Increasing certainty in systems biology models using Bayesian multimodel inference
Abstract Mathematical models are indispensable for studying the architecture and behavior of intracellular signaling networks. It is common to develop models using phenomenological approximations due to the difficulty of fully observing the intermediate steps in intracellular signaling pathways. Thu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-62415-4 |
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| _version_ | 1849342701710868480 |
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| author | Nathaniel Linden-Santangeli Jin Zhang Boris Kramer Padmini Rangamani |
| author_facet | Nathaniel Linden-Santangeli Jin Zhang Boris Kramer Padmini Rangamani |
| author_sort | Nathaniel Linden-Santangeli |
| collection | DOAJ |
| description | Abstract Mathematical models are indispensable for studying the architecture and behavior of intracellular signaling networks. It is common to develop models using phenomenological approximations due to the difficulty of fully observing the intermediate steps in intracellular signaling pathways. Thus, multiple models can be built to represent the same pathway. This opens up challenges for model selection and decreases certainty in predictions. Here, we investigate Bayesian multimodel inference (MMI) as an approach to increase certainty in systems biology predictions, which becomes relevant when one wants to leverage a set of potentially incomplete models. Using existing models of the extracellular-regulated kinase (ERK) pathway, we show that MMI successfully combines models and yields predictors robust to model set changes and data uncertainties. We then use MMI to identify possible mechanisms of experimentally measured subcellular location-specific ERK activity. This work highlights MMI as a disciplined approach to increasing the certainty of intracellular signaling activity predictions. |
| format | Article |
| id | doaj-art-9c7c8bc90baf46c686decd7d116b3a2f |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-9c7c8bc90baf46c686decd7d116b3a2f2025-08-20T03:43:16ZengNature PortfolioNature Communications2041-17232025-08-0116111610.1038/s41467-025-62415-4Increasing certainty in systems biology models using Bayesian multimodel inferenceNathaniel Linden-Santangeli0Jin Zhang1Boris Kramer2Padmini Rangamani3Department of Mechanical and Aerospace Engineering, University of California San DiegoDepartment of Pharmacology, University of California San DiegoDepartment of Mechanical and Aerospace Engineering, University of California San DiegoDepartment of Mechanical and Aerospace Engineering, University of California San DiegoAbstract Mathematical models are indispensable for studying the architecture and behavior of intracellular signaling networks. It is common to develop models using phenomenological approximations due to the difficulty of fully observing the intermediate steps in intracellular signaling pathways. Thus, multiple models can be built to represent the same pathway. This opens up challenges for model selection and decreases certainty in predictions. Here, we investigate Bayesian multimodel inference (MMI) as an approach to increase certainty in systems biology predictions, which becomes relevant when one wants to leverage a set of potentially incomplete models. Using existing models of the extracellular-regulated kinase (ERK) pathway, we show that MMI successfully combines models and yields predictors robust to model set changes and data uncertainties. We then use MMI to identify possible mechanisms of experimentally measured subcellular location-specific ERK activity. This work highlights MMI as a disciplined approach to increasing the certainty of intracellular signaling activity predictions.https://doi.org/10.1038/s41467-025-62415-4 |
| spellingShingle | Nathaniel Linden-Santangeli Jin Zhang Boris Kramer Padmini Rangamani Increasing certainty in systems biology models using Bayesian multimodel inference Nature Communications |
| title | Increasing certainty in systems biology models using Bayesian multimodel inference |
| title_full | Increasing certainty in systems biology models using Bayesian multimodel inference |
| title_fullStr | Increasing certainty in systems biology models using Bayesian multimodel inference |
| title_full_unstemmed | Increasing certainty in systems biology models using Bayesian multimodel inference |
| title_short | Increasing certainty in systems biology models using Bayesian multimodel inference |
| title_sort | increasing certainty in systems biology models using bayesian multimodel inference |
| url | https://doi.org/10.1038/s41467-025-62415-4 |
| work_keys_str_mv | AT nathaniellindensantangeli increasingcertaintyinsystemsbiologymodelsusingbayesianmultimodelinference AT jinzhang increasingcertaintyinsystemsbiologymodelsusingbayesianmultimodelinference AT boriskramer increasingcertaintyinsystemsbiologymodelsusingbayesianmultimodelinference AT padminirangamani increasingcertaintyinsystemsbiologymodelsusingbayesianmultimodelinference |