Noise suppression in stochastic genetic circuits using PID controllers.

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein cou...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Saurabh Modi, Supravat Dey, Abhyudai Singh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/65edbdce4e924cdd8b76372eac940b19
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:65edbdce4e924cdd8b76372eac940b19
record_format	dspace
spelling	oai:doaj.org-article:65edbdce4e924cdd8b76372eac940b192021-12-02T19:57:21ZNoise suppression in stochastic genetic circuits using PID controllers.1553-734X1553-735810.1371/journal.pcbi.1009249https://doaj.org/article/65edbdce4e924cdd8b76372eac940b192021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009249https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.Saurabh ModiSupravat DeyAbhyudai SinghPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009249 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Saurabh Modi Supravat Dey Abhyudai Singh Noise suppression in stochastic genetic circuits using PID controllers.
description	Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.
format	article
author	Saurabh Modi Supravat Dey Abhyudai Singh
author_facet	Saurabh Modi Supravat Dey Abhyudai Singh
author_sort	Saurabh Modi
title	Noise suppression in stochastic genetic circuits using PID controllers.
title_short	Noise suppression in stochastic genetic circuits using PID controllers.
title_full	Noise suppression in stochastic genetic circuits using PID controllers.
title_fullStr	Noise suppression in stochastic genetic circuits using PID controllers.
title_full_unstemmed	Noise suppression in stochastic genetic circuits using PID controllers.
title_sort	noise suppression in stochastic genetic circuits using pid controllers.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/65edbdce4e924cdd8b76372eac940b19
work_keys_str_mv	AT saurabhmodi noisesuppressioninstochasticgeneticcircuitsusingpidcontrollers AT supravatdey noisesuppressioninstochasticgeneticcircuitsusingpidcontrollers AT abhyudaisingh noisesuppressioninstochasticgeneticcircuitsusingpidcontrollers
_version_	1718375841910489088

Noise suppression in stochastic genetic circuits using PID controllers.

Ejemplares similares