title: SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks.
abstract: BACKGROUND
  The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment.
  
  
  RESULTS
  We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired.
  
  
  CONCLUSIONS
  The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios.
authors.affiliation: Systems Biology Research Group, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, CA, USA. andraeger@eng.ucsd.edu.|Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076, Germany. andraeger@eng.ucsd.edu.
authors.forename: Andreas
authors.initials: A
authors.lastname: Dräger
authors.affiliation: Systems Biology Research Group, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, CA, USA. dczielin@eng.ucsd.edu.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Zielinski
authors.affiliation: Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076, Germany. roland.keller@gmx.de.
authors.forename: Roland
authors.initials: R
authors.lastname: Keller
authors.affiliation: Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076, Germany. matthias.rall@student.uni-tuebingen.de.
authors.forename: Matthias
authors.initials: M
authors.lastname: Rall
authors.affiliation: Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076, Germany. johannes.eichner@gmail.de.
authors.forename: Johannes
authors.initials: J
authors.lastname: Eichner
authors.affiliation: Systems Biology Research Group, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, CA, USA. palsson@ucsd.edu.|Novo Nordisk Foundation Center for Biosustainability, Kogle Allé 6, Hørsholm, 2970, Denmark. palsson@ucsd.edu.
authors.forename: Bernhard O
authors.initials: BO
authors.lastname: Palsson
authors.affiliation: Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Sand 1, Tübingen, 72076, Germany. andreas.zell@uni-tuebingen.de.
authors.forename: Andreas
authors.initials: A
authors.lastname: Zell
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007700:Kinetics; D053858:Metabolic Networks and Pathways / Q000502:physiology; D008954:Models, Biological; D012984:Software; D049490:Systems Biology / Q000379:methods*