ArticlePhysical biology2011
Automated refinement and inference of analytical models for metabolic networks.
Article in Physical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- Learning Physics Informed Neural ODEs with Partial Measurements.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2025Article
- Neural ordinary differential equations with irregular and noisy data.Royal Society open science · 2023Article
- Changing biosynthesis of terpenoid percursors in rice through synthetic biology.Frontiers in plant science · 2023Article
- Discovery of nonlinear dynamical systems using a Runge-Kutta inspired dictionary-based sparse regression approach.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Contemporary Symbolic Regression Methods and their Relative Performance.Advances in neural information processing systems · 2021Article
- Is Human Walking a Network Medicine Problem? An Analysis Using Symbolic Regression Models with Genetic Programming.Computer methods and programs in biomedicine · 2021Article
- Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling.International journal for numerical methods in biomedical engineering · 2021Article
- Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.Frontiers in molecular biosciences · 2021Review
- Bayesian differential programming for robust systems identification under uncertainty.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics.Proceedings. Mathematical, physical, and engineering sciences · 2020Article
- AI Feynman: A physics-inspired method for symbolic regression.Science advances · 2020Article
- Automated, predictive, and interpretable inference ofProceedings of the National Academy of Sciences of the United States of America · 2019Article
- Identifying models of dielectric breakdown strength from high-throughput data via genetic programming.Scientific reports · 2017Article
- Circadian hormone control in a human-on-a-chip: In vitro biology's ignored component?Experimental biology and medicine (Maywood, N.J.) · 2017Review
- Data-driven discovery of partial differential equations.Science advances · 2017Article
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Article
- Half dozen of one, six billion of the other: What can small- and large-scale molecular systems biology learn from one another?Genome research · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model--suggesting nonlinear terms and structural modifications--or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.
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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.