A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology
Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e., estimate parameters from experimental data. Exis...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-07-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fams.2020.00025/full |
| _version_ | 1828886592612204544 |
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| author | Fortunato Bianconi Lorenzo Tomassoni Chiara Antonini Paolo Valigi |
| author_facet | Fortunato Bianconi Lorenzo Tomassoni Chiara Antonini Paolo Valigi |
| author_sort | Fortunato Bianconi |
| collection | DOAJ |
| description | Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e., estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC), and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC. |
| first_indexed | 2024-12-13T11:47:17Z |
| format | Article |
| id | doaj.art-682183a27e934fed95d84ebb0c6c051b |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-12-13T11:47:17Z |
| publishDate | 2020-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-682183a27e934fed95d84ebb0c6c051b2022-12-21T23:47:28ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872020-07-01610.3389/fams.2020.00025520711A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems BiologyFortunato Bianconi0Lorenzo Tomassoni1Chiara Antonini2Paolo Valigi3Independent Researcher, Perugia, ItalyDepartment of Engineering, University of Perugia, Perugia, ItalyDepartment of Engineering, University of Perugia, Perugia, ItalyDepartment of Engineering, University of Perugia, Perugia, ItalyComputational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e., estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC), and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.https://www.frontiersin.org/article/10.3389/fams.2020.00025/fullparameter estimationODE modelsBayesian algorithmsrobustness analysismodel calibrationcomputational systems biology |
| spellingShingle | Fortunato Bianconi Lorenzo Tomassoni Chiara Antonini Paolo Valigi A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology Frontiers in Applied Mathematics and Statistics parameter estimation ODE models Bayesian algorithms robustness analysis model calibration computational systems biology |
| title | A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology |
| title_full | A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology |
| title_fullStr | A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology |
| title_full_unstemmed | A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology |
| title_short | A New Bayesian Methodology for Nonlinear Model Calibration in Computational Systems Biology |
| title_sort | new bayesian methodology for nonlinear model calibration in computational systems biology |
| topic | parameter estimation ODE models Bayesian algorithms robustness analysis model calibration computational systems biology |
| url | https://www.frontiersin.org/article/10.3389/fams.2020.00025/full |
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