کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6595353 | 458515 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks
ترجمه فارسی عنوان
برآورد حداکثر احتمال درستنمایی شبکه های واکنش بیوشیمیایی منوموکولیک تصادفی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
مهندسی شیمی (عمومی)
چکیده انگلیسی
A sparse parameter matrix estimation method is proposed for identifying a stochastic monomolecular biochemical reaction network system. Identification of a reaction network can be achieved by estimating a sparse parameter matrix containing the reaction network structure and kinetics information. Stochastic dynamics of a biochemical reaction network system is usually modeled by a chemical master equation (CME) describing the time evolution of probability distributions for all possible states. This paper considers closed monomolecular reaction systems for which an exact analytical solution of the corresponding chemical master equation can be derived. The estimation method presented in this paper incorporates the closed-form solution into a regularized maximum likelihood estimation (MLE) for which model complexity is penalized. A simulation result is provided to verify performance improvement of regularized MLE over least-square estimation (LSE), which is based on a deterministic mass-average model, in the case of a small population size.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 90, 12 July 2016, Pages 111-120
Journal: Computers & Chemical Engineering - Volume 90, 12 July 2016, Pages 111-120
نویسندگان
Hong Jang, Kwang-Ki K. Kim, Richard D. Braatz, R. Bhushan Gopaluni, Jay H. Lee,