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research
Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems
Authors
A Fujioka
A Slepoy
+77 more
AB Bortz
AC Hindmarsh
AF Overbeck
B Goldstein
B Goldstein
B Goldstein
BB Aldridge
BR Angermann
C Maus
CI Lacayo
CV Falkenberg
DC Wylie
DG Vlachos
DT Gillespie
DT Gillespie
DT Gillespie
DT Gillespie
DT Gillespie
EC Stites
EL Haseltine
F Wilcoxon
G Grünert
H Conzelmann
H Salis
H Salis
HB Mann
J Colvin
J Colvin
J Faeder
J Feret
J Roland
J Yang
J Yang
James R. Faeder
JM McCollum
Jorg Stelling
JR Faeder
JR Faeder
JR Karr
Justin S. Hogg
K Burrage
K Pearson
K Takahashi
K Vasudeva
KD Stone
L Lok
LA Harris
Leonard A. Harris
Lori J. Stover
M Creamer
M Griffith
M Lis
M Tomita
MA Gibson
MI Monine
ML Blinov
ML Blinov
ML Blinov
ML Blinov
MW Sneddon
Niketh S. Nair
NM Borisov
NM Borisov
NM Borisov
T Fricke
TD Pollard
TP Schulze
TR Kiehl
U Moran
V Danos
V Danos
V Danos
WS Hlavacek
WS Hlavacek
Y Cao
Y Cao
Z Liu
Publication date
1 January 2014
Publisher
'Public Library of Science (PLoS)'
Doi
Cite
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on
arXiv
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. © 2014 Hogg et al
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