A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events
This paper presents a modeling framework for an intracellular signaling network based on formalisms derived from the fundamental concepts in probability theory. Cellular behavior is mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation o...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
ASME International
2018
|
| Online Access: | http://hdl.handle.net/1721.1/118928 https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 |
| _version_ | 1826198067438682112 |
|---|---|
| author | Mayalu, Michaelle N Asada, Haruhiko |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Mayalu, Michaelle N Asada, Haruhiko |
| author_sort | Mayalu, Michaelle N |
| collection | MIT |
| description | This paper presents a modeling framework for an intracellular signaling network based on formalisms derived from the fundamental concepts in probability theory. Cellular behavior is mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation of cell surface receptors. Multiple protein signal transductions occur concurrently through diverse pathways triggered by different extracellular cues. Through crosstalk, these pathways intersect at various node proteins. The state of a particular node protein is dependent on the binding order of molecules from various pathways. The probability of a particular binding order is evaluated using state dependent transduction time probabilities associated with each pathway. In this way, the probability of the cell to be in a given internal state is tracked and used to gain insight into the cell's phenotypic behavior. A simulation example illustrates the approach. Future work will incorporate the proposed method into the development of a feedback control strategy for the development of an in silico control design of endothelial cell migration during angiogenesis. Copyright © 2012 by ASME. |
| first_indexed | 2024-09-23T10:58:21Z |
| format | Article |
| id | mit-1721.1/118928 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:58:21Z |
| publishDate | 2018 |
| publisher | ASME International |
| record_format | dspace |
| spelling | mit-1721.1/1189282022-10-01T00:19:33Z A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events Mayalu, Michaelle N Asada, Haruhiko Massachusetts Institute of Technology. Department of Mechanical Engineering Mayalu, Michaelle N Asada, Haruhiko This paper presents a modeling framework for an intracellular signaling network based on formalisms derived from the fundamental concepts in probability theory. Cellular behavior is mediated by a network of intracellular protein activations that originate at the membrane in response to stimulation of cell surface receptors. Multiple protein signal transductions occur concurrently through diverse pathways triggered by different extracellular cues. Through crosstalk, these pathways intersect at various node proteins. The state of a particular node protein is dependent on the binding order of molecules from various pathways. The probability of a particular binding order is evaluated using state dependent transduction time probabilities associated with each pathway. In this way, the probability of the cell to be in a given internal state is tracked and used to gain insight into the cell's phenotypic behavior. A simulation example illustrates the approach. Future work will incorporate the proposed method into the development of a feedback control strategy for the development of an in silico control design of endothelial cell migration during angiogenesis. Copyright © 2012 by ASME. National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (Grant EFRI-0735997) National Science Foundation (U.S.). Center on Emergent Behaviors of Integrated Cellular Systems (STC-0902396) Singapore-MIT Alliance for Research and Technology (SMART) 2018-11-06T18:15:29Z 2018-11-06T18:15:29Z 2012-10 2018-10-23T16:17:00Z Article http://purl.org/eprint/type/JournalArticle 978-0-7918-4529-5 http://hdl.handle.net/1721.1/118928 Mayalu, Michaëlle N., and H. Harry Asada. “A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events.” ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference, 17-19 October, 2012, Fort Lauderdale, Florida, ASME, 2012, pp. 579–83. https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 http://dx.doi.org/10.1115/DSCC2012-MOVIC2012-8631 ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf ASME International ASME |
| spellingShingle | Mayalu, Michaelle N Asada, Haruhiko A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title | A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title_full | A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title_fullStr | A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title_full_unstemmed | A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title_short | A Time-Based Approach to Stochastic Modeling of Intracellular Signaling Events |
| title_sort | time based approach to stochastic modeling of intracellular signaling events |
| url | http://hdl.handle.net/1721.1/118928 https://orcid.org/0000-0002-9678-0157 https://orcid.org/0000-0003-3155-6223 |
| work_keys_str_mv | AT mayalumichaellen atimebasedapproachtostochasticmodelingofintracellularsignalingevents AT asadaharuhiko atimebasedapproachtostochasticmodelingofintracellularsignalingevents AT mayalumichaellen timebasedapproachtostochasticmodelingofintracellularsignalingevents AT asadaharuhiko timebasedapproachtostochasticmodelingofintracellularsignalingevents |