SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
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SBML2Julia：将 SBML 与高效的非线性 Julia 建模和求解工具连接以实现参数优化
DOI：
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发表时间：
2020
期刊：
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通讯作者：
V. Zavala
中科院分区：
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作者：
Paul F. Lang;Sungho Shin;V. Zavala
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive. While the Julia programming language was recently developed as a high-level and high-performance language for scientific computing, systems biologists have only started to realise its potential. For instance, we have recently used Julia to cut down the optimization time of a microbial community model by a factor of 140. To facilitate access of the systems biology community to the efficient nonlinear solvers used for this optimisation, we developed SBML2Julia. SBML2Julia translates optimisation problems specified in SBML and TSV files (PEtab format) into Julia for Mathematical Programming (JuMP), executes the optimization and returns the results in tabular format.
Availability and implementation: SBML2Julia is freely available under the MIT license. It comes with a command line interface and Python API. Internally, SBML2Julia calls the Julia LTS release v1.0.5 for optimisation. All necessary dependencies can be pulled from Docker Hub (this https URL). Source code and documentation are available at this https URL.
