Importance of input perturbations and stochastic gene expression in the reverse engineering of genetic regulatory networks: Insights from an identifiability analysis of an in silico network.
Open accessAbstract
Gene expression profiles are an increasingly common data source that can yield insights into the functions of cells at a system-wide level. The present work considers the limitations in information content of gene expression data for reverse engineering regulatory networks. An in silico genetic regulatory network was constructed for this purpose. Using the in silico network, a formal identifiability analysis was performed that considered the accuracy with which the parameters in the network could be estimated using gene expression data and prior structural knowledge (which transcription factors regulate which genes) as a function of the input perturbation and stochastic gene expression. The analysis yielded experimentally relevant results. It was observed that, in addition to prior structural knowledge, prior knowledge of kinetic parameters, particularly mRNA degradation rate constants, was necessary for the network to be identifiable. Also, with the exception of cases where the noise due to stochastic gene expression was high, complex perturbations were more favorable for identifying the network than simple ones. Although the results may be specific to the network considered, the present study provides a framework for posing similar questions in other systems.
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By examining 17 models from the systems biology literature, the paper showed that their parameter sensitivities all share striking common features deemed "sloppiness".
“By examining 17 models from the systems biology literature [2,21,25,28–41], we showed that their parameter sensitivities all share striking common features deemed “sloppiness””
From Universally Sloppy Parameter Sensitivities in Systems Biology Models (2007) · cites this paper as [25]
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Self-citation
- Authors
- Zak, D. E., Gonye, G. E., Schwaber, J. S., & Doyle, F. J.
- Affiliations
- University of Delaware (US)
- Thomas Jefferson University (US)
- University of California, Santa Barbara (US)
- Corresponding author
- Doyle, Francis J.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2003-11
- Volume
- 13
- Issue
- 11
- Pages
- 2396-2405
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC403758
- OpenAlex
- W2128506325
- Cited by
- 177
- References
- 1
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 5 · 2019: 4 · 2018: 4 · 2017: 2 · 2016: 3 · 2015: 3 · 2014: 11 · 2013: 6 · 2012: 9
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Delaware
- National Institutes of Health (5r01hl054194-05, DE-AC03- 76SF00098)
- Defense Advanced Research Projects Agency (DE-AC03-76SF00098)
- U.S. Public Health Service (P20 H64459)
- National Institute on Alcohol Abuse and Alcoholism (5T32AA07463-15, T32AA007463, R01 AA-13204-01, R01AA013204)
- National Heart, Lung, and Blood Institute (R01 HL54194-05)
- National Institute of General Medical Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- identifiability analysis, input perturbation, stochastic gene expression, gene expression data, parameter estimation, mRNA degradation rate, transcription factor regulation, kinetic parameters
- MeSH terms
- Computer Simulation, Gene Expression Regulation, Genetic Engineering, Models, Genetic, Stochastic Processes, Computational Biology
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