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Parameter estimation in ordinary differential equations for biochemical processes using the method of multiple
1Freiburg Centre for Data Analysis and Modelling, Eckerstr. 1, Freiburg 79104, Germany. peifer@fdm.uni-freiburg.de
Estimating parameters in biochemical models is crucial for accurate systems biology simulations. This study details the multiple shooting method for efficient and stable parameter estimation in ordinary differential equations.
Area of Science:
- Systems Biology
- Computational Biology
- Biochemical Modeling
Background:
- In silico investigations using dynamical models are vital in systems biology.
- Unknown model parameters can lead to misleading simulation results.
- Existing parameter estimation methods for ordinary differential equations (ODEs) have limitations in convergence or computational cost.
Purpose of the Study:
- To provide a comprehensive guide for implementing the multiple shooting method for parameter estimation in ODE models.
- To offer theoretical background and practical implementation details for the multiple shooting technique.
- To demonstrate the performance of the multiple shooting method through illustrative examples.
Main Methods:
- The study focuses on the multiple shooting method, a technique balancing convergence and computational efficiency for parameter estimation.
- Detailed numerical aspects and implementation strategies for the multiple shooting method are discussed.
- The performance is evaluated using two distinct case studies.
Main Results:
- The multiple shooting method offers a practical solution to parameter estimation challenges in ODE models.
- The provided information facilitates successful implementation and application of the method.
- The illustrative examples showcase the method's effectiveness and stability.
Conclusions:
- The multiple shooting method is a valuable tool for accurate parameter estimation in systems biology.
- This work addresses the gap in literature regarding the practical implementation and theoretical underpinnings of the method.
- The presented approach enhances the reliability of in silico investigations in systems biology.
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