论文信息 - Network Reconstruction with Realistic Models

Network Reconstruction with Realistic Models

We extend a recently proposed gradient-matching method for inferring interactions in complex systems described by differential equations in various respects: improved gradient inference, evaluation of the influence of the prior on kinetic parameters, comparative evaluation of two model selection paradigms: marginal likelihood versus DIC (divergence information criterion), comparative evaluation of different numerical procedures for computing the marginal likelihood, extension of the methodology from protein phosphorylation to transcriptional regulation, based on a realistic simulation of the underlying molecular processes with Markov jump processes.

Marco Grzegorczyk | Dirk Husmeier | Andrej Aderhold

[1] Takeshi Mizuno,et al. Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model , 2010, Molecular Systems Biology.

[2] Joe W. Gray,et al. Causal network inference using biochemical kinetics , 2014, Bioinform..

[3] M. Grzegorczyk,et al. Statistical inference of regulatory networks for circadian regulation , 2014, Statistical applications in genetics and molecular biology.