Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions
2022 IEEE 61st Conference on Decision and Control (CDC) December 6-9, 2022. Cancún, Mexico
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IEEE|2022 IEEE 61st Conference on Decision and Control (CDC)
2024
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| Online Access: | https://hdl.handle.net/1721.1/155726 |
| _version_ | 1826216349916987392 |
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| author | Grunberg, Theodore W. Del Vecchio, Domitilla |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Grunberg, Theodore W. Del Vecchio, Domitilla |
| author_sort | Grunberg, Theodore W. |
| collection | MIT |
| description | 2022 IEEE 61st Conference on Decision and Control (CDC) December 6-9, 2022. Cancún, Mexico |
| first_indexed | 2024-09-23T16:46:15Z |
| format | Article |
| id | mit-1721.1/155726 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:26:01Z |
| publishDate | 2024 |
| publisher | IEEE|2022 IEEE 61st Conference on Decision and Control (CDC) |
| record_format | dspace |
| spelling | mit-1721.1/1557262025-01-01T04:24:35Z Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions Grunberg, Theodore W. Del Vecchio, Domitilla Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mechanical Engineering 2022 IEEE 61st Conference on Decision and Control (CDC) December 6-9, 2022. Cancún, Mexico Biomolecular systems can often be modeled by chemical reaction networks with unknown parameters. In many cases, the available data is constituted of samples from the stationary distribution, wherein each sample is given by a cell in a population. In this work, we develop a framework to assess identifiability of parameters in such a situation. Working with the Linear Noise Approximation (LNA) we give an algebraic formulation of identifiability and use it to certify identifiability with Hilbert’s Nullstellensatz. We include applications to particular biomolecular systems, focusing on the identifiability of a sequestration-based motif and of a feedback arrangement based on it. 2024-07-19T18:11:12Z 2024-07-19T18:11:12Z 2022-12-06 2024-07-19T18:01:13Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/155726 Grunberg, Theodore W. and Del Vecchio, Domitilla. 2022. "Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions." en 10.1109/cdc51059.2022.9992540 Creative Commons Attribution-Noncommercial-ShareAlike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE|2022 IEEE 61st Conference on Decision and Control (CDC) Author |
| spellingShingle | Grunberg, Theodore W. Del Vecchio, Domitilla Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title | Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title_full | Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title_fullStr | Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title_full_unstemmed | Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title_short | Identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| title_sort | identifiability of linear noise approximation models of chemical reaction networks from stationary distributions |
| url | https://hdl.handle.net/1721.1/155726 |
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