Dynamic basis pursuit regularization for complex biochemical pathway identification

Martin Brown, Fei He, George Papadopoulos

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

The availability of both reliable parameter (kinetic constant) estimates and knowledge about sensitive pathway interactions are still limiting steps in the analysis of biochemical signal transduction pathways. This paper investigates feature selection/model reduction in biochemical pathways by examining parameter sensitivity using basis pursuit regularization. A 1-norm model complexity measure allows model structures to be ranked in a continuous manner. In particular, this paper analyzes the limitations associated with collocation-based approaches to pathway parameter locus identification which transform dynamic parameter estimation into a simple algebraic problem. The bias associated with these approaches can be overcome using a dynamic basis pursuit regularization approach which is developed, analyzed and compared with collocation approaches.

Original languageEnglish
Title of host publicationProceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
PublisherIEEE
Pages952-957
Number of pages6
ISBN (Print)9781424438716
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 - Shanghai, China
Duration: 15 Dec 200918 Dec 2009

Conference

Conference48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
CountryChina
CityShanghai
Period15/12/0918/12/09

Fingerprint

Basis Pursuit
Pathway
Regularization
Signal transduction
Model structures
Collocation
Kinetic parameters
Parameter estimation
Feature extraction
Identification (control systems)
Availability
Parameter Sensitivity
Model Complexity
Complexity Measure
Signal Transduction
Selection Model
Feature Model
Model Reduction
Feature Selection
Locus

Keywords

  • Parameter estimation
  • Biological system modeling
  • Cells (biology)
  • Evolution (biology)
  • Systems biology
  • Control system synthesis
  • Helium
  • Kinetic theory
  • Biochemical analysis
  • Sensitivity analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

Cite this

Brown, M., He, F., & Papadopoulos, G. (2009). Dynamic basis pursuit regularization for complex biochemical pathway identification. In Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 (pp. 952-957). [5400622] IEEE. https://doi.org/10.1109/CDC.2009.5400622

Dynamic basis pursuit regularization for complex biochemical pathway identification. / Brown, Martin; He, Fei; Papadopoulos, George.

Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. IEEE, 2009. p. 952-957 5400622.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Brown, M, He, F & Papadopoulos, G 2009, Dynamic basis pursuit regularization for complex biochemical pathway identification. in Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009., 5400622, IEEE, pp. 952-957, 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009, Shanghai, China, 15/12/09. https://doi.org/10.1109/CDC.2009.5400622
Brown M, He F, Papadopoulos G. Dynamic basis pursuit regularization for complex biochemical pathway identification. In Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. IEEE. 2009. p. 952-957. 5400622 https://doi.org/10.1109/CDC.2009.5400622
Brown, Martin ; He, Fei ; Papadopoulos, George. / Dynamic basis pursuit regularization for complex biochemical pathway identification. Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009. IEEE, 2009. pp. 952-957
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