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Volumn 79, Issue 5, 2009, Pages

Quantifying stochastic effects in biochemical reaction networks using partitioned leaping

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMICAL REACTION NETWORK; FUTURE APPLICATIONS; MULTISCALE; STOCHASTIC EFFECTS; STOCHASTIC METHODS; STOCHASTIC SIMULATIONS; STOCHASTICITY;

EID: 66849128260     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.79.051906     Document Type: Article
Times cited : (11)

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    • We found that significant speed ups can be achieved in the PLA simulations of the reduced repressilator model (Table 3) if we remove the ES classification (see Fig. 9). The problem lies in the iterative τ -selection procedure designed to account for the randomness of the ES reactions. In this particular case, we experienced an unexpected "classification cascade," whereby reactions classified as ES led to a reduced τ, which then led to more ES reactions (via reclassification), which further reduced τ, and so on and so forth. Removing the ES classification eliminated this problem with no major effect on the accuracy. However, this cannot be done in all cases. Removing the ES classification when simulating the full model led to numerous instances of negative populations, specifically for the species gx, { gx □ pr }, and { gx □ pr □ pr }, which can only have populations of zero or unity. These required costly reversals that significantly increased the run time. Further investigation of this issue is warranted and will be undertaken in the near future. Also note that all results reported in Figs. 7 8 were performed with the ES classification included.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.