Automated refinement and inference of analytical models for metabolic networks

By Michael D. Schmidt, Ravishankar Rao Vallabhajosyula, Jerry Jenkins, Jonathan E. Hood, A. S. Soni, John P. Wikswo Jr., Hod Lipson. 2011

135 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (2018, methodology): “…behavior [11], automated inference of dynamics [12, 13, 14], normal form identiﬁcation in climate [15], nonlinear Laplacian spectral analysis [16], modeling emergent behavior [11], Koopman analysis [17, 18, 19, 20], automated inference of dynamics [12, 13, 14], and symbolic regression [21, 22].”
Discovering governing equations from data by sparse identification of nonlinear dynamical systems (2015, methodology): “These include statistical methods of automated inference of dynamics [6, 7, 8], and equation-free modeling [9], including empirical dynamic modeling [10, 11].”
Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression (2014, methodology): “[5, 10]), while preserving the ability to fit nonlinearities pervasive in biological systems.”
Data Driven Governing Equations Approximation Using Deep Neural Networks (2018, methodology): “Earlier efforts include methods using symbolic regression ([5, 43]), equation-free modeling [24], heterogeneous multiscale method (HMM) ([15]), artificial neural networks ([19]), nonlinear regression ([50]), empirical dynamic modeling ([46, 53]), nonlinear Laplacian spectral analysis ([18]), automat”
Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics (2016, methodology): “This network has also been previously analyzed as a test case for model inference [80].”
Automated adaptive inference of phenomenological dynamical models (2015, methodology): “Approaches have been developed using linear dynamic models 10 , Bayesian Networks (Supplementary Note 8), recurrent neural networks 11 , evolved regulatory networks 12 and symbolic regression 13,14 .”
Data-driven discovery of coordinates and governing equations (2019, background): “Numerous recent approaches leverage machine learning to identify dynamical systems models from data [19–26], with many using neural networks to model time-series data [18, 27–38].”
Contemporary Symbolic Regression Methods and their Relative Performance (2021, methodology): “We intend to include these data in future versions, given the evidence that SR models can sometimes discover unexpected analytical models that outperform the expert models in a ﬁeld (e.g., in studies of yeast metabolism [76] and ﬂuid tank systems [67]).”
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (2020, methodology): “implicit-SINDy methods on the challenging yeast glycolysis model [46, 69] given by”