Identifying latent dynamic components in biological systems. Issue 5 (1st October 2015)

• Record Type: Journal Article
• Authors: Kondofersky, Ivan; Fuchs, Christiane; Theis, Fabian J.
• Is Part Of: IET systems biology. Volume 9:Issue 5(2015)
• Page Start: 193
• Page End: 203
• Publication Date: 2015-10-01
• Subjects: biology computing -- RNA -- splines (mathematics) -- maximum likelihood estimation -- approximation theory -- biochemistry; latent dynamic components -- biological systems -- computational system biology -- regulatory models -- multivariate readouts -- biological applications -- external regulations -- real‐world examples -- microRNA -- metabolic fluxes -- latent dynamic variables -- variable time course -- two‐step procedure -- spline approximation -- maximum‐likelihood estimation -- model selection -- signalling pathway model -- real data -- incomplete network structures; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2014.0013
• Languages: English
• ISSNs: 1751-8849

Abstract

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.