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Updated: May 29, 2026
10:44
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
Probabilistic polynomial dynamical systems for reverse engineering of gene regulatory networks
Elena S Dimitrova1, Indranil Mitra, Abdul Salam Jarrah
1Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, USA. edimit@clemson.edu.
EURASIP Journal on Bioinformatics & Systems Biology
|September 14, 2011
Summary
This study introduces a novel algorithm for reverse engineering gene regulatory networks using stochastic models. The method effectively captures network dynamics and outperforms existing algorithms in model generation.
Area of Science:
- Systems Biology
- Computational Biology
- Bioinformatics
Background:
- Gene regulatory networks (GRNs) are crucial for cellular function.
- Understanding GRN structure and dynamics is a key challenge in systems biology.
- Experimental data often contains noise and uncertainty, complicating GRN analysis.
Purpose of the Study:
- To develop a robust algorithm for reverse engineering GRNs.
- To create discrete, probabilistic polynomial dynamical systems from experimental data.
- To account for noise and uncertainty in GRN modeling.
Main Methods:
- Developed an algorithm within the framework of probabilistic polynomial dynamical systems.
- Reverse engineered GRNs into discrete, probabilistic polynomial dynamical systems.
- Assembled stochastic models from minimal models, assigning probabilities based on data likelihood.
Main Results:
- Successfully identified stochastic models for two published synthetic network models.
- The generated models retained key features of the original networks.
- The proposed method demonstrated favorable comparisons with other existing algorithms.
Conclusions:
- The developed algorithm provides a powerful tool for GRN reverse engineering.
- Stochastic modeling offers advantages in handling noise and uncertainty in biological data.
- This approach enhances the accuracy and reliability of GRN models.
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