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• W2090896051 startingPage "1270" @default.
• W2090896051 abstract "Advances in biotechnology and computer science are providing the possibility to construct mathematical models for complex biological networks and systematically understand their properties. Traditional network identification approaches, however, cannot accurately recover the model parameters from the noisy laboratory measurements. This article introduces the concept of optimal identification (OI), which utilizes a global inversion algorithm to extract the full distribution of parameters consistent with the laboratory data. In addition, OI integrates suitable computational algorithms with experimental capabilities in a closed loop fashion to maximally reduce the breadth of the extracted parameter distribution. The closed loop OI procedure seeks out the optimal set of control chemical fluxes and data observations that actively filter out experimental noise and enhance the sensitivity to the desired parameters. In this fashion, the highest quality network parameters can be attained from inverting the tailored laboratory data. The operation of OI is illustrated by identifying a simulated tRNA proofreading mechanism, in which OI provides superior solutions for all the rate constants compared with suboptimal and nonoptimal methods." @default.
• W2090896051 created "2016-06-24" @default.
• W2090896051 creator A5003764862 @default.
• W2090896051 creator A5019365403 @default.
• W2090896051 date "2004-03-01" @default.
• W2090896051 modified "2023-10-12" @default.
• W2090896051 title "Optimal Identification of Biochemical Reaction Networks" @default.
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• W2090896051 doi "https://doi.org/10.1016/s0006-3495(04)74201-0" @default.
• W2090896051 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/1303968" @default.
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• W2090896051 hasPublicationYear "2004" @default.
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