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Sensitivity analysis of dynamic biological systems with time-delays.

BMC bioinformatics·2010
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Related Experiment Video

Updated: Jun 27, 2026

A Simplified System for Evaluating Cell Mechanosensing and Durotaxis In Vitro
09:50

A Simplified System for Evaluating Cell Mechanosensing and Durotaxis In Vitro

Published on: August 27, 2015

Dynamic sensitivity analysis of biological systems.

Wu Hsiung Wu1, Feng Sheng Wang, Maw Shang Chang

  • 1Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan. wwh@cs.ccu.edu.tw

BMC Bioinformatics
|December 19, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient algorithm for dynamic sensitivity analysis of stiff ordinary differential equation (ODE) systems, extending capabilities to models with time-dependent inputs for improved biological system modeling.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Chemical Engineering

Background:

  • Systems biology relies on mathematical models, typically nonlinear ordinary differential equations (ODEs), to understand and predict biological system behavior.
  • Efficient and accurate simulation of dynamic behavior and parameter sensitivities in ODE systems is crucial for practical applications.
  • Time-dependent inputs in models, common in systems like fed-batch fermentation, pose challenges for classical dynamic sensitivity analysis, particularly for dynamic log gains.

Purpose of the Study:

  • To develop and present an algorithm for simultaneously computing solutions and dynamic parameter sensitivities of autonomous ODE systems.
  • To address the limitations of classical methods by enabling dynamic sensitivity analysis for models with time-dependent admissible inputs.
  • To evaluate the algorithm's accuracy and applicability on stiff ODE systems and realistic biological models.

Main Methods:

  • An algorithm with adaptive step-size control was developed for simultaneous computation of ODE solutions and dynamic sensitivities.
  • This algorithm employs a decoupled direct method approach for calculating dynamic sensitivities.
  • The method was implemented and tested on chemical reaction systems (ethane pyrolysis, formaldehyde oxidation) and an ethanol fed-batch fermentation system.

Main Results:

  • The algorithm accurately computes dynamic parameter sensitivities for stiff ODE systems, even when sensitivity equations are more numerically challenging than model equations.
  • Demonstrated moderate accuracy for dynamic sensitivities and time profiles using step sizes determined by model equations.
  • Successfully applied to an ethanol fed-batch fermentation system with time-varying feed rates, showcasing its utility for realistic models.

Conclusions:

  • The developed algorithm offers an accurate and efficient method for computing dynamic parameter sensitivities in stiff ODE problems.
  • It extends the applicability of dynamic sensitivity analysis to models incorporating time-dependent admissible inputs, a significant advancement.
  • The method provides a valuable tool for systems biology and related fields requiring robust dynamic modeling and sensitivity analysis.