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Exact hybrid particle/population simulation of rule-based models of biochemical systems.

Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR - PLoS Comput. Biol. (2014)

Bottom Line: Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches.The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles.Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

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Related in: MedlinePlus

HPP performance analyses for various lumping thresholds at cell fraction .(A) TLBR; (B) Actin; (C) ; (D) EGFR. In all plots, threshold values for different lumping sets are shown on the x-axis. For TLBR and Actin, some thresholds yield the same set of population species as larger thresholds and are thus omitted from the figures. For TLBR, results for thresholds  are omitted due to impractically large partial networks in those cases. Results for NFsim (‘NF’) and the hand-picked lumping sets from Figs. 5–8 (‘HPP’) are shown in all plots for comparison. Error bars show standard error (three samples).
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pcbi-1003544-g009: HPP performance analyses for various lumping thresholds at cell fraction .(A) TLBR; (B) Actin; (C) ; (D) EGFR. In all plots, threshold values for different lumping sets are shown on the x-axis. For TLBR and Actin, some thresholds yield the same set of population species as larger thresholds and are thus omitted from the figures. For TLBR, results for thresholds are omitted due to impractically large partial networks in those cases. Results for NFsim (‘NF’) and the hand-picked lumping sets from Figs. 5–8 (‘HPP’) are shown in all plots for comparison. Error bars show standard error (three samples).

Mentions: In Fig. 9, we show peak memory use and CPU run times for HPP simulations of each model at each lumping set considered. In general, these results illustrate the success of the hand-picked lumping sets, which produced memory savings close to the optimal in most cases. There was, however, some room for improvement in the model (Fig. 9C). This is because the fourth and fifth most populated species for this model were complexes comprised of five molecular subunits (see Dataset S1). Since we did not anticipate this result, these high-population species were not included in the hand-picked lumping set. The majority of the memory savings seen in Fig. 9C for thresholds are due to lumping of these species. Thus, our results also illustrate the value of using a more systematic approach to selecting population species in some cases.


Exact hybrid particle/population simulation of rule-based models of biochemical systems.

Hogg JS, Harris LA, Stover LJ, Nair NS, Faeder JR - PLoS Comput. Biol. (2014)

HPP performance analyses for various lumping thresholds at cell fraction .(A) TLBR; (B) Actin; (C) ; (D) EGFR. In all plots, threshold values for different lumping sets are shown on the x-axis. For TLBR and Actin, some thresholds yield the same set of population species as larger thresholds and are thus omitted from the figures. For TLBR, results for thresholds  are omitted due to impractically large partial networks in those cases. Results for NFsim (‘NF’) and the hand-picked lumping sets from Figs. 5–8 (‘HPP’) are shown in all plots for comparison. Error bars show standard error (three samples).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3974646&req=5

pcbi-1003544-g009: HPP performance analyses for various lumping thresholds at cell fraction .(A) TLBR; (B) Actin; (C) ; (D) EGFR. In all plots, threshold values for different lumping sets are shown on the x-axis. For TLBR and Actin, some thresholds yield the same set of population species as larger thresholds and are thus omitted from the figures. For TLBR, results for thresholds are omitted due to impractically large partial networks in those cases. Results for NFsim (‘NF’) and the hand-picked lumping sets from Figs. 5–8 (‘HPP’) are shown in all plots for comparison. Error bars show standard error (three samples).
Mentions: In Fig. 9, we show peak memory use and CPU run times for HPP simulations of each model at each lumping set considered. In general, these results illustrate the success of the hand-picked lumping sets, which produced memory savings close to the optimal in most cases. There was, however, some room for improvement in the model (Fig. 9C). This is because the fourth and fifth most populated species for this model were complexes comprised of five molecular subunits (see Dataset S1). Since we did not anticipate this result, these high-population species were not included in the hand-picked lumping set. The majority of the memory savings seen in Fig. 9C for thresholds are due to lumping of these species. Thus, our results also illustrate the value of using a more systematic approach to selecting population species in some cases.

Bottom Line: Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches.The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles.Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

View Article: PubMed Central - PubMed

Affiliation: Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.

ABSTRACT
Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

Show MeSH
Related in: MedlinePlus