Abstract
Properly modeling the dynamics of cell signaling pathways requires several steps, such as selecting a subset of chemical reactions, mapping them into a mathematical model that deals with the communication of the pathway with the remainder of the cell (e.g., systems of universal differential equations - UDEs), inferring model parameters, and selecting the best model based on experimental data. However, this entire process can be extremely laborious and time-consuming for many researchers, as they often have to access different and complicated tools to achieve this goal. To address the challenges associated with this process in a more efficient way, we propose a framework that provides a streamlined approach tailored for universal differential equation UDE-based cell signaling pathway modeling. The open-source, free framework (github.com/Dynamic-Systems-Biology/BSB-2023-Framework) combines parameter inference algorithms, model selection techniques, and data importation from public repositories of biochemical reactions into a single tool. We provide an example of the usage of the proposed framework in a Julia Jupyter notebook. We expect that this streamlined approach will enable researchers to design improved cell signaling pathway models more easily, which may lead to new insights and discoveries in the study of biological mechanisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chelliah, V., et al.: BioModels: ten-year anniversary. Nucleic Acids Res. 43(D1), D542–D548 (2015). https://doi.org/10.1093/nar/gku1181
Funahashi, A., Morohashi, M., Kitano, H., Tanimura, N.: CellDesigner: a process diagram editor for gene-regulatory and biochemical networks. Biosilico 1(5), 159–162 (2003)
Gutenkunst, R.N., Waterfall, J.J., Casey, F.P., Brown, K.S., Myers, C.R., Sethna, J.P.: Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol. 3(10), e189 (2007). https://doi.org/10.1371/journal.pcbi.0030189
Hairer, E., Wanner, G.: Solving Ordinary Differential Equations. II: Stiff and Differential-Algebraic Problems, vol. 14. Springer, Heidelberg (1993). https://doi.org/10.1007/978-3-642-05221-7
Hong, C.W., Rackauckas, C.V.: Bayesian inference of dynamical systems using Turing.jl. J. Open Source Softw. 5(47), 2067 (2020)
Hoops, S., et al.: COPASI-a complex pathway simulator. Bioinformatics 22(24), 3067–3074 (2006)
Hucka, M., et al.: The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core. J. Integr. Bioinform. 10(2), 186 (2013). https://doi.org/10.1515/jib-2017-0081
Innes, M., Edelman, A., Fischer, K., Rackauckas, C., Saba, E.: Introducing Lux: a julia package for rapid development of custom deep learning models. J. Open Source Softw. 6(57) (2021)
Ionita, M., Armeanu, A.: Sequential monte carlo methods for parameter inference in biological models. Int. J. Mol. Sci. 19(12), 3811 (2018)
Joshi-Tope, G., et al.: The Reactome: a knowledge base of biologic pathways and processes. Nucleic Acids Res. 33(Database Issue), D428–D432 (2005). https://doi.org/10.1093/nar/gki072
Liepe, J., et al.: ABC-SysBio-approximate Bayesian computation in Python with GPU support. Bioinformatics 26(14), 1797–1799 (2010). https://doi.org/10.1093/bioinformatics/btq278
MathWorks: MATLAB (2021). Version R2021a
Mogensen, P., Larsen, A., Städler, N.: Optim.jl: a mathematical optimization package for Julia. J. Open Source Softw. 3(24), 615 (2018). https://doi.org/10.21105/joss.00615
Montoni, F., et al.: Anguix: cell signaling modeling improvement through Sabio-RK association to Reactome. In: 2022 IEEE 18th International Conference on e-Science (e-Science), pp. 425–426 (2022). https://doi.org/10.1109/eScience55777.2022.00070
Montoni, F., et al.: Integration of Sabio-RK to the Reactome graph database for efficient gathering of cell signaling pathways, pp. 105–108 (2022). https://doi.org/10.5753/bresci.2022.222789. www.sol.sbc.org.br/index.php/bresci/article/view/20481
Neo4j Inc.: Neo4j (2007). www.neo4j.com/. Accessed 7 Apr 2023
Rackauckas, C., et al.: Universal differential equations for scientific machine learning. arXiv preprint arXiv:2012.09570 (2020)
Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. 2nd edn. Springer, New York (2013). https://doi.org/10.1007/978-1-4757-4145-2
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Smoot, M.E., Ono, K., Ideker, T.: Garuda and cyberinfrastructure: a recipe for interoperable and integrative analysis of complex data in the biological sciences (2010)
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M.P.H.: Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. Roy. Soc. Interface 6(31), 187–202 (2009). https://doi.org/10.1098/rsif.2008.0172
Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020). https://doi.org/10.1038/s41592-019-0686-2
Wittig, U., et al.: SABIO-RK-database for biochemical reaction kinetics. Nucleic Acids Res. 40(D1), D790–D796 (2012). https://doi.org/10.1093/nar/gkr1046
Acknowledgements.
This work was supported by CNPq, CAPES, BECAS Santander and also by grants 13/07467-1, 19/21619-5, 19/24580-2, 20/10329-3, 20/08555-5 and 21/04355-4, São Paulo Research Foundation (FAPESP).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Batista, M., Montoni, F., Campos, C., Nogueira, R., Armelin, H.A., Reis, M.S. (2023). A Framework for Inference and Selection of Cell Signaling Pathway Dynamic Models. In: Reis, M.S., de Melo-Minardi, R.C. (eds) Advances in Bioinformatics and Computational Biology. BSB 2023. Lecture Notes in Computer Science(), vol 13954. Springer, Cham. https://doi.org/10.1007/978-3-031-42715-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-42715-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42714-5
Online ISBN: 978-3-031-42715-2
eBook Packages: Computer ScienceComputer Science (R0)