### Abstract

Language | English |
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Pages | 6599-6604 |

Number of pages | 6 |

Publication status | Published - 2011 |

Event | 30th Chinese Control Conference - Yantai, China Duration: 22 Jul 2011 → 24 Jul 2011 |

### Conference

Conference | 30th Chinese Control Conference |
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Country | China |

City | Yantai |

Period | 22/07/11 → 24/07/11 |

### Fingerprint

### Keywords

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

### Cite this

*A direct derivative method for estimating kinetic parameters of biological networks*. 6599-6604. Paper presented at 30th Chinese Control Conference, Yantai, China.

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**A direct derivative method for estimating kinetic parameters of biological networks.** / Jia, Jianfang; Yue, Hong.

Research output: Contribution to conference › Paper

TY - CONF

T1 - A direct derivative method for estimating kinetic parameters of biological networks

AU - Jia, Jianfang

AU - Yue, Hong

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - biological system modeling

KW - spline

KW - smoothing methods

KW - parameter estimation

KW - computational modeling

KW - data models

UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6001635

M3 - Paper

SP - 6599

EP - 6604

ER -