Trade-offs between retroactivity and noise in connected transcriptional components

At the interconnection of two gene transcriptional components in a biomolecular network, the noise in the downstream component can be reduced by increasing its gene copy number. However, this method of reducing noise increases the load applied to the upstream system, called retroactivity, thereby ca...

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Bibliographic Details
Main Authors: Del Vecchio, Domitilla (Contributor), Herath, Narmada K. (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor)
Format: Article
Language:English
Published: 2015-06-15T16:02:04Z.
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Online Access:Get fulltext
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100 1 0 |a Del Vecchio, Domitilla  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Massachusetts Institute of Technology. Department of Mechanical Engineering  |e contributor 
100 1 0 |a Herath, Narmada K.  |e contributor 
100 1 0 |a Del Vecchio, Domitilla  |e contributor 
700 1 0 |a Herath, Narmada K.  |e author 
245 0 0 |a Trade-offs between retroactivity and noise in connected transcriptional components 
260 |c 2015-06-15T16:02:04Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/97415 
520 |a At the interconnection of two gene transcriptional components in a biomolecular network, the noise in the downstream component can be reduced by increasing its gene copy number. However, this method of reducing noise increases the load applied to the upstream system, called retroactivity, thereby causing a perturbation in the upstream system. In this work, we quantify the error in the system trajectories caused by perturbations due to retroactivity and noise, and analyze the trade-off between these two perturbations. We model the system as a set of nonlinear chemical Langevin equations and quantify the trade-off by employing contraction theory for stochastic systems. 
520 |a National Science Foundation (U.S.). Division of Computing and Communication Foundations (Award 1058127) 
520 |a United States. Air Force Office of Scientific Research (Award FA9550-12-1-0129) 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of the 2014 American Control Conference