Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways
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 sign...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81868 http://hdl.handle.net/10220/39714 |
| _version_ | 1811682636298452992 |
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| author | Zhang, Fan Chen, Haoting Zhao, Li Na Liu, Hui Przytycka, Teresa M. Zheng, Jie |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Zhang, Fan Chen, Haoting Zhao, Li Na Liu, Hui Przytycka, Teresa M. Zheng, Jie |
| author_sort | Zhang, Fan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:59:59Z |
| format | Journal Article |
| id | ntu-10356/81868 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:59:59Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/818682022-02-16T16:26:33Z Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways Zhang, Fan Chen, Haoting Zhao, Li Na Liu, Hui Przytycka, Teresa M. Zheng, Jie School of Computer Engineering Complexity Institute Generalized logical model; Signaling pathways; Dynamical system; Cancer 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. Published version 2016-01-19T07:44:59Z 2019-12-06T14:41:57Z 2016-01-19T07:44:59Z 2019-12-06T14:41:57Z 2016 Journal Article 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-. 1752-0509 https://hdl.handle.net/10356/81868 http://hdl.handle.net/10220/39714 10.1186/s12918-015-0249-9 26818802 en BMC Systems Biology © 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. 9 p. application/pdf |
| spellingShingle | Generalized logical model; Signaling pathways; Dynamical system; Cancer Zhang, Fan Chen, Haoting Zhao, Li Na Liu, Hui Przytycka, Teresa M. Zheng, Jie Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title | Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title_full | Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title_fullStr | Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title_full_unstemmed | Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title_short | Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| title_sort | generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways |
| topic | Generalized logical model; Signaling pathways; Dynamical system; Cancer |
| url | https://hdl.handle.net/10356/81868 http://hdl.handle.net/10220/39714 |
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