Contents -- Chapter 1: Introduction -- 1.1 Part I: Emerging Technologies to Understand Biological Systems -- 1.1.1 Knife-Edge Scanning Microscopy: High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures -- 1.1.2 4D Imaging of Multicomponent Biological Systems -- 1.1.3 Utilizing Parallel Processing in Computational Biology Applications -- 1.2 Part II: Understanding and Utilizing Parallel Processing Techniques -- 1.2.1 Introduction to High-Performance Computing Using MPI and OpenMP -- 1.2.2 Parallel Feature Extraction -- 1.2.3 Machine Learning Techniques for Large Data -- 1.3 Part III: Specific Applications of Parallel Computing -- 1.3.1 Scalable Image Registration and 3D Reconstruction at Microscopic Resolution -- 1.3.2 Data Analysis Pipeline for High-Content Screening in Drug Discovery -- 1.3.3 Information About Color and Orientation in the Primate Visual Cortex -- 1.3.4 High-Throughput Analysis of Microdissected Tissue Samples -- 1.3.5 Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data -- 1.4 Part IV: Postprocessing -- 1.4.1 Bisque: A Scalable Biological Image Database and Analysis Framework -- 1.4.2 High-Performance Computing Applications for Visualization of Large Microscopy Images -- 1.5 Conclusion -- Acknowledgments -- Part I: Emerging Technologies to Understand Biological Systems -- Chapter 2: Knife-Edge Scanning Microscopy:High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures -- 2.1 Background -- 2.1.1 High-Throughput, Physical-Sectioning Imaging -- 2.1.2 Volumetric Data Analysis Methods -- 2.2 Knife-Edge Scanning Microscopy -- 2.3 Tracing in 2D -- 2.4 Tracing in 3D -- 2.5 Interactive Visualization -- 2.6 Discussion -- 2.6.1 Validation and Editing -- 2.6.2 Exploiting Parallelism -- 2.7 Conclusion -- Acknowledgments -- References.

Chapter 3: Parallel Processing Strategies for Cell Motility and Shape Analysis -- 3.1 Cell Detection -- 3.1.1 Flux Tensor Framework -- 3.1.2 Flux Tensor Implementation -- 3.2 Cell Segmentation Using Level Set-Based Active Contours -- 3.2.1 Region-Based Active Contour Cell Segmentation -- 3.2.2 Edge-Based Active Contour Cell Segmentation -- 3.2.3 GPU Implementation of Level Sets -- 3.2.4 Results and Discussion -- 3.3 Cell Tracking -- 3.3.1 Cell-to-Cell Temporal Correspondence Analysis -- 3.3.2 Trajectory Segment Generation -- 3.3.3 Distributed Cell Tracking on Cluster of Workstations -- 3.3.4 Results and Discussion -- References -- Chapter 4: Utilizing Parallel Processing in Computational Biology Applications -- 4.1 Introduction -- 4.2 Algorithms -- 4.2.1 Tumor Cell Migration -- 4.2.2 Tissue Environment -- 4.2.3 Processes Controlling Individual Tumor Cells -- 4.2.4 Boundary Conditions -- 4.2.5 Nondimensionalization and Parameters -- 4.2.6 Model Simulation -- 4.3 Decomposition -- 4.3.1 Moving of Tumor Cells -- 4.3.2 Copying of Tumor Cells -- 4.3.3 Copying of Continuous Variables -- 4.3.4 Blue Gene Model Simulation -- 4.3.5 Multithreaded Blue Gene Model Simulation -- 4.4 Performance -- 4.5 Conclusions -- Acknowledgments -- References -- Part II: Understanding and Utilizing Parallel Processing Techniques -- Chapter 5: Introduction to High-Performance Computing Using MPI -- 5.1 Introduction -- 5.2 Parallel Architectures -- 5.3 Parallel Programming Models -- 5.3.1 The Three P's of a Parallel Programming Model -- 5.4 The Message Passing Interface -- 5.4.1 The Nine Basic Functions to Get Started with MPI Programming -- 5.4.2 Other MPI Features -- 5.5 Other Programming Models -- 5.6 Conclusions -- References -- Chapter 6: Parallel Feature Extraction -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Serial Block-Face Scanning -- 6.3 Computational Methods.

6.3.1 3D Filtering -- 6.3.2 3D Connected Component Analysis -- 6.3.3 Mathematical Morphological Operators -- 6.3.4 Contour Extraction -- 6.3.5 Requirements -- 6.4 Parallelization -- 6.4.1 Computation Issues -- 6.4.2 Communication Issues -- 6.4.3 Memory and Storage Issues -- 6.4.4 Domain Decomposition for Filtering Tasks -- 6.4.5 Domain Decomposition for Morphological Operators -- 6.4.6 Domain Decomposition for Contour Extraction Tasks -- 6.5 Computational Results -- 6.5.1 Median Filtering -- 6.5.3 Related Work -- 6.5.2 Contour Extraction -- 6.6 Conclusion -- References -- Chapter 7: Machine Learning Techniques for Large Data -- 7.1 Introduction -- 7.2 Feature Reduction and Feature Selection Algorithms -- 7.3 Clustering Algorithms -- 7.4 Classification Algorithms -- 7.5 Material Not Covered in This Chapter -- References -- Part III: Specific Applications of Parallel Computing -- Chapter 8: Scalable Image Registration and 3D Reconstruction at Microscopic Resolution -- 8.1 Introduction -- 8.2 Review of Large-Scale Image Registration -- 8.2.1 Common Approaches for Image Registration -- 8.2.2 Registering Microscopic Images for 3D Reconstruction in Biomedical Research -- 8.2.3 HPC Solutions for Image Registration -- 8.3 Two-Stage Scalable Registration Pipeline -- 8.3.1 Fast Rigid Initialization -- 8.3.2 Nonrigid Registration -- 8.3.3 Image Transformation -- 8.3.4 3D Reconstruction -- 8.4 High-Performance Implementation -- 8.4.1 Hardware Arrangement -- 8.4.2 Workflow -- 8.4.3 GPU Acceleration -- 8.5 Experimental Setup -- 8.5.1 Benchmark Dataset and Parameters -- 8.5.2 The Multiprocessor System -- 8.6 Experimental Results -- 8.6.1 Visual Results -- 8.6.2 Performance Results -- 8.7 Summary -- References -- Chapter 9: Data Analysis Pipeline for High Content Screening in Drug Discovery -- 9.1 Introduction -- 9.2 Background -- 9.3 Types of HCS Assay.

9.4 HCS Sample Preparation -- 9.4.1 Cell Culture -- 9.4.2 Staining -- 9.5 Image Acquisition -- 9.6 Image Analysis -- 9.7 Data Analysis -- 9.7.1 Data Process Pipeline -- 9.7.2 Preprocessing Normalization Module -- 9.7.3 Dose Response and Confidence Estimation Module -- 9.7.4 Automated Cytometry Classification Module -- 9.8 Factor Analysis -- 9.9 Conclusion and Future Perspectives -- Acknowledgments -- References -- Chapter 10: Information About Color and Orientation in the Primate Visual Cortex -- 10.1 Introduction -- 10.1.1 Monitoring Activity in Neuronal Populations: Optical Imaging and Other Methods -- 10.2 Methods and Results -- 10.3 Discussion -- Acknowledgments -- References -- Chapter 11: High-Throughput Analysis of Microdissected Tissue Samples -- 11.1 Introduction -- 11.2 Microdissection Techniques and Molecular Analysis of Tissues -- 11.2.1 General Considerations -- 11.2.2 Fixation----A Major Consideration When Working with Tissue Samples -- 11.2.3 Why Is Microdissection Important When Using Tissue Samples? -- 11.2.4 Tissue Microdissection Techniques -- 11.3 DNA Analysis of Microdissected Samples -- 11.3.1 General Considerations -- 11.3.2 Loss of Heterozygosity (LOH) -- 11.3.3 Global Genomic Amplification -- 11.3.4 Epigenetic Analysis -- 11.3.5 Mitochondrial DNA Analysis -- 11.4 mRNA Analysis of Microdissected Samples -- 11.4.1 General Considerations -- 11.4.2 Expression Microarrays -- 11.4.3 Quantitative RT-PCR -- 11.5 Protein Analysis of Microdissected Samples -- 11.5.1 General Considerations -- 11.5.2 Western Blot -- 11.5.3 Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE) -- 11.5.4 Mass Spectrometry -- 11.5.5 Protein Arrays -- 11.6 Statistical Analysis of Microdissected Samples -- 11.6.1 General Considerations -- 11.6.2 Quantification of Gene Expression -- 11.6.3 Sources of Variation When Studying Microdissected Material.

11.6.4 Comparisons of Gene Expression Between Two Groups -- 11.6.5 Microarray Analysis -- 11.7 Conclusions -- References -- Chapter 12: Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data -- 12.1 Introduction -- 12.1.1 fMRI Image Analysis Using the General Linear Model (GLM) -- 12.1.2 fMRI Image Analysis Based on Connectivity -- 12.2 The Theory of Granger Causality -- 12.2.1 The Linear Simplification -- 12.2.2 Sparse Regression -- 12.2.3 Solving Multivariate Autoregressive Model Using Lasso -- 12.3 Implementing Granger Causality Analysis on the Blue Gene/L Supercomputer -- 12.3.1 A Brief Overview of the Blue Gene/L Supercomputer -- 12.3.2 MATLAB on Blue Gene/L -- 12.3.3 Parallelizing Granger Causality Analysis -- 12.4 Experimental Results -- 12.4.1 Simulations -- 12.4.2 Simulation Setup -- 12.4.3 Results -- 12.4.4 Analysis of fMRI Data -- 12.5 Discussion -- References -- Part IV: Postprocessing -- Chapter 13: Bisque: A Scalable Biological Image Database and Analysis Framework -- 13.1 Introduction -- 13.1.1 Datasets and Domain Needs -- 13.1.2 Large-Scale Image Analysis -- 13.1.3 State of the Art: PSLID, OME, and OMERO -- 13.2 Rationale for Bisque -- 13.2.1 Image Analysis -- 13.2.2 Indexing Large Image Collections -- 13.3 Design of Bisque -- 13.3.1 DoughDB: A Tag-Oriented Database -- 13.3.2 Integration of Information Resources -- 13.3.3 Distributed Architecture for Scalable Computing -- 13.3.4 Analysis Framework -- 13.4 Analysis Architectures for Future Applications -- 13.5 Concluding Remarks -- References -- Chapter 14: High-Performance Computing Applications for Visualization of Large Microscopy Images -- 14.1 Mesoscale Problem: The Motivation -- 14.2 High-Performance Computing for Visualization -- 14.2.1 Data Acquisition -- 14.2.2 Computation -- 14.2.3 Data Storage and Management.

14.2.4 Moving Large Data with Optical Networks.