SemanticBits

High throughput biological experimentation provides massive amounts of data with little direct biological findings directly accessible. Researchers must work to find the missing piece of the puzzle - i.e. the biological model or system, which can in turn lead to additional generation of this type of data. Therefore, researchers need a tool that can help them model their hypothesis and simulate the generation of experimental data. This way they can come up with biological models to support their theory and further reuse the same models in similar experimental situations.

The Quantitative Biology Tool (QBT) is a software product used for both qualitative and quantitative modeling of biological systems. It is designed to support the iterative nature of the scientific method, which alternates between experimentation and model refinement. Researchers may utilize either hypothesis-driven or data-driven approaches with QBT. For a hypothesis-driven approach, QBT facilitates the transition from 'conceptual' to 'computable' models by providing users an environment to diagram the relevant biological sub-systems in their area of research. The user has the option of expanding their model by searching curated literature sources and biological pathway knowledge bases. Once a qualitative model is sufficiently detailed, quantitative analysis may be undertaken.

For a data-driven approach, users may import data directly or use an R-interface to evaluate their high-throughput microarray, proteomic, or metabolomic data. Simulation of model behavior can be performed using kinetic modeling (ODE-based) and Flux Balance Analysis (FBA).  Users can quantitatively assess the ability of their model to explain their data and the areas where the model can be improved.

Key features include:

• SVG Model editor
• Data simulation capabilities
• Using experimental data to implicate involved pathways
• Simulate and refine models to improve agreement with experimental data
• Using experimental data to estimate biological effects through a refined model
• Using findings to refine models and suggest additional experimenation
• Access to KEGG, Reactome and user-defined pathway databases
• Access to R-based methods, BLAS, SOSLib SBML solver, and SunDials matrix solvers
• Assisted model versioning and annotation
• Literature search and citation management

QBT heavily leverages industry standard open source technologies including: Spring Framework, JBoss Hibernate, NetBeans Rich Client Platform, C,C++, Java/J2EE, R Programming, JFreeChart, SunDials Matrix libraries, Jena ontology Engine, SVG/Batik Garaphics, SosLib Model solver,Apache Ant, Ivy, and Hudson.

QBT is developed by SemanticBits in collaboration with National Heart, Lung, and Blood Institute (NHLBI) and is being adopted there.

QBT is currently under active development and use at NHLBI.

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