ArticleNature communications2025
Increasing certainty in systems biology models using Bayesian multimodel inference.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Trans-dimensional Bayesian model averaging for 13C-metabolic flux analysis: evidence-based flux inference under structural model uncertainty.Bioinformatics (Oxford, England) · 2026Article
- Interplay between store-operated calcium entry and mitochondrial phosphate handling modulates force and fatigue during exercise.bioRxiv : the preprint server for biology · 2026Article
- Drosophila embryo cellularization is tuned by the viscoelastic properties of membrane-cortex linkers.Biophysical journal · 2026Article
- Uncovering the molecular basis of kinase activity and substrate recognition with phospho-PCA.bioRxiv : the preprint server for biology · 2025Article
- Systems modeling and uncertainty quantification of AMP-activated protein kinase signaling.NPJ systems biology and applications · 2025Article
- The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quantification.Annual review of biomedical engineering · 2025Review
- Mathematical modeling reveals cell differentiation processes and progenitor kinetics necessary for proper nephrogenesis.Frontiers in cell and developmental biology · 2025Article
- Spatiotemporal orchestration of calcium-cAMP oscillations on AKAP/AC nanodomains is governed by an incoherent feedforward loop.PLoS computational biology · 2024Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.