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Strategic Analysis of the World Computational Biology MarketsPublished by: Frost & Sullivan Published: Jun. 28, 2005 Table of Contents1. Executive Summary 1. Executive Summary 1. Summary of Findings 2. Exchange Rates and Glossary of Terms 1. Exchange Rates and Glossary of Terms 1. Exchange Rates Used in the Research Service 2. Glossary of Terms 3. Computational Biology Overview 1. Definition 1. Definition 2. Research Scope and Methodology 2. Technology Overview 1. Technological Overview 2. Primary Modeling Approaches 3. Applications of Computational Biology 1. Applications of Computational Biology 4. Major Initiatives of Computational Biology Across the Globe 1. Major Initiatives of Computational Biology Across the Globe 4. Industry Challenges 1. Industry Challenges 1. Introduction 2. Challenges and Issues 1. Challenges and Issues 5. Market Engineering Research for World Computational Biology Market 1. Market Overview 1. Overview 2. Market Segmentation 1. Segmentation 3. Market Engineering Measurements for World Computational Biology Markets 1. Market Engineering Measurements 4. Computational Biology Market Drivers 1. Market Drivers a. Systems Approach Increases the Value of the Data Being Generated b. Increasing Use of High Throughput Systems Increasing the Number of Compounds at Each Stage of the Process c. Reducing Drug Pipeline in the Industry Resulting in Companies Looking for Tools to Increase Drug Research Productivity d. Elimination of False Leads at an Early Stage Cuts Down Cost 5. Market Restraints 1. Restraints a. Pharmaceutical Companies Conservative Attitude on New Technologies b. Proprietary and Incompatible Data Formats Limit Value c. Saturated Big Pharmaceutical Market 6. World Computational Biology Revenue Forecasts 1. Revenue Forecasts 7. World Computational Biology Geographical Trends 1. Geographical Trends 8. World Computational Biology Technology Trends 1. Technical Trends 9. World Computational Biology Competitive Structure 1. Competitive Structure 6. Market Engineering Measurements for Biological Pathway Modeling and Simulation 1. Introduction 1. Introduction 2. Market Overview 1. Market Overview 3. Revenue Forecasts 1. Revenue Forecasts 7. Market Engineering Measurements for Cellular Modeling and Simulation 1. Introduction 1. Introduction 2. Market Overview 1. Market Overview 3. Revenue Forecasts 1. Revenue Forecasts 8. Market Engineering Measurements for Tissue Modeling and Simulation 1. Introduction 1. Introduction 2. Market Overview 1. Market Overview 3. Revenue Forecasts 1. Revenue Forecasts 9. Market Engineering Measurements for Disease Modeling and Simulation 1. Introduction 1. Introduction 2. Market Overview 1. Market Overview 3. Revenue Forecasts 1. Revenue Forecasts 10. Market Engineering Strategic Recommendations 1. Strategic Recommendations 1. Market Engineering Strategic Recommendations 11. Frost & Sullivan Awards for the World Computational Biology Market 1. Frost & Sullivan Award for World Computational Biology Markets 1. Frost & Sullivan Awards 2. Technology Leadership Award 3. Entrepreneurial Company Award 12. Database of Key Industry Participants 1. Database of Key Industry Participants 1. Database of Key Industry Participants 13. Decision Support Databases 1. Biotech Companies and Number of Employees 1. Number of Biotech Companies 2. Number of Employees in Biotech Companies 2. Government R&D Expenditure 1. Government R&D Expenditure 3. Private R&D Investment In Biotechnology 1. Private R&D Investment In Biotechnology 4. Pharma R&D Expenditure 1. Pharmaceutical R&D Expenditure AbstractValidation of Computational Biology Tools Essential to Augment Sales among Pharmaceutical CompaniesAlthough computational biology tools have been around for a long time, their adoption is still in its initial stage. Pharmaceutical companies that have invested heavily in these tools have yet to see any tangible returns and are naturally skeptical about their efficacy. Therefore, vendors of these tools need to validate themselves by conducting case studies and wet lab experiments to demonstrate the benefits of their products. This Frost & Sullivan research service provides comprehensive analysis of investment and growth opportunities in the world computational biology markets. The study segments the market into pathway modeling, tissue modeling, cellular modeling, and disease modeling tools. It also discusses the various market trends while providing in-depth market share analysis, revenue and market forecasts, and exhaustive discussions on the drivers and restraints. Systems Approach Enable Integration of Data from Multiple Sources Computational biology involves integration of data from various sources to model a biological process. "Scientists are looking to computational biology to build predictive models that give insights into how a drug affects a particular disease progression by utilizing the present day deluge of information," says the analyst of this research. However, the data currently available is from varied sources and is often corrupt. Furthermore, despite scientists having developed their own individual databases there has been very little standardization or coordination among them. As a result, many systems are unable to communicate with each other and fail to integrate the huge volume of data available to model a biological process. Hence, the need for standard data formats and interfaces is a major force in computational biology markets. Scientists have realized that existing approaches need to be augmented by a systems approach by which data from different sources can be combined to form a predictive model. Systems approach can enhance computational biology tools by allowing the dynamic utilization of such data for a variety of purposes. The increased value presented by this technology has the potential to revolutionize the drug discovery process worldwide. Computational Biology Tools Lower Cost by Eliminating False Leads in the Drug Discovery Process "Later stages of drug discovery are, as a rule, more expensive and time-consuming than earlier ones," says the analyst. "The rewards of a drug discovery program with a tightly integrated in-silico simulation system are astounding, with its ability to prioritize, validate, and eliminate targets at a very early stage in drug discovery." Eliminating a target in this manner can save $200.0 to $300.0 million over what that compound would have cost if it had made it into the later stage of clinical trial. Qualified software developers trained in biology, chemistry, and specific methods of modeling and simulation needed to interpret data are essential to improve the drug discovery process. Companies also have to be prepared to deal with the technical inertia among biologists who consider the biological system too complex to be implemented using a series of differential equations. The series of consolidations, which took place in the pharmaceutical industry has forced computational biology vendors to tailor their offerings to meet the demands of these companies for a large technological platform which can satisfy a multitude of their research needs. |
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