Dynamic basis pursuit regularization for complex biochemical pathway identification

Martin Brown; Fei He; George Papadopoulos

doi:10.1109/CDC.2009.5400622

Dynamic basis pursuit regularization for complex biochemical pathway identification

Martin Brown, Fei He, George Papadopoulos

University of Manchester

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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 language	English
Title of host publication	Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Publisher	IEEE
Pages	952-957
Number of pages	6
ISBN (Print)	9781424438716
DOIs	https://doi.org/10.1109/CDC.2009.5400622
Publication status	Published - 1 Dec 2009
Externally published	Yes
Event	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009 - Shanghai, China Duration: 15 Dec 2009 → 18 Dec 2009

Conference

Conference	48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009
Country/Territory	China
City	Shanghai
Period	15/12/09 → 18/12/09

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

Access to Document

10.1109/CDC.2009.5400622

Cite this

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 proceeding › Conference proceeding › peer-review

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

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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.",

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AB - 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.

KW - Parameter estimation

KW - Biological system modeling

KW - Cells (biology)

KW - Evolution (biology)

KW - Systems biology

KW - Control system synthesis

KW - Helium

KW - Kinetic theory

KW - Biochemical analysis

KW - Sensitivity analysis

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EP - 957

BT - Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009

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T2 - 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference, CDC/CCC 2009

Y2 - 15 December 2009 through 18 December 2009

ER -

Dynamic basis pursuit regularization for complex biochemical pathway identification

Abstract

Conference

Keywords

ASJC Scopus subject areas

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