ModelMage: A TOOL FOR AUTOMATIC MODEL GENERATION, SELECTION AND MANAGEMENT

Abstract

Mathematical modeling of biological systems usually involves implementing, simulating, and discriminating several candidate models that represent alternative hypotheses. Generating and managing these candidate models is a tedious and difficult task and can easily lead to errors. ModelMage is a tool that facilitates management of candidate models. It is designed for the easy and rapid development, generation, simulation, and discrimination of candidate models. The main idea of the program is to automatically create a defined set of model alternatives from a single master model. The user provides only one SBML-model and a set of directives from which the candidate models are created by leaving out species, modifiers or reactions. After generating models the software can automatically fit all these models to the data and provides a ranking for model selection, in case data is available. In contrast to other model generation programs, ModelMage aims at generating only a limited set of models that the user can precisely define. ModelMage uses COPASI as a simulation and optimization engine. Thus, all simulation and optimization features of COPASI are readily incorporated. ModelMage can be downloaded from http://sysbio.molgen.mpg.de/modelmage and is distributed as free software.

ArnS, a kinase involved in starvation‐induced archaellum expression

“…Model selection was done using Modelmage software (Flöttmann et al ., ). In order to select the most parsimonious mathematical model, which best approximates the data, we used the Akaike Information Criterion corrected for small sample sizes ( AIC c ) (Eq.…”

“….,n as the data pointed for experiment i. w i represents the respective weight of experiment i, set to the inverse of the average of the respective time series.ŷ i;j is the simulated value for data point number j within experiment i and y i,j is the measured data point j within experiment i. Model selection was done using Modelmage software (Fl€ ottmann et al, 2008). In order to select the most parsimonious mathematical model, which best approximates the data, we used the Akaike Information Criterion corrected for small sample sizes (AIC c ) (Eq.…”

Modelling reveals novel roles of two parallel signalling pathways and homeostatic feedbacks in yeast

“…When estimated parameters hit parameter boundaries, the boundaries were relaxed and the model refitted until the fit converged within defined parameter boundaries. Model ranking was performed using modelMaGe (Flottmann et al , 2008; Schaber et al , 2011).…”

Rapid process synthesis supported by a unified modular software framework

“…To help in this respect, methods have been proposed to reveal information about an appropriate reaction network for simple process models [10][11][12][13]. Methods regarding structure identification have been established as well (see [14][15][16][17]). They give hints which substances should be included in a model.…”