title: Topological augmentation to infer hidden processes in biological systems.
abstract: BACKGROUND
  A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data.
  
  
  RESULTS
  Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations.
  
  
  METHODS
  Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index
authors.affiliation: Department of Biosystems Science and Engineering/Swiss Institute of Bioinformatics, ETH Zurich, 4058 Basel, Switzerland, Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, 8093 Zurich, Switzerland, Institute of Evolutionary Biology and Environmental Studies/Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland, Institute for Molecular Systems Biology, 8093 Zurich, Switzerland and The Santa Fe Institute, Santa Fe, 87501 New Mexico, USA.
authors.forename: Mikael
authors.initials: M
authors.lastname: Sunnåker
authors.forename: Elias
authors.initials: E
authors.lastname: Zamora-Sillero
authors.forename: Adrián
authors.initials: A
authors.lastname: López García de Lomana
authors.forename: Florian
authors.initials: F
authors.lastname: Rudroff
authors.forename: Uwe
authors.initials: U
authors.lastname: Sauer
authors.forename: Joerg
authors.initials: J
authors.lastname: Stelling
authors.forename: Andreas
authors.initials: A
authors.lastname: Wagner
mesh_terms: D000465:Algorithms; D026923:Amino Acid Transport Systems, Basic / Q000378:metabolism; D001499:Bayes Theorem; D041981:Gastrointestinal Tract / Q000187:drug effects; D005973:Glutamine / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000008:administration & dosage; D010599:Pharmacokinetics; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D049490:Systems Biology
chemical_list: D026923:Amino Acid Transport Systems, Basic; D004364:Pharmaceutical Preparations; D005973:Glutamine; C051432:glutamine permease