@misc{OmranianKleessenTohgeetal.2015,
  author    = {Omranian, Nooshin and Kleessen, Sabrina and Tohge, Takayuki and Klie, Sebastian and Basler, Georg and M{\"u}ller-R{\"o}ber, Bernd and Fernie, Alisdair R. and Nikoloski, Zoran},
  title     = {Differential metabolic and coexpression networks of plant metabolism},
  series = {Trends in plant science},
  volume    = {20},
  journal   = {Trends in plant science},
  number    = {5},
  publisher = {Elsevier},
  address   = {London},
  issn      = {1360-1385},
  doi       = {10.1016/j.tplants.2015.02.002},
  pages     = {266 -- 268},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@misc{BaslerFernieNikoloski2018,
  author    = {Basler, Georg and Fernie, Alisdair R. and Nikoloski, Zoran},
  title     = {Advances in metabolic flux analysis toward genome-scale profiling of higher organisms},
  series = {Bioscience reports : communications and reviews in molecular and cellular biology},
  volume    = {38},
  journal   = {Bioscience reports : communications and reviews in molecular and cellular biology},
  publisher = {Portland Press (London)},
  address   = {London},
  issn      = {0144-8463},
  doi       = {10.1042/BSR20170224},
  pages     = {11},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}