Answer set programming; OMIC data integration; Regulatory and metabolic models integration; Regulatory network; Gene Expression; Signal Transduction; Discrete tools; Metabolic modelling; Metabolic network; Models integration; Quantitative tool; Regulatory and metabolic model integration; Regulatory model; Structural Biology; Biochemistry; Molecular Biology; Computer Science Applications; Applied Mathematics
Abstract :
[en] [en] BACKGROUND: The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely.
RESULTS: To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool.
CONCLUSIONS: MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
LE BARS, Sophie ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science
Bolteau, Mathieu; École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000, France
Bourdon, Jérémie; École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000, France
Guziolowski, Carito; École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000, France
External co-authors :
yes
Language :
English
Title :
Predicting weighted unobserved nodes in a regulatory network using answer set programming.
This article has been published as part of BMC Bioinformatics Volume 24 Supplement 1, 2023: Special Issue of the 19th International Conference on Computational Methods in Systems Biology. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-24-supplement-1.
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27(1):29–34. DOI: 10.1093/nar/27.1.29
Norsigian CJ, Pusarla N, McConn JL, Yurkovich JT, Dräger A, Palsson BO, et al. BiGG models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 2020;48(D1):D402–6.
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10. DOI: 10.1093/nar/30.1.207
Yu H, Blair RH. Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer’s disease. BMC Bioinform. 2019;20(1):386. DOI: 10.1186/s12859-019-2872-8
Yaghoobi H, Haghipour S, Hamzeiy H, Asadi-Khiavi M. A review of modeling techniques for genetic regulatory networks. J Med Signals Sens. 2012;2(1):61–70. DOI: 10.4103/2228-7477.108179
Angione C. Human systems biology and metabolic modelling: a review-from disease metabolism to precision medicine. BioMed Res Int. 2019;2019: e8304260. DOI: 10.1155/2019/8304260
Thiele S, Cerone L, Saez-Rodriguez J, Siegel A, Guziołowski C, Klamt S. Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies. BMC Bioinform. 2015;16(1):345. DOI: 10.1186/s12859-015-0733-7
Thiele S, Heise S, Hessenkemper W, Bongartz H, Fensky M, Schaper F, et al. Designing optimal experiments to discriminate interaction graph models. IEEE/ACM Trans Comput Biol Bioinform. 2019;16(3):925–35. DOI: 10.1109/TCBB.2018.2812184
Gouveia F, Lynce I, Monteiro PT. Revision of Boolean models of regulatory networks using stable state observations. J Comput Biol. 2020;27(2):144–55. DOI: 10.1089/cmb.2019.0289
Le Bars S, Bourdon J, Guziolowski C. Comparing probabilistic and logic programming approaches to predict the effects of enzymes in a neurodegenerative disease model. In: Wolf V, Petrov T, Abate A, editors. Computational methods in systems biology. Lecture notes in computer science. Cham: Springer; 2020. p. 141–56.
Lifschitz V. What is answer set programming? AAAI. 2008;p. 4.
Cowell RG. Local propagation in conditional Gaussian Bayesian networks. J Mach Learn Res. 2005;6(52):1517–50.
Zhang Z, Yan J, Chang Y, Yan SS, Shi H. Hypoxia inducible factor-1 as a target for neurodegenerative diseases. Curr Med Chem. 2011;18(28):4335–43. DOI: 10.2174/092986711797200426
Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Walker DG, et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genom. 2007;28(3):311–22. DOI: 10.1152/physiolgenomics.00208.2006
Downes NL, Laham-Karam N, Kaikkonen MU, Ylä-Herttuala S. Differential but complementary HIF1α and HIF2α transcriptional regulation. Mol Therapy J Am Soc Gene Therapy. 2018;26(7):1735–45. DOI: 10.1016/j.ymthe.2018.05.004
Yang W, Rosenstiel P, Schulenburg H. aFold-using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data. BMC Genom. 2019;20(1):364. DOI: 10.1186/s12864-019-5686-1
Gebser M, Kaufmann B, Neumann A, Schaub T. Clasp: a conflict-driven answer set solver. In: Baral C, Brewka G, Schlipf J, editors. Logic programming and nonmonotonic reasoning. Lecture notes in computer science. Berlin: Springer; 2007. p. 260–5. DOI: 10.1007/978-3-540-72200-7_23
Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics. 2002;18:546–54. DOI: 10.1093/bioinformatics/18.4.546
