### Abstract

Original language | English |
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Pages | 10454–10459 |

DOIs | |

Publication status | Published - 2011 |

Event | IFAC World Congress - Milan, Italy Duration: 28 Aug 2011 → 2 Sep 2011 |

### Conference

Conference | IFAC World Congress |
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Country | Italy |

City | Milan |

Period | 28/08/11 → 2/09/11 |

### Fingerprint

### Bibliographical note

The full text is currently unavailable on the repository.### Keywords

- Parameter Estimation
- Systems Biology
- Symmetry

### Cite this

*Set-based parameter estimation for symmetric network motifs*. 10454–10459. Paper presented at IFAC World Congress, Milan, Italy. https://doi.org/10.3182/20110828-6-IT-1002.03108

**Set-based parameter estimation for symmetric network motifs.** / Rumschinski, P.; Laila, Dina Shona; Findeisen, R.

Research output: Contribution to conference › Paper

}

TY - CONF

T1 - Set-based parameter estimation for symmetric network motifs

AU - Rumschinski, P.

AU - Laila, Dina Shona

AU - Findeisen, R.

N1 - The full text is currently unavailable on the repository.

PY - 2011

Y1 - 2011

N2 - Deriving a predictive model in systems biology is a complex task. One major problem is the typically large network size, which renders the analysis with standard methods difficult. Symmetry, as omnipresent in nature, was used in many applications to encounter this problem. In this work, we investigate the influence of symmetry on set-based parameter estimation. We show that the presence of symmetry in a model can be used to significantly simplify the parameter estimation problem. This is done by determining a symmetry-adapted basis, corresponding to a linear representation of a finite group, in which the problem size is of smaller dimension. We demonstrate the applicability of this approach for several common network motifs, as e.g. the Michaelis-Menten reaction and the feedforward motif.

AB - Deriving a predictive model in systems biology is a complex task. One major problem is the typically large network size, which renders the analysis with standard methods difficult. Symmetry, as omnipresent in nature, was used in many applications to encounter this problem. In this work, we investigate the influence of symmetry on set-based parameter estimation. We show that the presence of symmetry in a model can be used to significantly simplify the parameter estimation problem. This is done by determining a symmetry-adapted basis, corresponding to a linear representation of a finite group, in which the problem size is of smaller dimension. We demonstrate the applicability of this approach for several common network motifs, as e.g. the Michaelis-Menten reaction and the feedforward motif.

KW - Parameter Estimation

KW - Systems Biology

KW - Symmetry

U2 - 10.3182/20110828-6-IT-1002.03108

DO - 10.3182/20110828-6-IT-1002.03108

M3 - Paper

SP - 10454

EP - 10459

ER -