@incollection{Mostolizadeh2019a,
  author    = {Mostolizadeh, Reihaneh and Dr{\"a}ger, Andreas and Jamshidi, Neema},
  title     = {{Insights into Dynamic Network States Using Metabolomic Data}}, 
  booktitle = {{High-Throughput Metabolomics}},
  publisher = {Humana},
  editor    = {D'Alessandro, Angelo},
  pages     = {243--258},
  year      = {2019},
  chapter   = {15},
  month     = may,
  volume    = {1978},
  series    = {Methods in Molecular Biology (MIMB)},
  abstract  = {Metabolomic data is the youngest of the high-throughput data types;
        however, it is potentially one of the most informative, as it provides a 
        direct, quantitative biochemical phenotype. There are a number of ways in 
        which metabolomic data can be analyzed in systems biology; however, the 
        thermodynamic and kinetic relevance of these data cannot be overstated.
        Genome-scale metabolic network reconstructions provide a natural context
        to incorporate metabolomic data in order to provide insight into the
        condition-specific kinetic characteristics of metabolic networks. Herein
        we discuss how metabolomic data can be incorporated into constraint-based
        models in a flexible framework that enables scaling from small pathways
        to cell-scale models, while being able to accommodate coarse-grained
        to more detailed, allosteric interactions, all using the well-known
        principle of mass action.},
  address   = {New York, NY},
  doi       = {10.1007/978-1-4939-9236-2_15},
  isbn      = {978-1-4939-9236-2},
  keywords  = {Systems biology, Dynamic network states, Metabolomics},
  language  = {en},
  url       = {https://link.springer.com/protocol/10.1007/978-1-4939-9236-2_15},
}