Identification of parameters for large-scale kinetic models

Abdulla, Ugur G.; Poteau, Roby

doi:10.1016/j.jcp.2020.110026

Cited by 6 publications

(8 citation statements)

References 34 publications

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“…In this paper, we consider the problem of identification of the external electromagnetic field and internal hyperfine parameters which optimize the quantum singlet-triplet yield of a simplified radical pair system. We employ the qlopt algorithm [13][14][15][16] to identify optimal values of 3-dimensional external electromagnetic field vector and 3-or 6-dimensional hyperfine parameter vector which optimize the quantum singlet-triplet yield for the spin dynamics of radical pairs in 8-or 16-dimensional Schro ¨dinger system corresponding to one-and two-proton cases respectively. Numerical results demonstrate that the quantum singlet-triplet yield of the radical pair system can be significantly reduced if optimization is pursued simultaneously for external magnetic field and internal hyperfine parameters.…”

Section: Discussionmentioning

confidence: 99%

“…The comparison analysis performed in [17,19] demonstrates that robust deterministic local optimization methods embedded with MS strategy, and with sharp sensitivity analysis platform are the best candidates for the creation of powerful global optimization methods for large-scale biological and physical models. The comparison analysis of [15,16] demonstrates the competitiveness and advantage of the qlopt algorithm with other most popular local search methods like lsqnonlin, fmincon, nl2sol. The main goal of this paper is to develop and adapt qlopt method embedded with MS strategy for the quantum optimization in spin dynamics of radical pairs.…”

Section: Introductionmentioning

confidence: 96%

“…The rationale is that applications of such integration would enable the control of biological processes that are electromagnetic dependent. We employ the qlopt algorithm [13][14][15][16] to identify optimal values of 3-dimensional external electromagnetic field vector and 3-or 6-dimensional hyperfine parameter vector which optimize the quantum singlet-triplet yield for the spin dynamics of radical pairs in 8-or 16-dimensional Schro ¨dinger system corresponding to one-and two-proton cases respectively. The method combines ideas of Pontryagin optimization, sensitivity analysis, and Tikhonov regularization.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology

et al. 2023

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Identification of the external electromagnetic fields and internal hyperfine parameters which optimize the quantum singlet-triplet yield of simplified radical pairs modeled by Schrödinger system with spin Hamiltonians given by the sum of Zeeman interaction and hyperfine coupling interaction terms are analyzed. A method that combines sensitivity analysis with Tikhonov regularization is implemented. Numerical results demonstrate that the quantum singlet-triplet yield of the radical pair system can be significantly reduced if optimization is pursued simultaneously for both external magnetic fields and internal hyperfine parameters. The results may contribute towards understanding the structure-function relationship of a putative magnetoreceptor to manipulate and enhance quantum coherences at room temperature and leveraging biofidelic function to inspire novel quantum devices.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology

et al. 2023

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“…Starting from a "reasonable guess" the dynamic model parameters are estimated while minimizing the sum of square deviations between experimental and simulated profiles of state variables inside a nonlinear optimization framework. Though the approach seems straightforward, the estimation of a large set of parameters for complex models (i.e., considering multi-substrate growth, substrate/ production inhibition, or PHA reutilization) can be erroneous due to parametric compensation (Abdulla & Poteau, 2021). Estimation of unique parameters from dynamic models (parameter identifiability) requires the solution of ill-posed inverse problems from a nonlinear system with extreme parametric sensitivity.…”

Section: Model Selection Parameter Estimation and Quality Indicatorsmentioning

confidence: 99%

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production—A state‐of‐the‐art review

Lhamo

Mahanty

2022

Biotech & Bioengineering

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The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen‐limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the “modelling” studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking–Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.

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“…Moreover, measurements are noise-corrupted and limited in time resolution, necessitating the use of data from multiple experimental conditions and the introduction of parameter dependent observable functions. Unfortunately, this prohibits the application of more efficient calibration techniques, such as quasi-linearization methods [ 16 , 17 ] that require direct observation of all model species. Both the structure of biochemical models and limitations in the data impose a non-identifiability that results in parameter optimization problems that are not well-posed in a mathematical sense, violating a crucial assumption of many general-purpose optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models

Fröhlich

Sorger

2022

PLoS Comput Biol

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Ordinary differential equation (ODE) models are widely used to study biochemical reactions in cellular networks since they effectively describe the temporal evolution of these networks using mass action kinetics. The parameters of these models are rarely known a priori and must instead be estimated by calibration using experimental data. Optimization-based calibration of ODE models on is often challenging, even for low-dimensional problems. Multiple hypotheses have been advanced to explain why biochemical model calibration is challenging, including non-identifiability of model parameters, but there are few comprehensive studies that test these hypotheses, likely because tools for performing such studies are also lacking. Nonetheless, reliable model calibration is essential for uncertainty analysis, model comparison, and biological interpretation. We implemented an established trust-region method as a modular Python framework (fides) to enable systematic comparison of different approaches to ODE model calibration involving a variety of Hessian approximation schemes. We evaluated fides on a recently developed corpus of biologically realistic benchmark problems for which real experimental data are available. Unexpectedly, we observed high variability in optimizer performance among different implementations of the same mathematical instructions (algorithms). Analysis of possible sources of poor optimizer performance identified limitations in the widely used Gauss-Newton, BFGS and SR1 Hessian approximation schemes. We addressed these drawbacks with a novel hybrid Hessian approximation scheme that enhances optimizer performance and outperforms existing hybrid approaches. When applied to the corpus of test models, we found that fides was on average more reliable and efficient than existing methods using a variety of criteria. We expect fides to be broadly useful for ODE constrained optimization problems in biochemical models and to be a foundation for future methods development.

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Identification of parameters for large-scale kinetic models

Cited by 6 publications

References 34 publications

Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology

Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production—A state‐of‐the‐art review

Fides: Reliable trust-region optimization for parameter estimation of ordinary differential equation models

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