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Coarse graining of master equations with fast and slow states
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10.1063/1.2907242
/content/aip/journal/jcp/128/15/10.1063/1.2907242
http://aip.metastore.ingenta.com/content/aip/journal/jcp/128/15/10.1063/1.2907242
View: Figures

Figures

Image of FIG. 1.
FIG. 1.

Schematic example: A particle confined in a four-well potential.

Image of FIG. 2.
FIG. 2.

Schematic example of the transition rates before and after the decimation in a master equation with four states. Here, the state on the left is a fast state and is eliminated from the description. On the right, we represent the renormalized transition in terms of the original rates. The new transition rate from to contains the original one plus an additional contribution coming from the elimination of . The transition rate from to , equal to 0 in the original graph, contains only the effect of the decimation.

Image of FIG. 3.
FIG. 3.

Particle in a discrete double well potential. and are the states corresponding to the two minima of the potential.

Image of FIG. 4.
FIG. 4.

Eigenvalues listed in decreasing order of a random walk with defect. The number of state is . Circles: No defects, the jump rate is . Squares: With probability , the sites have defects and their jump probability is . Diamonds: Random walk with defects after applying the decimation scheme to all the fast states. All the eigenvalues are real. The difference between the figure and the inset is just the axis scale. Notice in the main figure that the first 100 eigenvalues of the decimated problem follow very closely the case with the defects. In the inset, the eigenvalues corresponding to the fast states are evident; notice that their number is the same as the average number of defects .

Image of FIG. 5.
FIG. 5.

Correlation functions. The system is the same as Fig. 4 prepared in the initial state , and the probability of being in state , averaged over realization, is plotted as a function of time. The three curves are of (continuous) the system without defects, (dashed) system with defects, and (dot dashed) the decimated system.

Image of FIG. 6.
FIG. 6.

Simulations of the master equation corresponding to the enzymatic reaction of Eq. (21). In all simulations, we start with of free substrate and let the system evolve: the rate as a function of the number of molecules is evaluated by averaging over realizations of the process. The numbers of enzymes are in the top figures and in the bottom figures. The reaction rates are (left) , and (right) , . The lines in the right figures are barely visible since the points fall very close to them.

Image of FIG. 7.
FIG. 7.

Product variance as a function of time, each averaged over realizations. The four figures correspond to the same parameter choices of Fig. 6. Continuous lines correspond to simulation of the full master equation, dashed lines are simulations of the reduced processes defined by Eqs. (25)–(27).

Image of FIG. 8.
FIG. 8.

Two states ( and , black) separates by a cluster of N states (white) to be decimated.

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/content/aip/journal/jcp/128/15/10.1063/1.2907242
2008-04-18
2014-04-18
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Coarse graining of master equations with fast and slow states
http://aip.metastore.ingenta.com/content/aip/journal/jcp/128/15/10.1063/1.2907242
10.1063/1.2907242
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