Computational Biology Market in the World to Reach $11.4B by 2025

Computational Biology Market in the world is poised to grow at a compound annual growth rate (CAGR) of about 21.7 percent over the next eight years to reach about $11.43B by 2015.
Carried out by Research and Markets, the report, referred to as “Global Computational Biology Market Analysis & Trends – Industry Forecast to 2025”, analyses the market forecasts and estimates of all the given sectors on global and regional levels presented in the scope of the research. The study focuses on leading players, market trends, supply chain trends, key developments, future strategies, and technological innovations. With detailed market assessment across geographies such as Europe, North America, Latin America, Asia Pacific, Middle East and the Rest of the world, the study is an important asset for the future investors, new entrants, and the existing players.
Growing demand for protein sequencing and nucleic acid, growing application of computational biology, increasing initiatives from private organizations and government, and improved collaborative ties between research institutes and companies are the main factors driving the growth of the computational biology market. The major factors limiting the growth of this market, on the other hand, are lack of adequate user-friendly tools and the shortage of skilled professional.


Using Big Data to Predict Toxicity of Chemicals Can Save Animals

There is a large amount of available data that can be used to predict chemical toxicity without carrying out animal tests, thanks to international data-sharing projects. The enormous volume of these databases makes it difficult to use conventional data-analysis tools when processing them. Recent advances in big-data analytics, however, provide new methods for predictions of chemical toxicity.
Recently, experts gathered at the Indian Institute of Technology Delhi for a national event, “Breaking Barriers through Bioinformatics and Computational Biology.” They shared information on the latest development in computational biology.
PETA India described how companies could use big data to reduce animal testing. It also asked private and government organizations to use big-data analytics methods. A PETA India poster discussed the disadvantages of traditional animal-based methods to determining the toxicity of chemicals, the progress on data-sharing, and how data in public repositories can be used to make models that can predict the toxicity level of chemical compounds. The steps that regulatory and government authorities can take to adopt big data and reduce animal testing were also discussed.
According to PETA India’s Dr. Rohit Bhatia, using big advanced data analytics approach to predict toxicity save money, time, and lives of many animals.

BioUML: An Open Source Software Platform for System Biology


BioUML is an open source and extensible software framework for data analysis from advanced computational biology developed by researchers from the Institute of Systems Biology. The platform is available online and is used in research labs for the discovery of disease origins and prevention. The platform aims at covering all areas of computational applications in systems biology and bioinformatics.
Currently, BioUML include three versions: BioUML Server, BioUML Workbench, and BioUML Web Edition. BioUML Server provides access to data and analysis techniques. BioUML Workbench is a Java application that works standalone for the platform server edition. Lastly, BioUML Web Edition is a web browser that offers most of the BioUML workbench functionality.
Since 2003, the platform has been developed constantly and provides data analysis and visualizations for researchers involved in molecular biology research. It allows scientists to comprehensively describe biological systems function and structure including tools needed to make findings related to metabolomics, transcriptomics, proteomics, and genomics.
High-throughput sequencing and other next-generation sequencing methods create big data. BioUML platform disseminates, study, and produce simulations and visualizations, facilitates parameter fitting and supports numerous other analysis techniques needed to deal with large amounts of data.

Scientists Identify 760 Genes that are Key to Cancer Growth


In one of the biggest efforts to create a detailed catalog of genetic susceptibilities in cancer, scientists from Harvard and Dana-Farber Cancer Institute and the Broad Institute of MIT have identified over 760 genes upon which many types of cancer are dependent for their survival and growth.
According to the researchers, many of these “dependencies” are specific to particular cancer types. About 10% of them, however, are common across numerous cancers. This suggests that relatively few therapies targeting these main dependencies may each hold promise for fighting several tumors.
To get these findings, the scientists carried out genome-wide RNA interference screens on 501 cell lines that represented over 20 types of cancer, silencing over 170000 genes separately in each line to find genetic dependencies that are unique to cancerous cells.
Cancer cells can have various genetic errors, ranging from small mutations to extensive exchanges of DNA between chromosomes. When an error affects a critical gene, a tumorous cell compensates by changing the activity of other genes, frequently creating a dependence on such changes to persist.
Finding these dependencies does not only offer an opportunity for the researcher to gain a better insight into cancer biology but also enable them to determine new therapeutic targets.
First draft of genome-wide cancer ‘dependency map’ released