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
