title: Identifying latent dynamic components in biological systems.
abstract: In computational systems biology, the general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. In the past, many such models have been formulated for different biological applications. The authors consider the scenario where a given model fails to predict a set of observations with acceptable accuracy and ask the question whether this is because of the model lacking important external regulations. Real-world examples for such entities range from microRNAs to metabolic fluxes. To improve the prediction, they propose an algorithm to systematically extend the network by an additional latent dynamic variable which has an exogenous effect on the considered network. This variable's time course and influence on the other species is estimated in a two-step procedure involving spline approximation, maximum-likelihood estimation and model selection. Simulation studies show that such a hidden influence can successfully be inferred. The method is also applied to a signalling pathway model where they analyse real data and obtain promising results. Furthermore, the technique can be employed to detect incomplete network structures.
authors.affiliation: Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany.
authors.forename: Ivan
authors.initials: I
authors.lastname: Kondofersky
authors.affiliation: Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany.
authors.forename: Christiane
authors.initials: C
authors.lastname: Fuchs
authors.affiliation: Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany. fabian.theis@helmholtz-muenchen.de.
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
mesh_terms: D000465:Algorithms; D053614:Janus Kinase 2; D008954:Models, Biological; D015233:Models, Statistical; D050799:STAT5 Transcription Factor; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
chemical_list: D050799:STAT5 Transcription Factor; D053614:Janus Kinase 2