Bioinformatics and Computational Biology: Bottlenecks and Options
CHI Insight Pharma Reports
June 1, 2009 134 Pages - SKU: CHI2390522
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The interdisciplinary fields of Bioinformatics and Computational Biology are locked in a high stakes race with analytical instrument developers and innovators. The pace and scope of change in many fields of biomedical research rivals what we once associated only with semiconductor devices. This report explores the interlocking challenges facing instrumentation advances, computational demands and our evolving systems biology knowledge. Key challenges presented in this report include:
- Instrumentation capable of generating terabytes of raw data daily
- Storage requirements for human gene sequences
- Need for cross platform data analysis standards
- Appropriateness of analysis & modeling applications
- Database data quality and annotation protocols
Bioinformatics and Computational Biology: Bottlenecks and Options reviews the state of the art and aims to determine the significant technological and market trends in the application of informatics and computation techniques to biological research and drug discovery. The progress of molecular biology has given us a profound understanding of human physiology and pathology at a molecular level. However, we understand that a functioning organism is more than simply a sum of chemical reactions. In recent years a concerted effort has been directed at moving from a reductionist approach to understanding physiology in an integrative systems framework complete with the associated mathematical-based models.
The growth of systems biology has been aided by the availability of constantly evolving computational capacity of cheap hardware as well as advances in analytical research instruments capable in some applications of generating terabytes of data each day. Such instruments are being used to make time series measurements of multiple-analyte fluxes during the perturbation of a physiological system. The robustness of such data are the building blocks for computational biology.
This report describes the tension the combined fields of Bioinformatics and Computational Biology are experiencing by first reviewing the capabilities of innovative analytical instrumentation to generate terabytes of data and then considering the availability of approaches, both in software and hardware, to compress, store, retrieve and combine these data. The report identifies this supply and demand as a strategic bottleneck issue. The discussion also considers issues of cross platform data analysis standards and the appropriate use of analysis and modeling applications on data quality.
Bioinformatics and Computational Biology: Bottlenecks and Options presents an analysis of the state of the field in terms of the current systems biology models and their applications, where the field is headed and the possible implications for applied biological science. The report also includes profiles of systems biology vendors and their products as well as a discussion of the applications in areas such as personalized medicine and drug discovery. The report closes with an overview of the strategy pressure points and the interlocking challenges inherent with instrumentation advances, computational demands and our evolving systems biology knowledge.
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- Chapter One INTRODUCTION TO BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
- 1.1. Definitions; Principle and Applications
- What are Bioinformatics and Computational Biology?
- What Is an Algorithm?
- Heuristics
- Approximation Algorithms for Parsimony Models
- Neural Networks
- Markov Chains
- B&CB Application of Markov Chain Modeling
- B&CB Application of Markov Chain Modeling to Timing of Antiretroviral Therapy
- Markov Chain Monte Carlo Algorithms
- 1.2. Scope of the Fields
- Overview of Presently Available Software Tools
- Managing Terabytes of Data
- 1.3. Product Categories
- Content Databases
- Data Mining
- Analytical Software and Services
- 1.4. Subsequent Chapters
- Chapter Two TEAMING BIOINFORMATICS AND POWERFUL HARDWARE
- 2.1. Biomedical Imagery Hardware
- Computerized Axial Tomography
- Magnetic Resonance Imaging
- Positron Emission Tomography
- Ultrasonography
- 2.2. Mass Spectrometry
- Theoretical Basis
- Bioinformatics Applications
- Sample Preparation for Mass Spectrometry
- 2.3. X-ray Crystallography
- 2.4. High-throughput Image Analysis
- 2.5. Sequencing
- 2.6. Microarrays
- 2.7. The Future of Imaging and B&CB
- Chapter Three OVERVIEW OF BIOINFORMATICS-DRIVEN APPLICATIONS
- 3.1. Genes, Genomes and Genomics
- 3.2. The Human Genome
- DNA Sequence Analysis
- Alignment
- Databases
- 3.3. Disease Determination
- Alzheimer’s Disease
- Other Genomes
- Comparative Genomics
- 3.4. Gene Regulation
- The Proteome and Proteomics
- Protein Structure Alignment
- Protein Structure Prediction
- Protein-Protein Interactions
- Clustering Algorithms
- The Future of the Proteome
- 3.5. Systems Biology
- 3.6. Biomedical Informatics
- Infectious Diseases and B&CB
- Epidemiology
- Institutional Support for Infectious Disease B&CB
- Population Dynamics of Drug Resistance
- Immunoinformatics
- 3.7. The Nature of Cancer and the Contributions of B&CB to its Elucidation
- Analysis of Mutations in Cancer
- Cancer Biomarkers
- Analysis of Bladder Cancer
- 3.8. Pharma Investigations
- Cheminformatics
- Drug Discovery
- New Uses for Existing Drugs
- Chiral Pharmaceuticals
- Natural Products as New Therapeutics
- In Silico Drug Development
- In Silico Prediction Tools
- Online Drug Resources
- Pharmacogenomics
- 3.9. Forensic Investigations
- Chapter Four THE DILEMMA AHEAD FOR BIOINFORMATICS
- 4.1. Data Proliferation: The Good News and Challenges
- 4.2. Some Storage Solutions
- 4.3. Product and Market Implications
- 4.4. Personalized Medicine
- Single-Gene Mutations and the Concept of Personalized Medicine
- Box 4.1. A company based on a paradigm of personalized medicine
- Genetic Determination by Multiple Factors and the Development of Personalized Medicine
- 4.5. Are GRID Networks the Answer?
- Chapter Five INTERVIEWS WITH BIOINFORMATICS SPECIALISTS
- 5.1. Interview with Tim Riley of Waters Corporation
- 5.2. Interview with Nasri G. Nahas, Chief Executive Officer, Geneva Bioinformatics (GeneBio) S.A.
- 5.3. Interview with Ruedi Aebersold, Chairman, Scientific Executive Board, SystemsX.ch Project, Zurich Switzerland
- 5.4. Interview with John Pestian, PhD, MBA, Director, Computational Medicine Center, Cincinnati Children’s Medical Center and the
- University of Cincinnati
5.5. Interview with Kevin Davies, Editor in Chief, BioIT World
Chapter Six CONCLUSIONS
6.1. B&CB Progress is Driven by Hardware Improvements
6.2. Old, Simplistic Models of Biomedicine Needs to be Critically Reexamined
6.3. Why Has So Little Progress been Made on the “War on Cancer”?
6.4. Toward a Cancer Program Based on B&CB
6.5. The Limitations of In Silico Pharmacology
6.6. The Limitations of B&CB
References
Company Index with Web Addresses
FIGURES
Figure 1.1. Image of Cells and Connections Within the Neocortex Microcircuitry
Figure 1.2. Stanislaw Ulam at work.
Figure 1.3. The Reverend Thomas Bayes.
Figure 2.1. Image showing a microarray experiment.
Figure 3.1. Mapping of SNPs in an idealized section of a chromosome
Figure 3.2 Outline of the Immune System
Figure 3.3. Genome-wide integrative analysis to identify pathways disrupted in cancer.
Figure 3.4. Genomic changes associated with clonal progression of bladder cancer
TABLES
Table 1.1. Biological Disciplines Using Bioinformatics and Computational Biology Showing Examples of Applications
Table 2.1. Companies Offering Mass Spectrometry Devices and Equipment
Table 2.2. Sample Prep Technology for Mass Spectrometry
Table 2.3. Essential Tools for Bioimaging Technologies
Table 2.4. Major Companies with DNA Sequencing Technologies
Table 2.5. Research Opportunities and Forecast
Table 3.1. Companies Providing Consumer-Oriented DNA Analysis through Databases
Table 3.2. NCBI Databases
Table 3.3. Programs in Systems Biology
Table 3.4. List of Publicly-Available Databases
Table 3.5. Some Companies Pursuing Biomarker Technologies
Table 3.6. Options for High-throughput Screening Assays
Table 3.7. In Silico Systems for Toxicity Prediction
Table 3.8. Some Companies Pursuing In Silico Drug Modeling
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