Multi-omic network inference from time-series data
Abstract Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a...
| Published in: | npj Systems Biology and Applications |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-10-01
|
| Online Access: | https://doi.org/10.1038/s41540-025-00591-1 |
| _version_ | 1848682290888048640 |
|---|---|
| author | María Moscardó García Atte Aalto Arthur N. Montanari Alexander Skupin Jorge Gonçalves |
| author_facet | María Moscardó García Atte Aalto Arthur N. Montanari Alexander Skupin Jorge Gonçalves |
| author_sort | María Moscardó García |
| collection | DOAJ |
| container_title | npj Systems Biology and Applications |
| description | Abstract Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a computational method that integrates multi-omic data from bulk metabolomics and single-cell transcriptomics through a Bayesian regression approach that explicitly models the timescale separation between molecular layers. We validate the method on both simulated datasets and experimental Parkinson’s disease data. MINIE exhibits accurate and robust predictive performance across and within omic layers, including curated multi-omic networks and the lac operon. Benchmarking demonstrated significant improvements over state-of-the-art methods while ranking among the top performers in comprehensive single-cell network inference analysis. The integration of regulatory dynamics across molecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference. |
| format | Article |
| id | doaj-art-22b7e8b1ea984fd6b37ba85b4e2d3901 |
| institution | Directory of Open Access Journals |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| spelling | doaj-art-22b7e8b1ea984fd6b37ba85b4e2d39012025-10-19T11:30:57ZengNature Portfolionpj Systems Biology and Applications2056-71892025-10-0111111310.1038/s41540-025-00591-1Multi-omic network inference from time-series dataMaría Moscardó García0Atte Aalto1Arthur N. Montanari2Alexander Skupin3Jorge Gonçalves4Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourg Centre for Systems Biomedicine, University of LuxembourgAbstract Biological phenotypes emerge from complex interactions across molecular layers. Yet, data-driven approaches to infer these regulatory networks have primarily focused on single-omic studies, overlooking inter-layer regulatory relationships. To address these limitations, we developed MINIE, a computational method that integrates multi-omic data from bulk metabolomics and single-cell transcriptomics through a Bayesian regression approach that explicitly models the timescale separation between molecular layers. We validate the method on both simulated datasets and experimental Parkinson’s disease data. MINIE exhibits accurate and robust predictive performance across and within omic layers, including curated multi-omic networks and the lac operon. Benchmarking demonstrated significant improvements over state-of-the-art methods while ranking among the top performers in comprehensive single-cell network inference analysis. The integration of regulatory dynamics across molecular layers and temporal scales provides a powerful tool for comprehensive multi-omic network inference.https://doi.org/10.1038/s41540-025-00591-1 |
| spellingShingle | María Moscardó García Atte Aalto Arthur N. Montanari Alexander Skupin Jorge Gonçalves Multi-omic network inference from time-series data |
| title | Multi-omic network inference from time-series data |
| title_full | Multi-omic network inference from time-series data |
| title_fullStr | Multi-omic network inference from time-series data |
| title_full_unstemmed | Multi-omic network inference from time-series data |
| title_short | Multi-omic network inference from time-series data |
| title_sort | multi omic network inference from time series data |
| url | https://doi.org/10.1038/s41540-025-00591-1 |
| work_keys_str_mv | AT mariamoscardogarcia multiomicnetworkinferencefromtimeseriesdata AT atteaalto multiomicnetworkinferencefromtimeseriesdata AT arthurnmontanari multiomicnetworkinferencefromtimeseriesdata AT alexanderskupin multiomicnetworkinferencefromtimeseriesdata AT jorgegoncalves multiomicnetworkinferencefromtimeseriesdata |