Motivation: Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals. Results: A new method predicts the dynamic state of protein complexes in a cell, with protein expression inferred from transcription profile time courses and protein complexes inferred by joint analysis of protein co-expression and protein-protein interaction maps. Two algorithmic advances are presented: a new method, DHAC (Dynamical Hierarchical Agglomerative Clustering), for clustering time-evolving networks; and a companion method, MATCH-EM, for matching corresponding clusters across time points. With link prediction as an objective assessment metric, DHAC provides a substantial advance over existing clustering methods. An application to the yeast metabolic cycle demonstrates how waves of gene expression correspond to individual protein complexes. Our results suggest regulatory mechanisms for assembling the mitochondrial ribosome and illustrate dynamic changes in the components of the nuclear pore.
Kolar et al. in REF discussed the biological dynamic networks and proposed the method to predict the state of protein complexes.
How networks change with time
Bioinformatics
Bioinformatics
Computer Science Medicine