A Data-Driven Framework for Identifying Nonlinear Dynamic Models of Genetic Parts

Krishnanathan, Kirubhakaran; Anderson, Sean; Billings, S.A.; Kadirkamanathan, Visakan

doi:10.1021/sb300009t

Cited by 18 publications

(9 citation statements)

References 58 publications

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“…The potential of the methodology to develop nonlinear models from data has since been utilized by a diverse range of scientific fields. It has been used in analyzing the adaptive changes in the photoreceptors of Drosophila Flies (Friederich et al, ), modeling the tide at the Venice Lagoon (Wei & Billings, ), the dynamics of Synthetic bioparts (Krishnanathan et al, ), and the Belousov‐Zhabotinsky chemical reaction (Zhao et al, ). In geospace the method was first used to model the Dst index and analyze the dynamics in the frequency domain (Boaghe et al, ; Balikhin et al, ).…”

Section: Narmax Methodologymentioning

confidence: 99%

The System Science Development of Local Time‐Dependent 40‐keV Electron Flux Models for Geostationary Orbit

et al. 2019

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At geosynchronous Earth orbit, the radiation belt/ring current electron fluxes with energies up to several hundred kiloelectron volts can vary widely in magnetic local time (MLT). This study aims to develop Nonlinear AutoRegressive eXogenous models using system science techniques, which account for the spatial variation in MLT. This is difficult for system science techniques, since there is sparse data availability of the electron fluxes at different MLT. To solve this problem, the data are binned from Geostationary Operational Environmental Satellites (GOES) 13, 14, and 15 by MLT, and a separate Nonlinear AutoRegressive eXogenous model is deduced for each bin using solar wind variables as the inputs to the model. These models are then conjugated into one spatiotemporal forecast. The model performance statistics for each model varies in MLT with a prediction efficiency between 47% and 75% and a correlation coefficient between 51.3% and 78.9% for the period from 1 March 2013 to 31 December 2017.

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Section: Narmax Methodologymentioning

confidence: 99%

The System Science Development of Local Time‐Dependent 40‐keV Electron Flux Models for Geostationary Orbit

et al. 2019

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“…The NARMAX system identification technique that is used to deduce the models in this study was originally developed by Leontaritis and Billings (1985a, 1985b) and has been used in a diverse range of scientific fields from biology (Friederich et al., 2009; Krishnanathan et al., 2012) to space physics (Boaghe et al., 2001). In space physics, the NARMAX methodology was first used to model the Dst index and analyze it in the frequency domain (Balikhin et al., 2001; Boaghe et al., 2001).…”

Section: Methodsmentioning

confidence: 99%

System Identification of Local Time Electron Fluencies at Geostationary Orbit

et al. 2020

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The electron fluxes at geostationary orbit measured by Geostationary Operational Environmental Satellite (GOES) 13, 14, and 15 spacecraft are modeled using system identification techniques. System identification, similar to machine learning, uses input-output data to train a model, which can then be used to provide forecasts. This study employs the nonlinear autoregressive moving average exogenous technique to deduce the electron flux models. The electron fluxes at geostationary orbit are known to vary in space and time, making it a spatiotemporal system, which complicates the modeling using system identification/machine learning approach. Therefore, the electron flux data are binned into 24 magnetic local time (MLT), and a separate model is developed for each of the 24 MLT bins. MLT models are developed for six of the GOES 13, 14, and 15 electron flux energy channels (75 keV, 150 keV, 275 keV, 475 keV, >800 keV, and >2 MeV). The models are assessed on separate test data by prediction efficiency (PE) and correlation coefficient (CC) and found these to vary by MLT and electron energy. The lowest energy of 75 keV at the midnight sector had a PE of 36.0 and CC of 59.3, which increased on the dayside to a PE of 66.9 and CC of 81.6. These metrics increased to the >2 MeV model, which had a low PE and CC of 63.0 and 81.8 on the nightside to a high of 80.3 and 90.8 on the dayside.

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“…Characterisation of uncertainty is important in control engineering (Gevers, 2005), but also in other areas where SID is now commonly applied such as the life sciences (Anderson, Lepora, Porrill, & Dean, 2010;Krishnanathan, Anderson, Billings, & Kadirkamanathan, 2012;Kukreja, Galiana, & Kearney, 2003). In SID, computational Bayesian (or probabilistic) methods are gaining popularity due to advances in processing power (Baldacchino, Anderson, & Kadirkamanathan, 2013;Falsone, Piroddi, & Prandini, 2015;Ninness & Henriksen, 2010).…”

Section: Introductionmentioning

confidence: 99%

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

Krishnanathan

Anderson

Billings

et al. 2015

International Journal of Systems Science

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In this paper we derive a system identification framework for continuous-time nonlinear systems, for the first time using a simulation-focused computational Bayesian approach. Simulation approaches to nonlinear system identification have been shown to outperform regression methods under certain conditions, such as non-persistently exciting inputs and fast-sampling. We use the approximate Bayesian computation (ABC) algorithm to perform simulation-based inference of model parameters. The framework has the following main advantages: (i) parameter distributions are intrinsically generated, giving the user a clear description of uncertainty, (ii) the simulation approach avoids the difficult problem of estimating signal derivatives as is common with other continuous-time methods, and (iii) as noted above, the simulation approach improves identification under conditions of non-persistently exciting inputs and fast-sampling. Term selection is performed by judging parameter significance using parameter distributions that are intrinsically generated as part of the ABC procedure. The results from a numerical example demonstrate that the method performs well in noisy scenarios, especially in comparison to competing techniques that rely on signal derivative estimation.

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A Data-Driven Framework for Identifying Nonlinear Dynamic Models of Genetic Parts

Cited by 18 publications

References 58 publications

The System Science Development of Local Time‐Dependent 40‐keV Electron Flux Models for Geostationary Orbit

The System Science Development of Local Time‐Dependent 40‐keV Electron Flux Models for Geostationary Orbit

System Identification of Local Time Electron Fluencies at Geostationary Orbit

Computational system identification of continuous-time nonlinear systems using approximate Bayesian computation

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