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The Use of Chemostats in Microbial Systems Biology
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Effective sampling trajectory optimisation for sensitivity analysis of biological systems.

Zhao Z Xu1, Ji Liu2

  • 1College of Information Science and Engineering, Key Laboratory of Advanced Control and Optimisation for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People's Republic of China.

IET Systems Biology
|June 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a trajectory optimization algorithm to improve the Morris method for sensitivity analysis in biological systems. The enhanced method provides more stable and consistent results for identifying key biological parameters.

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Area of Science:

  • Systems biology
  • Computational biology
  • Biophysics

Background:

  • Sensitivity analysis is crucial for understanding complex biological systems like metabolic networks and signaling pathways.
  • The Morris method is a screening approach for identifying key parameters but suffers from unstable measures due to insufficient space exploration.
  • Existing methods struggle to adequately cover the entire parameter space, leading to unreliable sensitivity results.

Purpose of the Study:

  • To develop a novel trajectory optimization algorithm for Morris-based sensitivity analysis.
  • To enhance the robustness and consistency of sensitivity measures in biological models.
  • To improve the identification of critical parameters in complex biological systems.

Main Methods:

  • A new trajectory optimization algorithm is proposed for the Morris method.
  • The algorithm ensures a comprehensive scan of the entire uncertain parameter space.
  • The method was tested on a benchmark example and a cellular signaling network model.

Main Results:

  • The optimized trajectory approach yields more consistent sensitivity analysis results compared to the standard Morris method.
  • The enhanced method effectively addresses the issue of unstable sensitivity measures caused by limited space sampling.
  • Application to a cellular signaling model confirmed the method's efficacy and agreement with literature data.

Conclusions:

  • The novel trajectory optimization algorithm significantly improves the reliability of Morris-based sensitivity analysis.
  • This method offers a more robust tool for dissecting complex biological systems and identifying key drivers.
  • The findings have broad implications for modeling and understanding various biological networks.