| Autor: |
WITZIG, JAKOB, BECKENBACH, ISABEL, EIFLER, LEON, FACKELDEY, KONSTANTIN, GLEIXNER, AMBROS, GREVER, ANDREAS, WEBER, MARCUS |
| Předmět: |
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| Zdroj: |
Multiscale Modeling & Simulation; 2018, Vol. 16 Issue 1, p248-265, 18p |
| Abstrakt: |
In this paper, we present a new, optimization-based method to exhibit cyclic behavior in nonreversible stochastic processes. While our method is general, it is strongly motivated by discrete simulations of ordinary differential equations representing nonreversible biological processes, in particular, molecular simulations. Here, the discrete time steps of the simulation are often very small compared to the time scale of interest, i.e., of the whole process. In this setting, the detection of a global cyclic behavior of the process becomes difficult because transitions between individual states may appear almost reversible on the small time scale of the simulation. We address this difficulty using a mixed-integer programming model that allows us to compute a cycle of clusters with maximum net ow, i.e., large forward and small backward probability. For a synthetic genetic regulatory network consisting of a ring oscillator with three genes, we show that this approach can detect cycles that have a productivity one magnitude larger than classical spectral analysis methods. Our method applies to general nonequilibrium steady state systems such as catalytic reactions, for which the objective value computes the effectiveness of the catalyst. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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