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|Title:||Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways||Authors:||Zhang, Fan
Zhao, Li Na
Przytycka, Teresa M.
|Keywords:||Generalized logical model; Signaling pathways; Dynamical system; Cancer||Issue Date:||2016||Source:||Zhang, F., Chen, H., Zhao, L. N., Liu, H., Przytycka, T. M., & Zheng, J. (2016). Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways. BMC Systems Biology, 10(S1), 7-.||Series/Report no.:||BMC Systems Biology||Abstract:||Background: Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways. Results: We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks. Conclusion: The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.||URI:||https://hdl.handle.net/10356/81868
|ISSN:||1752-0509||DOI:||10.1186/s12918-015-0249-9||Schools:||School of Computer Engineering||Research Centres:||Complexity Institute||Rights:||© 2016 Zhang et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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