Identifying latent dynamic components in biological systems
Kondofersky I, Fuchs C, Theis FJ (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8130 LNBI
Pages Range: 259-260
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: AUT
ISBN: 9783642407079
Abstract
In systems biology, a general aim is to derive regulatory models from multivariate readouts, thereby generating predictions for novel experiments. However any model only approximates reality, leaving out details or regulations. These may be completely new entities such as microRNAs or metabolic fluxes which have a substantial contribution to the network structure and can be used to improve the model describing the regulatory system and thus produce meaningful results. In this poster, we consider the case where a given model fails to predict a set of observations with acceptable accuracy. In order to refine the model, we propose an algorithm for inferring additional upstream species that improve the prediction as well as the model fit and at the same time are subject to the model dynamics. In the studied context of ODE-based models, this means systematically extending the network by an additional latent dynamic variable. This variable is modeled by splines in order to easily access derivatives; the influence vector of the variable onto the species is then estimated from the data via model selection. © Springer-Verlag 2013.
Involved external institutions
Germany (DE)How to cite
APA:
Kondofersky, I., Fuchs, C., & Theis, F.J. (2013). Identifying latent dynamic components in biological systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 259-260). AUT.
MLA:
Kondofersky, Ivan, Christiane Fuchs, and Fabian J. Theis. "Identifying latent dynamic components in biological systems." Proceedings of the 11th International Conference on Computational Methods in Systems Biology, CMSB 2013, AUT 2013. 259-260.
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