Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.
Description
Systems analysis of cellular networks under uncertainty - ScienceDirect
%0 Journal Article
%1 Kaltenbach2009Systems
%A Kaltenbach, Hans-Michael
%A Dimopoulos, Sotiris
%A Stelling, Jörg
%D 2009
%J FEBS Letters
%K modelling uncertainty
%N 24
%P 3923 - 3930
%R https://doi.org/10.1016/j.febslet.2009.10.074
%T Systems analysis of cellular networks under uncertainty
%U http://www.sciencedirect.com/science/article/pii/S0014579309008667
%V 583
%X Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.
@article{Kaltenbach2009Systems,
abstract = {Besides the often-quoted complexity of cellular networks, the prevalence of uncertainties about components, interactions, and their quantitative features provides a largely underestimated hallmark of current systems biology. This uncertainty impedes the development of mechanistic mathematical models to achieve a true systems-level understanding. However, there is increasing evidence that theoretical approaches from diverse scientific domains can extract relevant biological knowledge efficiently, even from poorly characterized biological systems. As a common denominator, the methods focus on structural, rather than more detailed, kinetic network properties. A deeper understanding, better scaling, and the ability to combine the approaches pose formidable challenges for future theory developments.},
added-at = {2019-06-12T08:59:42.000+0200},
author = {Kaltenbach, Hans-Michael and Dimopoulos, Sotiris and Stelling, Jörg},
biburl = {https://www.bibsonomy.org/bibtex/21bddf9c6f28e0e18e8bd43e736e8ed5e/karthikraman},
description = {Systems analysis of cellular networks under uncertainty - ScienceDirect},
doi = {https://doi.org/10.1016/j.febslet.2009.10.074},
interhash = {b5d3ac1cb22709c0a11c252a0d27aaa5},
intrahash = {1bddf9c6f28e0e18e8bd43e736e8ed5e},
issn = {0014-5793},
journal = {FEBS Letters},
keywords = {modelling uncertainty},
note = {Systems Biology - Nobel Symposium 146},
number = 24,
pages = {3923 - 3930},
timestamp = {2019-06-12T08:59:42.000+0200},
title = {Systems analysis of cellular networks under uncertainty},
url = {http://www.sciencedirect.com/science/article/pii/S0014579309008667},
volume = 583,
year = 2009
}