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Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

Tsai KJ, Chang CH - PLoS ONE (2014)

Bottom Line: In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis.The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production.We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.

ABSTRACT
Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

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A concept diagram of the reaction graph.In this example, the product for reaction 1 is a reactant for reaction 2. These two reactions can be connected together with reaction 2 designated as a downstream reaction for reaction 1. If the reactants for reaction 1 are available but the reactants for reaction 2 are not, reaction 2 will receive a non-zero predictive weight due to being a downstream reaction of an available reaction. The predictive weight represents a probability that a reaction will occur after the execution of mandatory upstream reactions.
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pone-0110380-g001: A concept diagram of the reaction graph.In this example, the product for reaction 1 is a reactant for reaction 2. These two reactions can be connected together with reaction 2 designated as a downstream reaction for reaction 1. If the reactants for reaction 1 are available but the reactants for reaction 2 are not, reaction 2 will receive a non-zero predictive weight due to being a downstream reaction of an available reaction. The predictive weight represents a probability that a reaction will occur after the execution of mandatory upstream reactions.

Mentions: After instantiation of a model, a reaction graph is created to act as a data structure for the creation of model slicing and predictive weights. Upstream connections for a reaction r are determined by all reactions that produce the reactants for reaction r. Conversely, all downstream connections for a reaction r are determined by all reactions whose reactants are produced by reaction r. An example of a reaction graph with up and downstream reactions is demonstrated in Figure 1. In order to prevent infinite or repetitive links, a downstream connection will not be added to the same reaction graph twice, which can occur during a network cycle or converging reaction paths. Exceptions are made for up and downstream connections for a list of common cofactors that can be defined by the user.


Diagnostics for stochastic genome-scale modeling via model slicing and debugging.

Tsai KJ, Chang CH - PLoS ONE (2014)

A concept diagram of the reaction graph.In this example, the product for reaction 1 is a reactant for reaction 2. These two reactions can be connected together with reaction 2 designated as a downstream reaction for reaction 1. If the reactants for reaction 1 are available but the reactants for reaction 2 are not, reaction 2 will receive a non-zero predictive weight due to being a downstream reaction of an available reaction. The predictive weight represents a probability that a reaction will occur after the execution of mandatory upstream reactions.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219680&req=5

pone-0110380-g001: A concept diagram of the reaction graph.In this example, the product for reaction 1 is a reactant for reaction 2. These two reactions can be connected together with reaction 2 designated as a downstream reaction for reaction 1. If the reactants for reaction 1 are available but the reactants for reaction 2 are not, reaction 2 will receive a non-zero predictive weight due to being a downstream reaction of an available reaction. The predictive weight represents a probability that a reaction will occur after the execution of mandatory upstream reactions.
Mentions: After instantiation of a model, a reaction graph is created to act as a data structure for the creation of model slicing and predictive weights. Upstream connections for a reaction r are determined by all reactions that produce the reactants for reaction r. Conversely, all downstream connections for a reaction r are determined by all reactions whose reactants are produced by reaction r. An example of a reaction graph with up and downstream reactions is demonstrated in Figure 1. In order to prevent infinite or repetitive links, a downstream connection will not be added to the same reaction graph twice, which can occur during a network cycle or converging reaction paths. Exceptions are made for up and downstream connections for a list of common cofactors that can be defined by the user.

Bottom Line: In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis.The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production.We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.

ABSTRACT
Modeling of biological behavior has evolved from simple gene expression plots represented by mathematical equations to genome-scale systems biology networks. However, due to obstacles in complexity and scalability of creating genome-scale models, several biological modelers have turned to programming or scripting languages and away from modeling fundamentals. In doing so, they have traded the ability to have exchangeable, standardized model representation formats, while those that remain true to standardized model representation are faced with challenges in model complexity and analysis. We have developed a model diagnostic methodology inspired by program slicing and debugging and demonstrate the effectiveness of the methodology on a genome-scale metabolic network model published in the BioModels database. The computer-aided identification revealed specific points of interest such as reversibility of reactions, initialization of species amounts, and parameter estimation that improved a candidate cell's adenosine triphosphate production. We then compared the advantages of our methodology over other modeling techniques such as model checking and model reduction. A software application that implements the methodology is available at http://gel.ym.edu.tw/gcs/.

Show MeSH