights
2.3.1.2 Estimating the Parameters2.3.2 Estimating the Testosterone Dynamics; 2.4 Experimental Results; 2.5 Simulations of the Closed-Loop System; References; 3 Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences; 3.1 Introduction; 3.2 Multiple Change-Point Problem; 3.3 Framework of the Algorithms; 3.3.1 Quickest Change-Point Detection; 3.3.2 The Cross-Entropy Method; 3.3.2.1 Bonferroni Correction for Multiple Hypothesis Testing; 3.4 Numerical Results; 3.4.1 Results on Artificially Generated Data; 3.4.2 Results on Real Data; 3.4.2.1 Fibroblast Cell Lines Data
3.4.2.2 Breast Tumor DataReferences; 4 Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease; 4.1 Introduction; 4.2 The Extraocular Muscles; 4.3 Smooth Pursuit; 4.4 Eye Tracking; 4.5 Parkinson's Disease; 4.6 Probability Density Estimation; 4.6.1 Stochastic Variables; 4.6.2 Kernel Density Estimation; 4.6.3 Orthogonal Series Approximation; 4.6.4 Finding the Outlier Region; 4.7 Non-parametric Method; 4.8 Parametric Method; 4.9 Visual Stimuli; 4.10 Experiment; 4.11 Results; 4.11.1 Non-parametric Method; 4.11.2 Parametric Method; References
5 Identification of the Reichardt Elementary Motion Detector Model5.1 Background; 5.2 Mathematical Model of EMD; 5.2.1 Single Frequency Sinusoidal Signal; 5.2.1.1 Symmetrical and Non-symmetrical EMD Model; 5.2.2 EMD Response to a L2 Pulse; 5.3 Identification Approach; 5.3.1 Identification of a Single EMD; 5.3.1.1 Pure Time-Delay Model; 5.3.2 Identification of a Layer of EMDs; 5.3.2.1 Identifiability Properties for EMD-Layer Estimation; 5.3.2.2 Spatial Excitation of a Sum of Sinusoidal Gratings: An Example; 5.3.2.3 Visualization; 5.4 Experiments; 5.4.1 Periodicity in the Experimental Data