Multi-omic network inference from time-series data#

Authors#

Maria Isabel Moscardo Garcia, Atte Aalto, Arthur Noronha Montanari, Alexander Skupin, Jorge Goncalves

Abstract#

Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these reg- ulatory networks have primarily focused on single-omic stud- ies, overlooking key interactions spanning different layers. To address these limitations, we propose MINIE, a computational method that integrates multi-omic data from bulk metabolom- ics and single-cell transcriptomics to infer regulatory networks across multiple layers. By leveraging time-series data, our ap- proach identifies causal interactions through a Bayesian regres- sion framework while also accounting for the timescale separa- tion observed between the omic layers. We validate the method using both simulated data from biological models and experi- mental data from a Parkinson’s disease study. Overall, MINIE exhibits accurate and robust predictive performance across and within omic layers, outperforming previously published meth- ods. The proposed integration of system dynamics across mo- lecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference.