### Abstract

Language | Undefined |
---|---|

Title of host publication | Proceedings 1st International Workshop on Synthesis of Continuous Parameters |

Editors | Étienne André, Goran Frehse |

Publisher | Open Publishing Association |

Pages | 35-47 |

Number of pages | 13 |

DOIs | |

State | Published - 8 Apr 2014 |

### Publication series

Name | Electronic Proceedings in Theoretical Computer Science |
---|---|

Publisher | Open Publishing Association |

Volume | 145 |

ISSN (Print) | 2075-2180 |

ISSN (Electronic) | 2075-2180 |

### Keywords

- signal transduction
- EWI-24659
- Experimental data
- Computational modeling
- IR-91060
- METIS-304060
- FMT-TOOLS
- parameter synthesis
- biological networks

### Cite this

*Proceedings 1st International Workshop on Synthesis of Continuous Parameters*(pp. 35-47). (Electronic Proceedings in Theoretical Computer Science; Vol. 145). Open Publishing Association. DOI: 10.4204/EPTCS.145.5

}

*Proceedings 1st International Workshop on Synthesis of Continuous Parameters.*Electronic Proceedings in Theoretical Computer Science, vol. 145, Open Publishing Association, pp. 35-47. DOI: 10.4204/EPTCS.145.5

**Setting Parameters for Biological Models With ANIMO.** / Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Setting Parameters for Biological Models With ANIMO

AU - Schivo,Stefano

AU - Scholma,Jetse

AU - Karperien,Hermanus Bernardus Johannes

AU - Post,Janine Nicole

AU - van de Pol,Jan Cornelis

AU - Langerak,Romanus

N1 - eemcs-eprint-24659

PY - 2014/4/8

Y1 - 2014/4/8

N2 - ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings.

AB - ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions between biological entities in form of a graph, while the parameters determine the speed of occurrence of such interactions. When a mismatch is observed between the behavior of an ANIMO model and experimental data, we want to update the model so that it explains the new data. In general, the topology of a model can be expanded with new (known or hypothetical) nodes, and enables it to match experimental data. However, the unrestrained addition of new parts to a model causes two problems: models can become too complex too fast, to the point of being intractable, and too many parts marked as "hypothetical" or "not known" make a model unrealistic. Even if changing the topology is normally the easier task, these problems push us to try a better parameter fit as a first step, and resort to modifying the model topology only as a last resource. In this paper we show the support added in ANIMO to ease the task of expanding the knowledge on biological networks, concentrating in particular on the parameter settings.

KW - signal transduction

KW - EWI-24659

KW - Experimental data

KW - Computational modeling

KW - IR-91060

KW - METIS-304060

KW - FMT-TOOLS

KW - parameter synthesis

KW - biological networks

U2 - 10.4204/EPTCS.145.5

DO - 10.4204/EPTCS.145.5

M3 - Conference contribution

T3 - Electronic Proceedings in Theoretical Computer Science

SP - 35

EP - 47

BT - Proceedings 1st International Workshop on Synthesis of Continuous Parameters

PB - Open Publishing Association

ER -