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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 model slicing evaluation.In this example we have graphically represented the evaluation of model slicing with simplified upstream, downstream, and combination of upstream/downstream connections as an inductive foundation for more complex examples. The first figure (a) shows the inductive step of upstream connections, where anything upstream of species a is included in the slice. The second figure (b) show a 1st and 2nd degree downstream connection, where the 2nd degree downstream connection is removed, however the 1st is kept due to its direct influence on a. The third figure (c) represents how slicing treats downstream connections for peer branch species of a. In all figures, the light blue color represents the model information cleaved from model slicing with respect to compound a.
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pone-0110380-g003: A concept diagram of the model slicing evaluation.In this example we have graphically represented the evaluation of model slicing with simplified upstream, downstream, and combination of upstream/downstream connections as an inductive foundation for more complex examples. The first figure (a) shows the inductive step of upstream connections, where anything upstream of species a is included in the slice. The second figure (b) show a 1st and 2nd degree downstream connection, where the 2nd degree downstream connection is removed, however the 1st is kept due to its direct influence on a. The third figure (c) represents how slicing treats downstream connections for peer branch species of a. In all figures, the light blue color represents the model information cleaved from model slicing with respect to compound a.

Mentions: We used a heuristic justification approach to analyze the relevant information maintained by the model slice and assume all cofactors are readily available. Figure 3 uses simplified examples to demonstrate the logic behind the model slice. The assumption regarding the availability of cofactors is detailed in the discussion.


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

Tsai KJ, Chang CH - PLoS ONE (2014)

A concept diagram of the model slicing evaluation.In this example we have graphically represented the evaluation of model slicing with simplified upstream, downstream, and combination of upstream/downstream connections as an inductive foundation for more complex examples. The first figure (a) shows the inductive step of upstream connections, where anything upstream of species a is included in the slice. The second figure (b) show a 1st and 2nd degree downstream connection, where the 2nd degree downstream connection is removed, however the 1st is kept due to its direct influence on a. The third figure (c) represents how slicing treats downstream connections for peer branch species of a. In all figures, the light blue color represents the model information cleaved from model slicing with respect to compound a.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110380-g003: A concept diagram of the model slicing evaluation.In this example we have graphically represented the evaluation of model slicing with simplified upstream, downstream, and combination of upstream/downstream connections as an inductive foundation for more complex examples. The first figure (a) shows the inductive step of upstream connections, where anything upstream of species a is included in the slice. The second figure (b) show a 1st and 2nd degree downstream connection, where the 2nd degree downstream connection is removed, however the 1st is kept due to its direct influence on a. The third figure (c) represents how slicing treats downstream connections for peer branch species of a. In all figures, the light blue color represents the model information cleaved from model slicing with respect to compound a.
Mentions: We used a heuristic justification approach to analyze the relevant information maintained by the model slice and assume all cofactors are readily available. Figure 3 uses simplified examples to demonstrate the logic behind the model slice. The assumption regarding the availability of cofactors is detailed in the discussion.

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