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Updated: May 22, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

A specialized ODE integrator for the efficient computation of parameter sensitivities.

Pedro Gonnet¹, Sotiris Dimopoulos, Lukas Widmer

¹Mathematical Institute, University of Oxford, Oxford, UK.

BMC Systems Biology

|May 22, 2012

Summary

A new numerical integration algorithm for systems biology models significantly speeds up computations, especially for parameter sensitivity analysis. This advance facilitates more efficient parameter estimation and optimization in complex biological systems.

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Area of Science:

Systems Biology
Computational Biology
Mathematical Modeling

Background:

Dynamic mathematical models, often systems of ordinary differential equations (ODEs), are crucial in systems biology.
Efficient and accurate numerical integration of ODEs is vital for complex models, particularly for parameter sensitivity analysis in systems identification.
Existing ODE solvers often lack automatic computation of parameter sensitivities.

Purpose of the Study:

To develop a novel numerical integration algorithm for systems biology models.
To improve the speed and accuracy of solving ODEs and computing parameter sensitivities.
To enhance the efficiency of parameter estimation and optimization in computational biology.

Main Methods:

A novel integration algorithm based on second derivatives and improved error estimates.
Implementation within a framework that automatically generates integrator input from SBML descriptions.
Comparison with established integrators for solving system equations and evaluating parameter sensitivities.

Main Results:

The new algorithm allows for larger time steps compared to other methods.
It competes effectively with established integrators in solving system equations.
The method significantly outperforms existing integrators when evaluating local parameter sensitivities.

Conclusions:

"Cheap" parameter sensitivities will enable advances in computationally expensive parameter estimation and optimization problems.
Exploiting domain-specific characteristics can lead to substantial improvements in computational performance.
Elements of the developed error estimation method may have broader applicability in general numerical algorithms.