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A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

Quaiser T, Dittrich A, Schaper F, Mönnigmann M - BMC Syst Biol (2011)

Bottom Line: These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small.In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Automatic Control and Systems Theory, Ruhr University Bochum, D-44801 Bochum, Germany.

ABSTRACT

Background: Modeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood parameter estimation, only provides valid results, if the complexity of the model is in balance with the amount and quality of the experimental data. If this is the case the model is said to be identifiable for the given data. If a model turns out to be unidentifiable, two steps can be taken. Either additional experiments need to be conducted, or the model has to be simplified.

Results: We propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results. These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small. We choose parameter variances as stopping criterion, since they are concise and easy to interpret. For both, the parameter estimation and the calculation of parameter variances, multi-start parameter estimations are run on a parallel cluster. In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.

Conclusions: We apply the proposed approach to a model of early signaling events in the JAK-STAT pathway. The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

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Model outputs.
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Figure 2: Model outputs.

Mentions: where IFN is assumed to be removed completely from the medium in a washing step at time t = 7 min. Throughout the paper we consider the outputs y1,...,y4 illustrated in Figure 2. Since it is reasonable to assume that these outputs could be measured every minute in a laboratory experiment, we record their simulated values at times t = 0 min,..., 15 min and discard all other simulated values. The outputs y1,...,y4 correspond to the following quantities: the sum of the concentrations of all phosphorylated STAT1 molecules regardless of their binding or dimerization status (y1); the concentration of all activated JAK molecules regardless of their binding status (y2); the concentration of all STAT1 dimers regardless of their binding and phosphorylation status (y3); and the concentration of all STAT1 monomers regardless of their binding or phosphorylation status (y4).


A simple work flow for biologically inspired model reduction--application to early JAK-STAT signaling.

Quaiser T, Dittrich A, Schaper F, Mönnigmann M - BMC Syst Biol (2011)

Model outputs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3050741&req=5

Figure 2: Model outputs.
Mentions: where IFN is assumed to be removed completely from the medium in a washing step at time t = 7 min. Throughout the paper we consider the outputs y1,...,y4 illustrated in Figure 2. Since it is reasonable to assume that these outputs could be measured every minute in a laboratory experiment, we record their simulated values at times t = 0 min,..., 15 min and discard all other simulated values. The outputs y1,...,y4 correspond to the following quantities: the sum of the concentrations of all phosphorylated STAT1 molecules regardless of their binding or dimerization status (y1); the concentration of all activated JAK molecules regardless of their binding status (y2); the concentration of all STAT1 dimers regardless of their binding and phosphorylation status (y3); and the concentration of all STAT1 monomers regardless of their binding or phosphorylation status (y4).

Bottom Line: These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small.In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

View Article: PubMed Central - HTML - PubMed

Affiliation: Automatic Control and Systems Theory, Ruhr University Bochum, D-44801 Bochum, Germany.

ABSTRACT

Background: Modeling of biological pathways is a key issue in systems biology. When constructing a model, it is tempting to incorporate all known interactions of pathway species, which results in models with a large number of unknown parameters. Fortunately, unknown parameters need not necessarily be measured directly, but some parameter values can be estimated indirectly by fitting the model to experimental data. However, parameter fitting, or, more precisely, maximum likelihood parameter estimation, only provides valid results, if the complexity of the model is in balance with the amount and quality of the experimental data. If this is the case the model is said to be identifiable for the given data. If a model turns out to be unidentifiable, two steps can be taken. Either additional experiments need to be conducted, or the model has to be simplified.

Results: We propose a systematic procedure for model simplification, which consists of the following steps: estimate the parameters of the model, create an identifiability ranking for the estimated parameters, and simplify the model based on the identifiability analysis results. These steps need to be applied iteratively until the resulting model is identifiable, or equivalently, until parameter variances are small. We choose parameter variances as stopping criterion, since they are concise and easy to interpret. For both, the parameter estimation and the calculation of parameter variances, multi-start parameter estimations are run on a parallel cluster. In contrast to related work in systems biology, we do not suggest simplifying a model by fixing some of its parameters, but change the structure of the model.

Conclusions: We apply the proposed approach to a model of early signaling events in the JAK-STAT pathway. The resulting model is not only identifiable with small parameter variances, but also shows the best trade-off between goodness of fit and model complexity.

Show MeSH