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Parameter estimation in systems biology models by using extended kalman filter

  • Silesian University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Models in systems biology, which reflect complex dynamic biological phenomena aremost often described as ordinary differential equations (ODE). Characteristic properties of these differential equations is nonlinearity and large size (number of state variables). These models also contain large numbers of unknown parameters. So the main challenge in developing models in systems biology is estimation of numerous unknown parameters in nonlinear differential equations. There are already numerous approaches to parameter estimation in systems biology models. However, main difficulties speed of convergence and multiple minima (multiple solutions) are still obstacles in achieving solutions of sufficient efficiency. In this chapter we propose a new approach based on combination of extended Kalman filtering dynamical optimization with spline approximation of solutions to ODE, for parameter estimation in systems biology models. We present the main idea and we show comparisons to some published results.

Original languageEnglish
Title of host publicationMan–Machine Interactions - 4th International Conference on Man–Machine Interactions, ICMMI 2015
EditorsTadeusz Czachórski, Aleksandra Gruca, Agnieszka Brachman, Stanisław Kozielski, Tadeusz Czachórski
PublisherSpringer Verlag
Pages195-204
Number of pages10
ISBN (Print)9783319234366
DOIs
Publication statusPublished - 2016
Event4th International Conference on Man–Machine Interactions, ICMMI 2015 - Kocierz Pass, Poland
Duration: 6 Oct 20159 Oct 2015

Publication series

NameAdvances in Intelligent Systems and Computing
Volume391
ISSN (Print)2194-5357

Conference

Conference4th International Conference on Man–Machine Interactions, ICMMI 2015
Country/TerritoryPoland
CityKocierz Pass
Period6/10/159/10/15

Keywords

  • Dynamic
  • Extended kalman filter
  • Optimization
  • Parameter estimation
  • Spline approximation
  • Systems biology

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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