TY  - JOUR
A1  - Omranian, Nooshin
A1  - Kleessen, Sabrina
A1  - Tohge, Takayuki
A1  - Klie, Sebastian
A1  - Basler, Georg
A1  - Müller-Röber, Bernd
A1  - Fernie, Alisdair R.
A1  - Nikoloski, Zoran
T1  - Differential metabolic and coexpression networks of plant metabolism
JF  - Trends in plant science
N2  - Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used.
KW  - plant specialized metabolism
KW  - metabolic networks
KW  - gene coexpression
KW  - differential network analysis
Y1  - 2015
U6  - https://doi.org/10.1016/j.tplants.2015.02.002
SN  - 1360-1385
VL  - 20
IS  - 5
SP  - 266
EP  - 268
PB  - Elsevier
CY  - London
ER  - 
TY  - JOUR
A1  - Basler, Georg
A1  - Fernie, Alisdair R.
A1  - Nikoloski, Zoran
T1  - Advances in metabolic flux analysis toward genome-scale profiling of higher organisms
JF  - Bioscience reports : communications and reviews in molecular and cellular biology
N2  - Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.
Y1  - 2018
U6  - https://doi.org/10.1042/BSR20170224
SN  - 0144-8463
SN  - 1573-4935
VL  - 38
PB  - Portland Press (London)
CY  - London
ER  -