ArticlePloS one2015
Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems.Entropy (Basel, Switzerland) · 2024Article
- Neural ordinary differential equations with irregular and noisy data.Royal Society open 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
- 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
- Systems biology informed deep learning for inferring parameters and hidden dynamics.PLoS computational biology · 2020Article
- Automated, predictive, and interpretable inference ofProceedings of the National Academy of Sciences of the United States of America · 2019Article
- A modified particle swarm optimization algorithm for parameter estimation of a biological system.Theoretical biology & medical modelling · 2018Article
- Robust data-driven discovery of governing physical laws with error bars.Proceedings. Mathematical, physical, and engineering sciences · 2018Article
- Untangling the Hairball: Fitness-Based Asymptotic Reduction of Biological Networks.Biophysical journal · 2017Article
- Data-driven discovery of partial differential equations.Science advances · 2017Article
- Bridging Mechanistic and Phenomenological Models of Complex Biological Systems.PLoS computational biology · 2016Article
- 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
- Automated adaptive inference of phenomenological dynamical models.Nature communications · 2015Article
- Data-Driven Identification of Rational Nonlinear Dynamics in Biochemical Networks via an Implicit Singular Value Decomposition Based Framework.IET systems biologyArticle
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2 authors.
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Abstract
The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.
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