Abstract
The yeast metabolic cycle (YMC), characterized by cyclic oscillations in transcripts and metabolites, is an ideal model for studying biological rhythms. Although multiple omics datasets on the YMC are available, a unified landscape for this process is missing. To address this gap, we integrated multi-omics datasets by singular value decompositions (SVDs), which stratify each dataset into two levels and define four eigen-phases: primary 1A/1B and secondary 2A/2B. The eigen-phases occur cyclically in the order 1B, 2A, 1A, and 2B, demonstrating an interplay of induction and repression: one eigen-phase induces the next one at a different level, while represses the other one at the same level. Distinct molecular characteristics were identified for each eigen-phase. Novel ones include the production and consumption of glycerol in eigen-phases 2A/2B, and the opposite regulation of ribosome biogenesis and aerobic respiration between 2A/2B. Moreover, we estimated the timing of multi-omics: histone modifications H3K9ac/H3K18ac precede mRNA transcription in ∼3 min, followed by metabolomic changes in ∼13 min. The transition to the next eigen-phase occurs roughly 38 min later. From epigenome H3K9ac/H3K18ac to metabolome, the eigen-entropy increases. This work provides a computational framework applicable to multi-omics data integration.