A direct derivative method for estimating kinetic parameters of biological networks

Jianfang Jia, Hong Yue

Research output: Contribution to conferencePaperpeer-review


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
Number of pages6
Publication statusPublished - 2011
Event30th Chinese Control Conference - Yantai, China
Duration: 22 Jul 201124 Jul 2011


Conference30th Chinese Control Conference


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


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