Multi-omic network inference from time-series data.

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Biological phenotypes arise from intricate molecular interactions.
• Current data-driven methods often analyze single omics, neglecting cross-layer regulatory dynamics.
• Integrating multi-omic data is crucial for a holistic understanding of cellular regulation.

Purpose of the Study:

• To develop a computational method, MINIE, for integrating multi-omic data.
• To infer regulatory networks across different molecular layers and timescales.
• To address limitations of single-omic approaches in network inference.

Main Methods:

• Developed MINIE, a Bayesian regression-based computational method.
• Integrated bulk metabolomics and single-cell transcriptomics data.
• Modeled timescale separation between molecular layers for improved inference.

Main Results:

• MINIE demonstrated accurate and robust predictive performance across and within omic layers.
• Validated on simulated datasets and experimental Parkinson's disease data.
• Outperformed state-of-the-art methods in multi-omic network inference.

Conclusions:

• MINIE provides a powerful tool for comprehensive multi-omic network inference.
• The method effectively integrates regulatory dynamics across molecular layers and temporal scales.
• Enables deeper insights into complex biological systems through integrated omics analysis.