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Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis.

Tanevski J, Todorovski L, Kalaidzidis Y, Džeroski S - BMC Syst Biol (2015)

Bottom Line: Furthermore, taking into account the complexity of the model does not lead to better model selection.However, the use of domain-specific criteria results in a remarkable improvement over the other two model selection criteria.We also find that some of the model structures discarded as implausible in previous studies lead to the expected Rab5-Rab7 switch behavior.

View Article: PubMed Central - PubMed

Affiliation: Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia. jovan.tanevski@ijs.si.

ABSTRACT

Background: Given its recent rapid development and the central role that modeling plays in the discipline, systems biology clearly needs methods for automated modeling of dynamical systems. Process-based modeling focuses on explanatory models of dynamical systems; it constructs such models from measured time-course data and formalized modeling knowledge. In this paper, we apply process-based modeling to the practically relevant task of modeling the Rab5-Rab7 conversion switch in endocytosis. The task is difficult due to the limited observability of the system variables and the noisy measurements, which pose serious challenges to the process of model selection. To address these issues, we propose a domain-specific model selection criteria that take into account knowledge about the necessary properties of the simulated model behavior.

Results: In a series of modeling experiments, we compare the results of process-based modeling obtained with different model selection criteria. The first is the standard maximum likelihood criterion based solely on least-squares model error. The second one is a parsimony-based criterion that also takes into account model complexity. We also introduce three domain-specific criteria based on domain expert expectations about the simulated behavior of an endocytosis model. According to the first criterion, 90 of the candidate models are indistinguishable. Furthermore, taking into account the complexity of the model does not lead to better model selection. However, the use of domain-specific criteria results in a remarkable improvement over the other two model selection criteria.

Conclusions: We demonstrate the applicability of process-based modeling to the task of modeling the Rab5-Rab7 dynamics in endocytosis. Our experiments show that the domain-specific criteria outperform the standard domain-independent criteria for model selection. We also find that some of the model structures discarded as implausible in previous studies lead to the expected Rab5-Rab7 switch behavior.

No MeSH data available.


The process of automated modeling with ProBMoT
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Related In: Results  -  Collection

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Fig1: The process of automated modeling with ProBMoT

Mentions: A graphical description of the process of automated modeling using ProBMoT is presented in Fig. 1. ProBMoT takes as input time-series data, i.e., measurements of the dynamical behavior of the observed system. It also takes as input modeling knowledge about the studied domain, represented as a library of template model components, i.e., entities and processes. Finally, it takes as input a set of constraints, i.e., an incomplete model, that correspond to the particular modeling assumptions made for the specific task at hand.Fig. 1


Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis.

Tanevski J, Todorovski L, Kalaidzidis Y, Džeroski S - BMC Syst Biol (2015)

The process of automated modeling with ProBMoT
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482292&req=5

Fig1: The process of automated modeling with ProBMoT
Mentions: A graphical description of the process of automated modeling using ProBMoT is presented in Fig. 1. ProBMoT takes as input time-series data, i.e., measurements of the dynamical behavior of the observed system. It also takes as input modeling knowledge about the studied domain, represented as a library of template model components, i.e., entities and processes. Finally, it takes as input a set of constraints, i.e., an incomplete model, that correspond to the particular modeling assumptions made for the specific task at hand.Fig. 1

Bottom Line: Furthermore, taking into account the complexity of the model does not lead to better model selection.However, the use of domain-specific criteria results in a remarkable improvement over the other two model selection criteria.We also find that some of the model structures discarded as implausible in previous studies lead to the expected Rab5-Rab7 switch behavior.

View Article: PubMed Central - PubMed

Affiliation: Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia. jovan.tanevski@ijs.si.

ABSTRACT

Background: Given its recent rapid development and the central role that modeling plays in the discipline, systems biology clearly needs methods for automated modeling of dynamical systems. Process-based modeling focuses on explanatory models of dynamical systems; it constructs such models from measured time-course data and formalized modeling knowledge. In this paper, we apply process-based modeling to the practically relevant task of modeling the Rab5-Rab7 conversion switch in endocytosis. The task is difficult due to the limited observability of the system variables and the noisy measurements, which pose serious challenges to the process of model selection. To address these issues, we propose a domain-specific model selection criteria that take into account knowledge about the necessary properties of the simulated model behavior.

Results: In a series of modeling experiments, we compare the results of process-based modeling obtained with different model selection criteria. The first is the standard maximum likelihood criterion based solely on least-squares model error. The second one is a parsimony-based criterion that also takes into account model complexity. We also introduce three domain-specific criteria based on domain expert expectations about the simulated behavior of an endocytosis model. According to the first criterion, 90 of the candidate models are indistinguishable. Furthermore, taking into account the complexity of the model does not lead to better model selection. However, the use of domain-specific criteria results in a remarkable improvement over the other two model selection criteria.

Conclusions: We demonstrate the applicability of process-based modeling to the task of modeling the Rab5-Rab7 dynamics in endocytosis. Our experiments show that the domain-specific criteria outperform the standard domain-independent criteria for model selection. We also find that some of the model structures discarded as implausible in previous studies lead to the expected Rab5-Rab7 switch behavior.

No MeSH data available.