A hybrid agent-based approach for modeling microbiological systems
Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use di...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/84456 http://hdl.handle.net/10220/10170 |
| _version_ | 1826113463273914368 |
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| author | Guo, Zaiyi. Sloot, Peter M. A. Tay, Joc Cing. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Guo, Zaiyi. Sloot, Peter M. A. Tay, Joc Cing. |
| author_sort | Guo, Zaiyi. |
| collection | NTU |
| description | Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 103 cells and 1.2×106 molecules. The model produces cell migration patterns that are comparable to laboratory observations. |
| first_indexed | 2024-10-01T03:23:38Z |
| format | Journal Article |
| id | ntu-10356/84456 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:23:38Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/844562020-05-28T07:17:54Z A hybrid agent-based approach for modeling microbiological systems Guo, Zaiyi. Sloot, Peter M. A. Tay, Joc Cing. School of Computer Engineering DRNTU::Engineering::Computer science and engineering Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 103 cells and 1.2×106 molecules. The model produces cell migration patterns that are comparable to laboratory observations. 2013-06-11T04:03:22Z 2019-12-06T15:45:31Z 2013-06-11T04:03:22Z 2019-12-06T15:45:31Z 2008 2008 Journal Article Guo, Z., Sloot, P. M. A., & Tay, J. C. (2008). A hybrid agent-based approach for modeling microbiological systems. Journal of Theoretical Biology, 255(2), 163-175. https://hdl.handle.net/10356/84456 http://hdl.handle.net/10220/10170 10.1016/j.jtbi.2008.08.008 en Journal of theoretical biology © 2008 Elsevier Ltd. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Guo, Zaiyi. Sloot, Peter M. A. Tay, Joc Cing. A hybrid agent-based approach for modeling microbiological systems |
| title | A hybrid agent-based approach for modeling microbiological systems |
| title_full | A hybrid agent-based approach for modeling microbiological systems |
| title_fullStr | A hybrid agent-based approach for modeling microbiological systems |
| title_full_unstemmed | A hybrid agent-based approach for modeling microbiological systems |
| title_short | A hybrid agent-based approach for modeling microbiological systems |
| title_sort | hybrid agent based approach for modeling microbiological systems |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/84456 http://hdl.handle.net/10220/10170 |
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