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Negative feedback enables structurally signed steady-state influences in artificial biomolecular networks

Giordano, Giulia LU and Franco, Elisa (2016) 55th IEEE Conference on Decision and Control 2016 In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 p.3369-3374
Abstract

We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we show that, in certain negative feedback architectures, changes in the total source concentration cause structurally signed variations of the steady-state output concentration, regardless of reaction rate parameters. Conversely, positive feedback schemes can exhibit the same signed behaviour for reasonable (but not for arbitrary) values of the... (More)

We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we show that, in certain negative feedback architectures, changes in the total source concentration cause structurally signed variations of the steady-state output concentration, regardless of reaction rate parameters. Conversely, positive feedback schemes can exhibit the same signed behaviour for reasonable (but not for arbitrary) values of the parameters. Numerical simulations demonstrate how the steady-state concentrations of different network architectures vary, responding to perturbations in total source amounts, consistently with our structural previsions.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2016 IEEE 55th Conference on Decision and Control, CDC 2016
pages
6 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
55th IEEE Conference on Decision and Control 2016
external identifiers
  • scopus:85010764207
ISBN
9781509018376
DOI
10.1109/CDC.2016.7798776
language
English
LU publication?
yes
id
9ebcee37-63bd-4cc5-8cac-805af70f657d
date added to LUP
2017-01-10 11:35:01
date last changed
2017-03-28 09:49:07
@inproceedings{9ebcee37-63bd-4cc5-8cac-805af70f657d,
  abstract     = {<p>We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we show that, in certain negative feedback architectures, changes in the total source concentration cause structurally signed variations of the steady-state output concentration, regardless of reaction rate parameters. Conversely, positive feedback schemes can exhibit the same signed behaviour for reasonable (but not for arbitrary) values of the parameters. Numerical simulations demonstrate how the steady-state concentrations of different network architectures vary, responding to perturbations in total source amounts, consistently with our structural previsions.</p>},
  author       = {Giordano, Giulia and Franco, Elisa},
  booktitle    = {2016 IEEE 55th Conference on Decision and Control, CDC 2016},
  isbn         = {9781509018376},
  language     = {eng},
  month        = {12},
  pages        = {3369--3374},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Negative feedback enables structurally signed steady-state influences in artificial biomolecular networks},
  url          = {http://dx.doi.org/10.1109/CDC.2016.7798776},
  year         = {2016},
}