BDC-Decomposition for global influence analysis

Franco Blanchini, Giulia Giordano*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
127 Downloads (Pure)

Abstract

In biochemical networks, the steady-state input-output influence is the sign of the output steady-state variation due to a persistent positive input perturbation; if the sign does not depend on the value of the strictly positive system parameters, the influence is structural. As recently shown for small perturbations, when the linearized system approximation is valid, steady-state input-output influences can be structurally assessed, for biochemical networks with m unknown parameters, by means of a vertex algorithm with complexity 2m. This letter shows that the structural input-output influence of a biochemical network is a global property, which does not require any small-perturbation assumption. It also shows that, using a new algorithm, the complexity can be reduced down to 2m-n , where n is the system order, thus drastically reducing the computation time. Finally, when the uncertain parameters belong to known intervals, non-conservative bounds are given for the steady-state ratio between output and input, allowing for sensitivity analysis.

Original languageEnglish
Article number8444706
Pages (from-to)260-265
JournalIEEE Control Systems Letters
Volume3
Issue number2
DOIs
Publication statusPublished - 2019

Bibliographical note

Accepted Author Manuscript

Keywords

  • Biomolecular systems
  • network analysis and control
  • systems biology

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