Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network
© 2019 American Automatic Control Council. Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs conce...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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IEEE
2021
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| Online Access: | https://hdl.handle.net/1721.1/137959 |
| _version_ | 1826188908081184768 |
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| author | Bruno, Simone Al-Radhawi, M. Ali Sontag, Eduardo D. Del Vecchio, Domitilla |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Bruno, Simone Al-Radhawi, M. Ali Sontag, Eduardo D. Del Vecchio, Domitilla |
| author_sort | Bruno, Simone |
| collection | MIT |
| description | © 2019 American Automatic Control Council. Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs concentrations, to the pluripotent state. Due to the OL nature of this approach, the concentration of TFs cannot be accurately controlled. Recently, a closed loop (CL) feedback control strategy was proposed to overcome this problem with promising theoretical results. However, previous analyses of the controller were based on deterministic models. It is well known that cellular systems are characterized by substantial stochasticity, especially when molecules are in low copy number as it is the case in reprogramming problems wherein the gene copy number is usually one or two. Hence, in this paper, we analyze the Chemical Master Equation (CME) for the reaction model of the PL GRN with and without the feedback controller. We computationally and analytically investigate the performance of the controller in biologically relevant parameter regimes where stochastic effects dictate system dynamics. Our results indicate that the feedback control approach still ensures reprogramming even when both the PL GRN and the controller are stochastic. |
| first_indexed | 2024-09-23T08:06:33Z |
| format | Article |
| id | mit-1721.1/137959 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:06:33Z |
| publishDate | 2021 |
| publisher | IEEE |
| record_format | dspace |
| spelling | mit-1721.1/1379592023-02-09T16:00:12Z Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network Bruno, Simone Al-Radhawi, M. Ali Sontag, Eduardo D. Del Vecchio, Domitilla Massachusetts Institute of Technology. Department of Mechanical Engineering © 2019 American Automatic Control Council. Cellular reprogramming is traditionally accomplished through an open loop (OL) control approach, wherein key transcription factors (TFs) are injected in cells to steer the state of the pluripotency (PL) gene regulatory network (GRN), as encoded by TFs concentrations, to the pluripotent state. Due to the OL nature of this approach, the concentration of TFs cannot be accurately controlled. Recently, a closed loop (CL) feedback control strategy was proposed to overcome this problem with promising theoretical results. However, previous analyses of the controller were based on deterministic models. It is well known that cellular systems are characterized by substantial stochasticity, especially when molecules are in low copy number as it is the case in reprogramming problems wherein the gene copy number is usually one or two. Hence, in this paper, we analyze the Chemical Master Equation (CME) for the reaction model of the PL GRN with and without the feedback controller. We computationally and analytically investigate the performance of the controller in biologically relevant parameter regimes where stochastic effects dictate system dynamics. Our results indicate that the feedback control approach still ensures reprogramming even when both the PL GRN and the controller are stochastic. 2021-11-09T16:22:39Z 2021-11-09T16:22:39Z 2019-07 2020-07-08T14:55:37Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/137959 Bruno, Simone, Al-Radhawi, M. Ali, Sontag, Eduardo D. and Del Vecchio, Domitilla. 2019. "Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network." Proceedings of the American Control Conference, 2019-July. en 10.23919/acc.2019.8814355 Proceedings of the American Control Conference Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf IEEE PMC |
| spellingShingle | Bruno, Simone Al-Radhawi, M. Ali Sontag, Eduardo D. Del Vecchio, Domitilla Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title | Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title_full | Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title_fullStr | Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title_full_unstemmed | Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title_short | Stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| title_sort | stochastic analysis of genetic feedback controllers to reprogram a pluripotency gene regulatory network |
| url | https://hdl.handle.net/1721.1/137959 |
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