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.
journal_name
Biophys Jjournal_title
Biophysical journalauthors
Feng XJ,Rabitz Hdoi
10.1016/S0006-3495(04)74201-0subject
Has Abstractpub_date
2004-03-01 00:00:00pages
1270-81issue
3eissn
0006-3495issn
1542-0086pii
S0006-3495(04)74201-0journal_volume
86pub_type
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