Time series data can now be routinely collected for biochemical reaction
pathways, and recently, several methods have been proposed to infer
reaction mechanisms for metabolic pathways and networks. In this
paper we provide a survey of mathematical techniques for determining
reaction mechanisms for time series data on the concentration or
abundance of different reacting components, with little prior information
about the pathways involved.
%0 Journal Article
%1 Cram_2004_77
%A Crampin, E. J.
%A Schnell, S.
%A McSharry, P. E.
%D 2004
%J Prog. Biophys. Mol. Biol.
%K 15261526 Algorithms, Analysis, Animals, Biochemical Biological, Cell Chemical, Comparative Computer Computer-Assisted, Dynamics, Gov't, Humans, Linear Models, Non-U.S. Nonlinear Numerical Phenomena, Physiology, Research Signal Simulation, Study, Support, Systems Theory, Transduction,
%N 1
%P 77--112
%R 10.1016/j.pbiomolbio.2004.04.002
%T Mathematical and computational techniques to deduce complex biochemical
reaction mechanisms.
%U http://dx.doi.org/10.1016/j.pbiomolbio.2004.04.002
%V 86
%X Time series data can now be routinely collected for biochemical reaction
pathways, and recently, several methods have been proposed to infer
reaction mechanisms for metabolic pathways and networks. In this
paper we provide a survey of mathematical techniques for determining
reaction mechanisms for time series data on the concentration or
abundance of different reacting components, with little prior information
about the pathways involved.
@article{Cram_2004_77,
abstract = {Time series data can now be routinely collected for biochemical reaction
pathways, and recently, several methods have been proposed to infer
reaction mechanisms for metabolic pathways and networks. In this
paper we provide a survey of mathematical techniques for determining
reaction mechanisms for time series data on the concentration or
abundance of different reacting components, with little prior information
about the pathways involved.},
added-at = {2009-06-03T11:20:58.000+0200},
author = {Crampin, E. J. and Schnell, S. and McSharry, P. E.},
biburl = {https://www.bibsonomy.org/bibtex/2abf500ed5e1edf9b9975821f01635dbe/hake},
description = {The whole bibliography file I use.},
doi = {10.1016/j.pbiomolbio.2004.04.002},
file = {Cram_2004_77.pdf:Cram_2004_77.pdf:PDF},
interhash = {7bbe527031645530223683c34852cf31},
intrahash = {abf500ed5e1edf9b9975821f01635dbe},
journal = {Prog. Biophys. Mol. Biol.},
keywords = {15261526 Algorithms, Analysis, Animals, Biochemical Biological, Cell Chemical, Comparative Computer Computer-Assisted, Dynamics, Gov't, Humans, Linear Models, Non-U.S. Nonlinear Numerical Phenomena, Physiology, Research Signal Simulation, Study, Support, Systems Theory, Transduction,},
month = Sep,
number = 1,
pages = {77--112},
pii = {S007961070400046X},
pmid = {15261526},
timestamp = {2009-06-03T11:21:09.000+0200},
title = {Mathematical and computational techniques to deduce complex biochemical
reaction mechanisms.},
url = {http://dx.doi.org/10.1016/j.pbiomolbio.2004.04.002},
volume = 86,
year = 2004
}