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M. Sunnaker, E. Zamora-Sillero, A. Garcia de Lomana, F. Rudroff, U. Sauer, J. Stelling, A. Wagner:
"Topological augmentation to infer hidden processes in biological systems";
Bioinformatics, 2 (2014), 30; 221 - 227.

Motivation:
A common problem in understanding a biochemical
system is to infer its correct structure or topology. This topology con-
sists of all relevant state variables-usually molecules and their inter-
actions. 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 pro-
cess 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 semiauto-
matic 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.
Availability and implementation:
Matlab code and examples are
available at: http://www.csb.ethz.ch/tools/index.

http://dx.doi.org/10.1093/bioinformatics/btt638

http://publik.tuwien.ac.at/files/PubDat_236651.pdf