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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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Graphical representations of the original model (a), Gay et al. model reduction (b), and MPP model slice (c).Regarding the Gay et al. model reduction, although MAPKK and MPK3 have no substrate or product role in a reaction, their concentration influences the rate of other reactions as described in the model. Furthermore, unlike model parameter constants, they have been defined as independent species and are thus represented on the figure as being part of the collection of species but without having any direct reaction connections. The reduction removes the intermediate species and directly connects M, MP and MPP while preserving the redundant reaction traffic between the species. As mentioned previously, removal of intermediates is a common approach for model reduction. The MPP model slice preserves the intermediate connections, but has a more definitive source and sink architecture resulting in less redundancy of M to MPP flow. Although fewer reactions occur and the slice is designed for a source/sink architecture, the MPP model slice is able to preserve the time unit speed of reactions to reach stabilization and demonstrates more similar behavior to the original model.
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pone-0110380-g010: Graphical representations of the original model (a), Gay et al. model reduction (b), and MPP model slice (c).Regarding the Gay et al. model reduction, although MAPKK and MPK3 have no substrate or product role in a reaction, their concentration influences the rate of other reactions as described in the model. Furthermore, unlike model parameter constants, they have been defined as independent species and are thus represented on the figure as being part of the collection of species but without having any direct reaction connections. The reduction removes the intermediate species and directly connects M, MP and MPP while preserving the redundant reaction traffic between the species. As mentioned previously, removal of intermediates is a common approach for model reduction. The MPP model slice preserves the intermediate connections, but has a more definitive source and sink architecture resulting in less redundancy of M to MPP flow. Although fewer reactions occur and the slice is designed for a source/sink architecture, the MPP model slice is able to preserve the time unit speed of reactions to reach stabilization and demonstrates more similar behavior to the original model.

Mentions: We have performed a comparison of the model reduction algorithm described in Gay et al. [29] versus our model slicing approach. The models used were MAPK cascade models, BioModels ID BIOMD0000000026 as the original and BIOMD0000000027 as the reduction, from the BioModels database. The authors of the model reduction had evaluated these models with their model comparison algorithm and had determined them to be reductions in the same family. Since the original model has a source/sink architecture with 500 units of M slowly transferring to MPP till stabilization, we used a model slice relative to the sink, MPP, and designated M as the source, the only species with a non-zero initial amount. A figure with a graphical representation of the three models is shown in Figure 10.


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

Tsai KJ, Chang CH - PLoS ONE (2014)

Graphical representations of the original model (a), Gay et al. model reduction (b), and MPP model slice (c).Regarding the Gay et al. model reduction, although MAPKK and MPK3 have no substrate or product role in a reaction, their concentration influences the rate of other reactions as described in the model. Furthermore, unlike model parameter constants, they have been defined as independent species and are thus represented on the figure as being part of the collection of species but without having any direct reaction connections. The reduction removes the intermediate species and directly connects M, MP and MPP while preserving the redundant reaction traffic between the species. As mentioned previously, removal of intermediates is a common approach for model reduction. The MPP model slice preserves the intermediate connections, but has a more definitive source and sink architecture resulting in less redundancy of M to MPP flow. Although fewer reactions occur and the slice is designed for a source/sink architecture, the MPP model slice is able to preserve the time unit speed of reactions to reach stabilization and demonstrates more similar behavior to the original model.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110380-g010: Graphical representations of the original model (a), Gay et al. model reduction (b), and MPP model slice (c).Regarding the Gay et al. model reduction, although MAPKK and MPK3 have no substrate or product role in a reaction, their concentration influences the rate of other reactions as described in the model. Furthermore, unlike model parameter constants, they have been defined as independent species and are thus represented on the figure as being part of the collection of species but without having any direct reaction connections. The reduction removes the intermediate species and directly connects M, MP and MPP while preserving the redundant reaction traffic between the species. As mentioned previously, removal of intermediates is a common approach for model reduction. The MPP model slice preserves the intermediate connections, but has a more definitive source and sink architecture resulting in less redundancy of M to MPP flow. Although fewer reactions occur and the slice is designed for a source/sink architecture, the MPP model slice is able to preserve the time unit speed of reactions to reach stabilization and demonstrates more similar behavior to the original model.
Mentions: We have performed a comparison of the model reduction algorithm described in Gay et al. [29] versus our model slicing approach. The models used were MAPK cascade models, BioModels ID BIOMD0000000026 as the original and BIOMD0000000027 as the reduction, from the BioModels database. The authors of the model reduction had evaluated these models with their model comparison algorithm and had determined them to be reductions in the same family. Since the original model has a source/sink architecture with 500 units of M slowly transferring to MPP till stabilization, we used a model slice relative to the sink, MPP, and designated M as the source, the only species with a non-zero initial amount. A figure with a graphical representation of the three models is shown in Figure 10.

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