Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach
A cell's behavior in response to stimuli is governed by a signaling network, called cue-signal-response. Endothelial Cells (ECs), for example, migrate towards the source of chemo-attractants by detecting cues (chemo-attractants and their concentration gradient), feeding them into an intra-cellu...
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| Format: | Article |
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ASME International
2018
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| Online Access: | http://hdl.handle.net/1721.1/118796 https://orcid.org/0000-0003-3155-6223 |
| _version_ | 1826209208313315328 |
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| author | Asada, Haruhiko |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Asada, Haruhiko |
| author_sort | Asada, Haruhiko |
| collection | MIT |
| description | A cell's behavior in response to stimuli is governed by a signaling network, called cue-signal-response. Endothelial Cells (ECs), for example, migrate towards the source of chemo-attractants by detecting cues (chemo-attractants and their concentration gradient), feeding them into an intra-cellular signaling network (coded internal state), and producing a response (migration). It is known that the cue-signal-response process is a nonlinear, dynamical system with high dimensionality and stochasticity. This paper presents a system dynamics approach to modeling the cue-signal-response process for the purpose of manipulating and guiding the cell behavior through feedback control. A Hammerstein type model is constructed by representing the entire process in two stages. One is the cue-to-signal process represented as a nonlinear feedforward map, and the other is the signal-to-response process as a stochastic linear dynamical system, which contains feedback loops and auto-regressive dynamics. Analysis of the signaling space based on Singular-Value Decomposition yields a set of reduced order synthetic signals, which are used as inputs to the dynamical system. A prediction-error method is used for identifying the model from experimental data, and an optimal system order is determined based on Akaike's Information Criterion. The resultant low order model is capable of predicting the expected response to cues, and is directly usable for feedback control. The method is applied to an in vitro angiogenic process using microfluidic devices. Topics: Modeling, Signals |
| first_indexed | 2024-09-23T14:19:12Z |
| format | Article |
| id | mit-1721.1/118796 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:19:12Z |
| publishDate | 2018 |
| publisher | ASME International |
| record_format | dspace |
| spelling | mit-1721.1/1187962022-09-29T08:41:03Z Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach Asada, Haruhiko Massachusetts Institute of Technology. Department of Mechanical Engineering Asada, Haruhiko A cell's behavior in response to stimuli is governed by a signaling network, called cue-signal-response. Endothelial Cells (ECs), for example, migrate towards the source of chemo-attractants by detecting cues (chemo-attractants and their concentration gradient), feeding them into an intra-cellular signaling network (coded internal state), and producing a response (migration). It is known that the cue-signal-response process is a nonlinear, dynamical system with high dimensionality and stochasticity. This paper presents a system dynamics approach to modeling the cue-signal-response process for the purpose of manipulating and guiding the cell behavior through feedback control. A Hammerstein type model is constructed by representing the entire process in two stages. One is the cue-to-signal process represented as a nonlinear feedforward map, and the other is the signal-to-response process as a stochastic linear dynamical system, which contains feedback loops and auto-regressive dynamics. Analysis of the signaling space based on Singular-Value Decomposition yields a set of reduced order synthetic signals, which are used as inputs to the dynamical system. A prediction-error method is used for identifying the model from experimental data, and an optimal system order is determined based on Akaike's Information Criterion. The resultant low order model is capable of predicting the expected response to cues, and is directly usable for feedback control. The method is applied to an in vitro angiogenic process using microfluidic devices. Topics: Modeling, Signals National Science Foundation (U.S.). Office of Emerging Frontiers in Research and Innovation (EFRI0735997) Singapore-MIT Alliance for Research and Technology (SMART) 2018-10-30T14:05:04Z 2018-10-30T14:05:04Z 2010-09 2018-10-23T15:38:35Z Article http://purl.org/eprint/type/JournalArticle 978-0-7918-4417-5 http://hdl.handle.net/1721.1/118796 Asada, H. Harry. “Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach.” ASME 2010 Dynamic Systems and Control Conference, Volume 1, 12-15 September, 2010, ASME, Cambridge, Massachusetts, 2010, pp. 445–51. © 2010 by ASME https://orcid.org/0000-0003-3155-6223 http://dx.doi.org/10.1115/DSCC2010-4246 ASME 2010 Dynamic Systems and Control Conference, Volume 1 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 | Asada, Haruhiko Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title | Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title_full | Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title_fullStr | Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title_full_unstemmed | Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title_short | Reduced-Order Cue-Signal-Response Modeling for Angiogenic Cell Migration Control: A Principal Signal Approach |
| title_sort | reduced order cue signal response modeling for angiogenic cell migration control a principal signal approach |
| url | http://hdl.handle.net/1721.1/118796 https://orcid.org/0000-0003-3155-6223 |
| work_keys_str_mv | AT asadaharuhiko reducedordercuesignalresponsemodelingforangiogeniccellmigrationcontrolaprincipalsignalapproach |