A direct derivative method for estimating kinetic parameters of biological networks

Jianfang Jia, Hong Yue

Research output: Contribution to conferencePaper

Abstract

Challenged by strong nonlinearity of cellular network models, large uncertainty in model parameters, and noisy experimental data, a new parameter estimation algorithm, direct derivative method (DDM), is presented in which the measurement data are firstly fitted with smoothing splines, and then the first-order derivative of state variables are evaluated and substituted into the model. Thus, a dynamic optimization problem is converted into a linear or nonlinear regression problem. There is no need to solve ordinary differential equations of the system models iteratively, the computational complexity is therefore reduced to a large extent. Taking the IκBα-NF-κB signal transduction pathways as an example, unknown parameters are estimated effectively using the proposed DDM algorithm, and various factors that affect the results are investigated.
Original languageEnglish
Pages6599-6604
Number of pages6
Publication statusPublished - 2011
Event30th Chinese Control Conference - Yantai, China
Duration: 22 Jul 201124 Jul 2011

Conference

Conference30th Chinese Control Conference
CountryChina
CityYantai
Period22/07/1124/07/11

Keywords

  • biological system modeling
  • spline
  • smoothing methods
  • parameter estimation
  • computational modeling
  • data models

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  • Cite this

    Jia, J., & Yue, H. (2011). A direct derivative method for estimating kinetic parameters of biological networks. 6599-6604. Paper presented at 30th Chinese Control Conference, Yantai, China.