@inbook{Draeger2013b,
chapter = {{Parameter Estimation, Metabolic Network Modeling}},
pages = {1627--1631},
title = {{Encyclopedia of Systems Biology}},
publisher = {Springer-Verlag},
year = {2013},
editor = {Dubitzky, Werner and Wolkenhauer, Olaf and Yokota, Hiroki and Cho,
Kwang-Hyun},
author = {Dr\"ager, Andreas and Planatscher, Hannes},
address = {Springer New York Heidelberg Dorodrecht London},
month = aug,
abstract = {Metabolic networks can mathematically be modeled as a differential
equation system. These models consist of a stoichiometric matrix,
a modulation matrix, and a vector of reaction rates. The mathematical
structure of the differential equation system is usually assumed
to be known, its parameters, however, in general are not. Here the
term ``parameter'' denotes all quantities within the model whose
values are uncertain or difficult to obtain experimentally. In the
most common case, these comprise kinetic constants, concentrations
of external metabolites, enzyme concentrations or activities, and
compartment sizes. The parameter estimation problem aims at estimating
meaningful values for these targets by trying to coincide given experimental
data with the predictions of the model. Bayesian methods, maximum
likelihood estimates, and (biologically inspired) optimization procedures
are frequently used estimation approaches. Experimental data usually
contain a time-course or steady state of concentration values of
the reacting species (compounds) within the network. Before estimating
their values, a parameter identifiability analysis should be conducted.
Estimated values should be analyzed regarding their thermodynamic
plausibility.},
doi = {10.1007/978-1-4419-9863-7_1174},
isbn = {978-1441998644},
keywords = {Model calibration, Model fitting, Parameter fitting, Parameter identification,
Regression},
language = {eng},
url = {http://link.springer.com/referenceworkentry/10.1007/978-1-4419-9863-7_1174}
}