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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Complexity reduction preserving dynamical behavior of biochemical networks.
Mochamad Apri1, Maarten de Gee, Jaap Molenaar
1Biometris, Wageningen University and Research Center, 6708 PB, Wageningen, The Netherlands. mochamad.apri@wur.nl
Journal of Theoretical Biology
|April 3, 2012
Summary
This study introduces a new method to simplify complex biochemical models by identifying essential parameters. The approach successfully reduced an epidermal growth factor receptor (EGFR) model, retaining only 34% of components for accurate response prediction.
Area of Science:
- Biochemistry
- Systems Biology
- Mathematical Modeling
Background:
- Biochemical systems are complex due to numerous components and interactions, hindering understanding.
- Effective methods are needed to simplify these models and identify key elements.
Purpose of the Study:
- To present a novel and efficient reduction method for mathematical models of biochemical systems.
- To identify essential parameters and components for accurate system behavior prediction.
Main Methods:
- Exploration of the admissible region, defined as the set of parameters yielding required model output.
- Analysis of the admissible region's shape to distinguish essential from redundant parameters.
- Application of the method to an artificial network and an epidermal growth factor receptor (EGFR) network model.
Main Results:
- The method successfully identified essential parameters by analyzing the admissible region.
- Application to the EGFR network model revealed that only 34% of components were required for the correct response to epidermal growth factor (EGF).
- Parameter sensitivity analysis alone was found to be unreliable for model reduction.
Conclusions:
- The proposed admissible region method offers an efficient way to simplify complex biochemical models.
- A significant portion of components in the EGFR network model were found to be redundant for predicting the response to EGF.
- Model reduction strategies should not solely rely on parameter sensitivity.
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