Computing the structural influence matrix for biological systems
(2016) In Journal of Mathematical Biology 72(7). p.1927-1958- Abstract
We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally... (More)
We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.
(Less)
- author
- Giordano, Giulia LU ; Cuba Samaniego, Christian ; Franco, Elisa and Blanchini, Franco
- organization
- publishing date
- 2016-06-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Influence matrix, Perfect adaptation, Steady-state variation, Structural analysis, Vertex algorithm
- in
- Journal of Mathematical Biology
- volume
- 72
- issue
- 7
- pages
- 32 pages
- publisher
- Springer
- external identifiers
-
- scopus:84944627209
- pmid:26395779
- ISSN
- 0303-6812
- DOI
- 10.1007/s00285-015-0933-9
- language
- English
- LU publication?
- no
- id
- 63d437f2-13bf-42cf-b648-3d9c5d8fc2b9
- date added to LUP
- 2016-07-06 15:10:35
- date last changed
- 2024-04-05 03:29:43
@article{63d437f2-13bf-42cf-b648-3d9c5d8fc2b9,
abstract = {{<p>We consider the problem of identifying structural influences of external inputs on steady-state outputs in a biological network model. We speak of a structural influence if, upon a perturbation due to a constant input, the ensuing variation of the steady-state output value has the same sign as the input (positive influence), the opposite sign (negative influence), or is zero (perfect adaptation), for any feasible choice of the model parameters. All these signs and zeros can constitute a structural influence matrix, whose (i, j) entry indicates the sign of steady-state influence of the jth system variable on the ith variable (the output caused by an external persistent input applied to the jth variable). Each entry is structurally determinate if the sign does not depend on the choice of the parameters, but is indeterminate otherwise. In principle, determining the influence matrix requires exhaustive testing of the system steady-state behaviour in the widest range of parameter values. Here we show that, in a broad class of biological networks, the influence matrix can be evaluated with an algorithm that tests the system steady-state behaviour only at a finite number of points. This algorithm also allows us to assess the structural effect of any perturbation, such as variations of relevant parameters. Our method is applied to nontrivial models of biochemical reaction networks and population dynamics drawn from the literature, providing a parameter-free insight into the system dynamics.</p>}},
author = {{Giordano, Giulia and Cuba Samaniego, Christian and Franco, Elisa and Blanchini, Franco}},
issn = {{0303-6812}},
keywords = {{Influence matrix; Perfect adaptation; Steady-state variation; Structural analysis; Vertex algorithm}},
language = {{eng}},
month = {{06}},
number = {{7}},
pages = {{1927--1958}},
publisher = {{Springer}},
series = {{Journal of Mathematical Biology}},
title = {{Computing the structural influence matrix for biological systems}},
url = {{http://dx.doi.org/10.1007/s00285-015-0933-9}},
doi = {{10.1007/s00285-015-0933-9}},
volume = {{72}},
year = {{2016}},
}