TY - JOUR
T1 - State-space reduction and equivalence class sampling for a molecular self-assembly model
AU - Packwood, Daniel M.
AU - Han, Patrick
AU - Hitosugi, Taro
PY - 2016/7/20
Y1 - 2016/7/20
N2 - Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving ‘target information’ from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of aMarkov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
AB - Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving ‘target information’ from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of aMarkov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
KW - Markov chain Monte Carlo
KW - Model reduction
KW - Self-assembly
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U2 - 10.1098/rsos.150681
DO - 10.1098/rsos.150681
M3 - Article
AN - SCOPUS:84978904051
VL - 3
SP - 20
JO - Royal Society Open Science
JF - Royal Society Open Science
SN - 2054-5703
IS - 7
M1 - 18
ER -