ArticlePloS one2014
Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using high-dimensional ordinary differential equations.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Constructing cell-type-specific gene regulatory networks from cell-cell communication and a global gene regulatory network.Briefings in bioinformatics · 2026Article
- Advancing curation of viral life cycles, host interactions, and therapeutics in Reactome.Journal of virology · 2025Review
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- MDIC3: Matrix decomposition to infer cell-cell communication.Patterns (New York, N.Y.) · 2024Article
- LogBTF: gene regulatory network inference using Boolean threshold network model from single-cell gene expression data.Bioinformatics (Oxford, England) · 2023Article
- An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries.Database : the journal of biological databases and curation · 2020Article
- Modelling of hypoxia gene expression for three different cancer cell lines.International journal of computational biology and drug design · 2020Article
- FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis.Bioinformatics (Oxford, England) · 2017Article
- Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study.Frontiers in genetics · 2017Article
- Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism.International journal of genomics · 2017Article
- Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human.Infectious Disease Modelling · 2016Article
- Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.Current genomics · 2015Article
- Topological sensitivity analysis for systems biology.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The immune response to viral infection is regulated by an intricate network of many genes and their products. The reverse engineering of gene regulatory networks (GRNs) using mathematical models from time course gene expression data collected after influenza infection is key to our understanding of the mechanisms involved in controlling influenza infection within a host. A five-step pipeline: detection of temporally differentially expressed genes, clustering genes into co-expressed modules, identification of network structure, parameter estimate refinement, and functional enrichment analysis, is developed for reconstructing high-dimensional dynamic GRNs from genome-wide time course gene expression data. Applying the pipeline to the time course gene expression data from influenza-infected mouse lungs, we have identified 20 distinct temporal expression patterns in the differentially expressed genes and constructed a module-based dynamic network using a linear ODE model. Both intra-module and inter-module annotations and regulatory relationships of our inferred network show some interesting findings and are highly consistent with existing knowledge about the immune response in mice after influenza infection. The proposed method is a computationally efficient, data-driven pipeline bridging experimental data, mathematical modeling, and statistical analysis. The application to the influenza infection data elucidates the potentials of our pipeline in providing valuable insights into systematic modeling of complicated biological processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.