### Abstract

Language | English |
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Pages | 1357-1362 |

Number of pages | 6 |

Publication status | Published - Aug 2009 |

Event | 7th Asian Control Conference, 2009 (ASCC 2009) - , Hong Kong Duration: 27 Aug 2009 → 29 Aug 2009 |

### Conference

Conference | 7th Asian Control Conference, 2009 (ASCC 2009) |
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Country | Hong Kong |

Period | 27/08/09 → 29/08/09 |

### Fingerprint

### Keywords

- biological system modeling
- kinetic theory
- noise measurement
- biology computing
- parameter estimation
- particle swarm optimisation
- probability
- sensitivity analysis

### Cite this

*Sensitivity analysis and parameter estimation of signal transduction pathways model*. 1357-1362. Paper presented at 7th Asian Control Conference, 2009 (ASCC 2009), Hong Kong.

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**Sensitivity analysis and parameter estimation of signal transduction pathways model.** / Jia, J.F.; Yue, H.

Research output: Contribution to conference › Paper

TY - CONF

T1 - Sensitivity analysis and parameter estimation of signal transduction pathways model

AU - Jia, J.F.

AU - Yue, H.

PY - 2009/8

Y1 - 2009/8

N2 - Due to the high nonlinearity in system models, the large number of kinetics parameters involved, the inadequate measurement data in experiments and the noise pollution, etc., parameter estimation is therefore a challenging problem in systems biology. In this work, sensitivity analysis of model output with respect to model parameters is evaluated using Latin hypercube sampling method. Then, a new objective function is proposed based on the probability density function (PDF) of the system output, and particle swarm optimization is used to optimize the objective function through particles' cooperation and evolution. Taking NF-kappaB signal pathways model as an example, this method is applied to rank importance of parameters and to estimate the unknown sensitive parameters for complex signal transduction pathways model. The simulation results show the effectiveness of this new algorithm.

AB - Due to the high nonlinearity in system models, the large number of kinetics parameters involved, the inadequate measurement data in experiments and the noise pollution, etc., parameter estimation is therefore a challenging problem in systems biology. In this work, sensitivity analysis of model output with respect to model parameters is evaluated using Latin hypercube sampling method. Then, a new objective function is proposed based on the probability density function (PDF) of the system output, and particle swarm optimization is used to optimize the objective function through particles' cooperation and evolution. Taking NF-kappaB signal pathways model as an example, this method is applied to rank importance of parameters and to estimate the unknown sensitive parameters for complex signal transduction pathways model. The simulation results show the effectiveness of this new algorithm.

KW - biological system modeling

KW - kinetic theory

KW - noise measurement

KW - biology computing

KW - parameter estimation

KW - particle swarm optimisation

KW - probability

KW - sensitivity analysis

UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5276315

M3 - Paper

SP - 1357

EP - 1362

ER -