## Abstrakti

When compiling a biological model, one often starts with an abstract representation,

that is subsequently refined through several consecutive steps to incorporate

more details regarding various reactants and/or reactions. To avoid the computationally

expensive refitting of the model after each refinement step, the new

parameters should be set so that the numerical behavior of the initial model is

preserved. The iterative process of adding details to a model while preserving its

numerical behavior is called quantitative model refinement, and it has been previously

discussed for ODE-based models and for kappa-based models. In this

paper, we investigate and compare this approach in four modeling frameworks:

ordinary differential equations, rule-based modeling, Petri nets and guarded command

languages. As case study we use a model for the eukaryotic heat shock

response that we refine to include the acetylation of one molecule. We discuss

how to perform the refinement in each of these frameworks in order to avoid the

combinatorial state explosion of the refined model. We conclude that Bionetgen

(and rule-based modeling in general) is well-suited for a compact representation

of the refined model, Petri nets offer a good solution through the use of colors,

while the PRISM refined model may be much larger than the basic model.

that is subsequently refined through several consecutive steps to incorporate

more details regarding various reactants and/or reactions. To avoid the computationally

expensive refitting of the model after each refinement step, the new

parameters should be set so that the numerical behavior of the initial model is

preserved. The iterative process of adding details to a model while preserving its

numerical behavior is called quantitative model refinement, and it has been previously

discussed for ODE-based models and for kappa-based models. In this

paper, we investigate and compare this approach in four modeling frameworks:

ordinary differential equations, rule-based modeling, Petri nets and guarded command

languages. As case study we use a model for the eukaryotic heat shock

response that we refine to include the acetylation of one molecule. We discuss

how to perform the refinement in each of these frameworks in order to avoid the

combinatorial state explosion of the refined model. We conclude that Bionetgen

(and rule-based modeling in general) is well-suited for a compact representation

of the refined model, Petri nets offer a good solution through the use of colors,

while the PRISM refined model may be much larger than the basic model.

Alkuperäiskieli | Englanti |
---|---|

Kustantaja | Turku Centre for Computer Science |

Sivumäärä | 48 |

ISBN (painettu) | 978-952-12-2622-9 |

Tila | Julkaistu - 2013 |

OKM-julkaisutyyppi | D4 Julkaistut kehitykset tai tutkimusraportit tai tutkimukset |