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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
Time-varying modeling of gene expression regulatory networks using the wavelet dynamic vector autoregressive method
A Fujita1, J R Sato, H M Garay-Malpartida
1Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010-São Paulo, 05508-090, SP, Brazil.
Bioinformatics (Oxford, England)
|April 28, 2007
Summary
We introduce a statistical method to infer gene regulatory networks from time-series data. This approach identifies causal relationships and information flow dynamics without prior biological knowledge.
Area of Science:
- Systems Biology
- Bioinformatics
- Computational Biology
Background:
- Understanding complex cellular processes requires detailed molecular network descriptions.
- Gene regulatory networks (GRNs) are crucial for deciphering these mechanisms.
- Inferring causal relationships in GRNs from expression data is challenging.
Purpose of the Study:
- To develop a statistical method for estimating time-varying gene regulatory networks.
- To address the challenge of inferring causation from correlation in gene expression data.
- To identify the temporal dynamics of information flow within gene regulatory networks.
Main Methods:
- Utilized the Dynamic Vector Autoregressive (DyVAR) model.
- Applied the model to time-series microarray data.
- Estimated GRNs solely from gene expression profiles, without prior biological information.
Main Results:
- Successfully estimated time-varying gene regulatory networks.
- Predicted network connectivity and Granger-causality.
- Characterized the dynamics of information flow in p53, NF-kappaB, and c-myc networks for HeLa cells.
Conclusions:
- The Dynamic Vector Autoregressive model effectively infers causal gene regulatory networks from expression data.
- This method provides insights into network dynamics and information flow without requiring a priori biological knowledge.
- The approach is applicable to various biological networks and cell types.
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