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Gene regulatory network inference: Data integration in dynamic models—A review
Abstract
Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.
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“Overall, integration of multiple data types increases the accuracy of the resulting predictions [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [147]
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“However, a focus on TFs is rarely sufficient, and integration of multiple data types is often required to increase the accuracy and usefulness of the resulting networks [13, 147].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [147]
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans
Fire, A., Xu, S., Montgomery, M. K., Kostas, S. A., Driver, S. E., & Mello, C. C. (1998) · Nature · doi:10.1038/35888 · Cited by 15,429
System identification—Theory for the user
Sinha, N. K. (1989) · Automatica · doi:10.1016/0005-1098(89)90019-8 · Cited by 9,310
The Large-Scale Organization of Metabolic Networks
Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35036627 · Cited by 4,972
Metabolic stability and epigenesis in randomly constructed genetic nets
Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,871
Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248
Network motifs in the transcriptional regulation network of Escherichia coli
Shen-Orr, S. S., Milo, R., Mangan, S., & Alon, U. S. (2002) · Nature Genetics · doi:10.1038/ng881 · Cited by 2,970
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956
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- Westra, R, 2008. International Workshop on Gene Regulatory Network Inference, Jena, Personal Communication.
- Yong-A-Poi, J., 2008. Adaptive least Absolute Regression Network Analysis Improves Genetic Network Reconstruction by Employing Prior Knowledge (Master Thesis). Delft University of Technology, The Netherlands.
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Zheng, M., Huang, Y., Shen, W., ZHONG, Y., Wu, J., Liu, G., & Zhou, Y. L. (2012) · PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS · doi:10.3724/sp.j.1206.2011.00311 · Cited by 3
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Rout, S., Swarnkar, T., Mahapatra, S., & Senapati, D. (2012) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-35314-7_14 · Cited by 3
Koestler, S. A., Alaybeyoglu, B., Weichenberger, C. X., & Çelik, A. (2015) · F1000Research · doi:10.12688/f1000research.7556.1 · Cited by 3
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Shen, J., Petkova, M. D., Tu, Y., Liu, F., & Tang, C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/374439 · Cited by 2
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Slenter, D. (2024) · Research Publications (Maastricht University) · doi:10.26481/dis.20240624ds · Cited by 2
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Issuesand Strategies for Modeling Gene Regulatory Networks
Bansal, B., Nanda, A., & Sahoo, A. (2020) · International Conference on Research, Innovation, Knowledge Management and Technology Application for Business Sustainability (INBUSH) · doi:10.1109/inbush46973.2020.9392183 · Cited by 1
Leitner, S. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197668122.013.14 · Cited by 1
Chee, F. T., Harun, S., Daud, K. M., Sulaiman, S., & Muhammad, N. A. N. (2024) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2024.04.002 · Cited by 1
Overview of Cellular Computing-Basic Principles and Applications
Das, A., Dasgupta, R., & Bagchi, A. (2016) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-0058-2.ch026 · Cited by 1
Metadata
- Authors
- Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R.
- Affiliations
- Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie e. V. - Hans-Knöll-Institut (HKI) (DE)
- BioControl Jena (Germany) (DE)
- Corresponding author
- Guthke, Reinhard
- ORCID
- Hecker, Michael
- Venue
- Biosystems
- Publisher
- Elsevier BV
- Published
- 2009-04
- Volume
- 96
- Issue
- 1
- Pages
- 86-103
- ISSN
- 0303-2647 (ISSN-L)
- OpenAlex
- W1963522244
- Cited by
- 755
- References
- 132
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 37 · 2023: 22 · 2022: 30 · 2021: 33 · 2020: 32 · 2019: 48 · 2018: 63 · 2017: 50 · 2016: 62 · 2015: 71 · 2014: 56 · 2013: 62 · 2012: 61
- FWCI
- 2.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biosystems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (0313078D, 0313692D)
- 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 › Bioinformatics and Genomic Networks
- Keywords
- gene regulatory network inference, data integration, gene expression microarrays, genome sequence data, genome-wide data, learning algorithms
- MeSH terms
- Computer Simulation, Database Management Systems, Gene Expression Regulation, Models, Biological, Signal Transduction, Systems Integration, Proteome, Gene Expression Profiling, Databases, Protein, Systems Biology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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