Market Insights
The area of computational biology is concerned with the use of computer systems for addressing problems associated with molecular and evolutionary biology. Computational biology is also referred to as bioinformatics and is engaged in development of algorithms for simulations and building models for understanding biological aspects in life-science.
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Although the global computational biology market is currently at its introductory stage, the market shall witness significant opportunities and considerable rivalries among the players. The need for significant capital investments shall restrain the entry of new market players. On the positive side, favorable government initiatives across the developed markets and Asia Pacific promoting research in genomics and proteomics, neurology and drug discovery are driving the global computational biology market. Heavy investment by major IT companies is also a characteristic feature of this market. Intensive research has led to generation of significant amounts of databases, and need for the same is visibly emergent in computational biology. Conversely, requirement of specialized workforce for operational efficiency also challenges the global market growth. Additionally, lack of standardization of such large amount of data is inhibiting the market progress to a certain extent. Compatibility of required tools to the basic applications and need for one size fits all tools model is also a challenge faced in the market. Other market restraints include need for optimum solution for data storage, requirement for specialized workforce and constant updating of databases and software. However, with further evolution of market towards growth stage, such small challenges are expected to diminish. Issues pertaining to data storage can be potentially addressed with cloud computing, as cloud computing has the desired scalability and flexibility to store such copious amount of data generated every day.
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Disease modelling and drug discovery is anticipated to be the largest segment in the computational biology market. The segment will also progress at a rapid pace during the forecast period from 2018 to 2026. The urgent need in the market for reducing the lead-time in drug commercialization while simultaneously reducing the incurred cost has gathered interest of drug developers into computational biology. Geographically, North America is the largest market in terms of revenue. The dominance of the U.S. market is evident through copious amounts of funding from biotechnology and pharmaceutical giants in the adoption of computational biology, and domicile of major market players. On the other hand, Asia Pacific will progress at the fastest growth rate during the forecast period. Proliferated front of contract research organizations in the region mainly contributes to the rapid growth of Asia Pacific market.
The global computational biology market is characterized by intense completion among the existing market players. Market consolidation is an evident feature in this market and a limited number of players occupy the largest revenue share. Mergers and acquisitions are typically observed in this market. Some of the major players operating in the global computational biology market are Rhenovia Pharma SAS, Chemical Computing Group Inc., Compugen, Ltd., Accelrys, Insilico Biotechnology AG, Nimbus Discovery LLC, Entelos, Simulation Plus, Inc., Genedata AG, Leadscope, Inc., and Certara.
Key Market Movements:
- Increasing economic pressures in areas of drug development and commercialization
- Persistent burden of chronic diseases demanding novel treatments and diagnostic solutions in shortest turnaround time
- Technological advances bringing synergy among biotechnology, statistics and IT
- Exponential increase in interest of IT giants in the field of computational biology along with growing funding in research and development activities
ToC:
Chapter 1. Preface
1.1. Report Description
1.1.1. Study Purpose
1.1.2. Target Audience
1.1.3. USP and Key Offerings
1.2. Market Segmentation
1.3. Research Scope
1.4. Research Methodology
1.4.1. Phase I – Secondary Research
1.4.2. Phase II – Primary Research
1.4.3. Phase III – Expert Panel Review
1.4.4. Assumptions
1.5. Market Segmentation
Chapter 2. Executive Summary
2.1. Global Computational Biology Market Portraiture
2.2. Global Computational Biology Market, by Application, 2017 (US$ Mn)
2.3. Global Computational Biology Market, by Component, 2017 (US$ Mn)
2.4. Global Computational Biology Market, by Service Type, 2017 (US$ Mn)
2.5. Global Computational Biology Market, by End User, 2017 (US$ Mn)
2.6. Global Computational Biology Market, by Geography, 2017 (US$ Mn)
Chapter 3. Global Computational Biology Market: Dynamics and Future Outlook
3.1. Overview
3.2. Drivers
3.3. Challenges
3.4. Opportunities
3.5. Attractive Investment Proposition, by Geography, 2017
3.6. Competitive Analysis: Global Computational Biology Market, by Key Players, 2017
Chapter 4. Global Computational Biology Market, by Application, 2016-2026 (US$ Mn)
4.1. Overview
4.2. Cellular and Biological Simulation
4.2.1. Computational Genomics
4.2.2. Computational Proteomics
4.2.3. Pharmacogenomics
4.2.4. Transcriptomics/Metabolomics
4.3. Disease Modelling and Drug Discovery
4.3.1. Target Identification
4.3.2. Target Validation
4.3.3. Lead Discovery
4.3.4. Lead Optimization
4.4. Preclinical Drug Development
4.4.1. Pharmacokinetics
4.4.2. Pharmacodynamics
4.5. Clinical Trials
4.6. Human Body Simulation
……………..toc continued
“Computational Biology, which includes many aspects of bioinformatics, is the science of using biological data to develop algorithms or models to understand biological systems and relationships. Until recently, biologists did not have access to very large amounts of data. This data has now become commonplace, particularly in molecular biology and genomics. Researchers were able to develop analytical methods for interpreting biological information, but were unable to share them quickly among colleagues.
Bioinformatics began to develop in the early 1970s. It was considered the science of analyzing informatics processes of various biological systems. At this time, research in artificial intelligence was using network models of the human brain in order to generate new algorithms. This use of biological data to develop other fields pushed biological researchers to revisit the idea of using computers to evaluate and compare large data sets. By 1982, information was being shared among researchers through the use of punch cards. The amount of data being shared began to grow exponentially by the end of the 1980s. This required the development of new computational methods in order to quickly analyze and interpret relevant information.
Since the late 1990s, computational biology has become an important part of developing emerging technologies for the field of biology.The terms computational biology and evolutionary computation have a similar name, but are not to be confused. Unlike computational biology, evolutionary computation is not concerned with modeling and analyzing biological data. It instead creates algorithms based on the ideas of evolution across species. Sometimes referred to as genetic algorithms, the research of this field can be applied to computational biology. While evolutionary computation is not inherently a part of computational biology, Computational evolutionary biology is a subfield of it.
Computational biology has been used to help sequence the human genome, create accurate models of the human brain, and assist in modeling biological systems.–wikipedia “
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