Setting Parameters for Biological Models With ANIMO

Stefano Schivo
(Formal Methods and Tools, Faculty of EEMCS, University of Twente, Enschede, The Netherlands)
Jetse Scholma
(Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands)
Marcel Karperien
(Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands)
Janine N. Post
(Developmental BioEngineering, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands)
Jaco van de Pol
(Formal Methods and Tools, Faculty of EEMCS, University of Twente, Enschede, The Netherlands)
Rom Langerak
(Formal Methods and Tools, Faculty of EEMCS, University of Twente, Enschede, The Netherlands)

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.

In Étienne André and Goran Frehse: Proceedings 1st International Workshop on Synthesis of Continuous Parameters (SynCoP 2014), Grenoble, France, 6th April 2014, Electronic Proceedings in Theoretical Computer Science 145, pp. 35–47.
Published: 31st March 2014.

ArXived at: http://dx.doi.org/10.4204/EPTCS.145.5 bibtex PDF
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