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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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The algorithm for predictive weights.
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pone-0110380-g004: The algorithm for predictive weights.

Mentions: The probability that a reaction occurs at a given time is determined by the Gillespie algorithm implemented in the simulation engine. The algorithm takes into account reactant availability and the reaction's kinetic law, specified in the model, in order to compute a reaction weight, which is correlated with the probability. In several instances, especially that of long pathway networks, several reactants may not yet be available until the execution of mandatory upstream reactions. However, a calculation is still useful to determine the probability of reaching a certain reaction as the species created may be of central interest to the network. To address this, we have implemented an algorithm based on the forward algorithm for hidden Markov models which calculates a state sequence probability by iterative probability aggregation. The algorithm uses the same reaction graph model slice to calculate the probability of a reaction occurring when none of the reaction's reactants are immediately available. The algorithm looks at the first instance of reaction availability in upstream reactions. This “predictive weight” is calculated dynamically at each simulation time point using the algorithm described in Figure 4.


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

Tsai KJ, Chang CH - PLoS ONE (2014)

The algorithm for predictive weights.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0110380-g004: The algorithm for predictive weights.
Mentions: The probability that a reaction occurs at a given time is determined by the Gillespie algorithm implemented in the simulation engine. The algorithm takes into account reactant availability and the reaction's kinetic law, specified in the model, in order to compute a reaction weight, which is correlated with the probability. In several instances, especially that of long pathway networks, several reactants may not yet be available until the execution of mandatory upstream reactions. However, a calculation is still useful to determine the probability of reaching a certain reaction as the species created may be of central interest to the network. To address this, we have implemented an algorithm based on the forward algorithm for hidden Markov models which calculates a state sequence probability by iterative probability aggregation. The algorithm uses the same reaction graph model slice to calculate the probability of a reaction occurring when none of the reaction's reactants are immediately available. The algorithm looks at the first instance of reaction availability in upstream reactions. This “predictive weight” is calculated dynamically at each simulation time point using the algorithm described in Figure 4.

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