ArticleNPJ systems biology and applications2025
Current state and open problems in universal differential equations for systems biology.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Lipid nanoparticles as active biointerfaces: From membrane interaction to systemic dysregulation.Acta pharmaceutica Sinica. B · 2026Review
- Universal differential equations for quantifying NF-κB-p53 signaling crosstalk.Bioinformatics (Oxford, England) · 2026Article
- A Hybrid Machine Learning Framework for Interpretable Kinetics of α-Tocopherol and Myricetin Synergism.Journal of food science · 2026Article
- Improvement in model flexibility reveals signaling pathway in T cell responses to pulsatile stimuli.NPJ systems biology and applications · 2026Article
- Learning dynamical systems with biochemically informed neural ordinary differential equations.bioRxiv : the preprint server for biology · 2026Article
- 75 Years of Mathematical Oncology.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Experimentally Guided Iterative Parameter Estimation for Predictive Chemical Oscillator Models.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Universal Differential Equations (UDEs) combine mechanistic differential equations with data-driven artificial neural networks, forming a flexible framework for modelling complex biological systems. This hybrid approach leverages prior knowledge and data to uncover unknown processes and deliver accurate predictions. However, UDEs face challenges in efficient and reliable training due to stiff dynamics and noisy, sparse data common in biology, and in ensuring the interpretability of the parameters of the mechanistic model. We investigate these challenges and evaluate UDE performance on realistic biological scenarios, providing a systematic training pipeline. Our results demonstrate the versatility of UDEs in systems biology and reveal that noise and limited data significantly degrade performance, but regularisation can improve accuracy and interpretability. By addressing key challenges and offering practical solutions, this work advances UDE methodology and underscores its potential in tackling complex problems in systems biology.
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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.