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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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Various network graphs created from production counts in the application's watch panel.The production counts were averaged over multiple simulations. In the initial slice simulation with arbitrary reaction kinetic law values (a), 100 glucose units conserve semi-efficiently through the glycolysis pathway until the production of 2-phosphoglycerate, which only produces 16.2 units on average. The representation in (b) shows the results after the adjustment of the R_PGM reaction. Production amount of downstream species can sometimes be greater than the production of their upstream counterparts due to other species and reactions not listed in the glycolysis pathway that are nonetheless part of the slice. These can be cyclic paths that cause high production counts for those specific intermediates but do not cause a high overall amount. The initial simulation of the whole model with the adjusted slice (c) demonstrated glycolysis bottlenecks towards the initial ATP consuming reactions, however, after adjusting for competing ATP consuming reactions that were not incorporated into the reaction graph we were able to observe adequate production (d). The debugging information for the network representations were obtained from the application's watch panel and are not typically observable in traditional concentration plots.
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pone-0110380-g009: Various network graphs created from production counts in the application's watch panel.The production counts were averaged over multiple simulations. In the initial slice simulation with arbitrary reaction kinetic law values (a), 100 glucose units conserve semi-efficiently through the glycolysis pathway until the production of 2-phosphoglycerate, which only produces 16.2 units on average. The representation in (b) shows the results after the adjustment of the R_PGM reaction. Production amount of downstream species can sometimes be greater than the production of their upstream counterparts due to other species and reactions not listed in the glycolysis pathway that are nonetheless part of the slice. These can be cyclic paths that cause high production counts for those specific intermediates but do not cause a high overall amount. The initial simulation of the whole model with the adjusted slice (c) demonstrated glycolysis bottlenecks towards the initial ATP consuming reactions, however, after adjusting for competing ATP consuming reactions that were not incorporated into the reaction graph we were able to observe adequate production (d). The debugging information for the network representations were obtained from the application's watch panel and are not typically observable in traditional concentration plots.

Mentions: We were able to observe a lack of ATP due to the low production of initial glycolysis species in the application's watch panel. Since ATP is required for ATP production, we lowered the kinetic law values of ATP consuming reactions. We identified 4 ATP consuming reactions (R_AP4AS, R_NADK, R_PPKr, R_PPK2r) that were in the original model and not accounted for in the model slice. These reactions could be executed with the initial reactants of the model slice during predictive weight testing. The results in species production for all model simulations are represented in Figure 9.


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

Tsai KJ, Chang CH - PLoS ONE (2014)

Various network graphs created from production counts in the application's watch panel.The production counts were averaged over multiple simulations. In the initial slice simulation with arbitrary reaction kinetic law values (a), 100 glucose units conserve semi-efficiently through the glycolysis pathway until the production of 2-phosphoglycerate, which only produces 16.2 units on average. The representation in (b) shows the results after the adjustment of the R_PGM reaction. Production amount of downstream species can sometimes be greater than the production of their upstream counterparts due to other species and reactions not listed in the glycolysis pathway that are nonetheless part of the slice. These can be cyclic paths that cause high production counts for those specific intermediates but do not cause a high overall amount. The initial simulation of the whole model with the adjusted slice (c) demonstrated glycolysis bottlenecks towards the initial ATP consuming reactions, however, after adjusting for competing ATP consuming reactions that were not incorporated into the reaction graph we were able to observe adequate production (d). The debugging information for the network representations were obtained from the application's watch panel and are not typically observable in traditional concentration plots.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110380-g009: Various network graphs created from production counts in the application's watch panel.The production counts were averaged over multiple simulations. In the initial slice simulation with arbitrary reaction kinetic law values (a), 100 glucose units conserve semi-efficiently through the glycolysis pathway until the production of 2-phosphoglycerate, which only produces 16.2 units on average. The representation in (b) shows the results after the adjustment of the R_PGM reaction. Production amount of downstream species can sometimes be greater than the production of their upstream counterparts due to other species and reactions not listed in the glycolysis pathway that are nonetheless part of the slice. These can be cyclic paths that cause high production counts for those specific intermediates but do not cause a high overall amount. The initial simulation of the whole model with the adjusted slice (c) demonstrated glycolysis bottlenecks towards the initial ATP consuming reactions, however, after adjusting for competing ATP consuming reactions that were not incorporated into the reaction graph we were able to observe adequate production (d). The debugging information for the network representations were obtained from the application's watch panel and are not typically observable in traditional concentration plots.
Mentions: We were able to observe a lack of ATP due to the low production of initial glycolysis species in the application's watch panel. Since ATP is required for ATP production, we lowered the kinetic law values of ATP consuming reactions. We identified 4 ATP consuming reactions (R_AP4AS, R_NADK, R_PPKr, R_PPK2r) that were in the original model and not accounted for in the model slice. These reactions could be executed with the initial reactants of the model slice during predictive weight testing. The results in species production for all model simulations are represented in Figure 9.

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