Automatic selection of verification tools for efficient analysis of biochemical models
Mehmet Emin Bakir1, Savas Konur2, Marian Gheorghe2
1Department of Computer Science, University of Sheffield, Sheffield, UK.
Bioinformatics (Oxford, England)
|April 25, 2018
Summary
This study introduces an automated tool to select the best statistical model checking (SMC) tool for biological models, achieving over 90% accuracy. This simplifies complex verification processes for biologists.
Area of Science:
- Computational Biology
- Systems Biology
- Bioinformatics
Background:
- Formal verification, including model checking, is crucial for ensuring system correctness in engineering and biological applications.
- Model checking is computationally intensive and struggles with scalability for large biological models.
- Statistical model checking (SMC) offers a more scalable alternative, but tool performance varies significantly, posing a usability challenge for biologists.
Purpose of the Study:
- To address the challenge of selecting the most efficient SMC tool for specific biological models.
- To introduce a novel method and computational tool for the automatic selection of appropriate model checkers.
- To reduce the computational expertise barrier for biologists using advanced verification techniques.
Main Methods:
- Development of a predictive system to identify the fastest model checking tool for a given biological model.
- Evaluation of tool performance across diverse biological models and requirement specifications.
- Implementation of an automated selection mechanism based on predictive accuracy.
Main Results:
- The developed system demonstrates high-confidence predictions with over 90% accuracy in identifying the optimal SMC tool.
- Significant performance gains in verification time are achieved through accurate tool selection.
- The tool substantially lowers the barrier to entry for biologists, enhancing accessibility to computational verification technologies.
Conclusions:
- Automated selection of SMC tools is feasible and highly accurate for biological applications.
- The developed tool enhances the efficiency and usability of formal verification in systems and synthetic biology.
- This advancement empowers biologists with powerful computational tools, accelerating research and discovery.
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