Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecular Interaction Network

Abstract

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is ∼67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in ∼590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format.

Further developments towards a genome-scale metabolic model of yeast

“…In the recent very large-scale reconstruction of the yeast molecular interaction network by Aho et al . [ 36 ], genomic, transcriptomic, proteomic and metabolomic data were integrated. These authors note that incorporating the higher quality data of Yeast 1.0 (and therefore even more of this contribution) would considerably improve their reconstruction over the metabolic information extracted from KEGG, and also that standards compliance is essential to this integration task.…”

Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

“…Yeast 5 further expands the scope of reconstruction to refine details of sphingolipid metabolism [ 20 , 21 ]. Though the stoichiometrically constrained approach can theoretically be expanded to include all biochemical reactions in the organism being modeled [ 43 ], the Yeast reconstruction is currently limited to the yeast metabolic network. Although Yeast 5 is not strain-specific, the auxotroph information we used for our analysis focused on auxotrophies documented in S. cerevisiae reference strain SC288C.…”

Expanding Interactome Analyses beyond Model Eukaryotes

“…Finally, machine learning approaches can predict PPI using a variety of high quality data types during classifier training [175][176][177][178][179][180][181]. The use of multiple data types improves prediction accuracy over single-type prediction methods [182,183]. Popular algorithms for PPI prediction include support vector machines [85,176,184,185], random forest algorithms [176,182,[186][187][188], and naïve Bayes [176,189].…”