• Corpus ID: 226254216

SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization

@article{Lang2020SBML2JuliaIS,
  title={SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization},
  author={Paul F. Lang and Sungho Shin and Victor M. Zavala},
  journal={arXiv: Quantitative Methods},
  year={2020}
}
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… 

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References

SHOWING 1-10 OF 15 REFERENCES
Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems
TLDR
This work presents a modeling environment for MATLAB that pioneers these challenges to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting.
Benchmarking optimization methods for parameter estimation in large kinetic models
TLDR
The results show that, thanks to recent advances in the calculation of parametric sensitivities, a multi-start of gradient-based local methods is often a successful strategy, but a better performance can be obtained with a hybrid metaheuristic.
AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology
TLDR
This work presents the AMIGO2 toolbox: the first multiplatform software tool that automatizes the solution of all those problems, offering a suite of state-of-the-art (multi-objective) global optimizers and advanced simulation approaches.
Scalable nonlinear programming framework for parameter estimation in dynamic biological system models
TLDR
A scalable computational framework to rapidly infer parameters in complex dynamic models of biological systems from large-scale experimental data is presented and can be used to analyze parameter uncertainty, to diagnose whether the experimental data are sufficient to uniquely determine the parameters, to determine the model that best describes the data, and to inference parameters in the face of data outliers.
PEtab—Interoperable specification of parameter estimation problems in systems biology
TLDR
PEtab is introduced, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated.
Benchmark problems for dynamic modeling of intracellular processes
TLDR
This work presents a collection of 20 ODE models developed given experimental data as benchmark problems in order to evaluate new and existing methodologies, e.g. for parameter estimation or uncertainty analysis.
Julia: A Fresh Approach to Numerical Computing
TLDR
The Julia programming language and its design is introduced---a dance between specialization and abstraction, which recognizes what remains the same after computation, and which is best left untouched as they have been built by the experts.
JuMP: A Modeling Language for Mathematical Optimization
JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a
Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood
TLDR
This work suggests an approach that exploits the profile likelihood that enables to detect structural non-identifiabilities, which manifest in functionally related model parameters, that might arise due to limited amount and quality of experimental data.
On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
TLDR
A comprehensive description of the primal-dual interior-point algorithm with a filter line-search method for nonlinear programming is provided, including the feasibility restoration phase for the filter method, second-order corrections, and inertia correction of the KKT matrix.
...
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