scholarly journals Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 98
Author(s):  
Ewelina Weglarz-Tomczak ◽  
Jakub M. Tomczak ◽  
Agoston E. Eiben ◽  
Stanley Brul
Keyword(s):  
Experimental Data ◽  
Saccharomyces Cerevisiae ◽  
Systems Biology ◽  
Parameter Identification ◽  
Dynamic Models ◽  
Optimization Methods ◽  
Population Based ◽  
Model Parameters ◽  
K Nearest Neighbor ◽  
Dynamic Simulations

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

Nonlinear Optimization Methods for the Determination of Heat Source Model Parameters

2016 ◽  
Vol 879 ◽  
pp. 2008-2013
Author(s):  
Udo Hartel ◽  
Alexander Ilin ◽  
Steffen Sonntag ◽  
Vesselin Michailov
Keyword(s):  
Experimental Data ◽  
Heat Source ◽  
Parameter Identification ◽  
Nonlinear Optimization ◽  
Laser Beam Welding ◽  
Source Model ◽  
Transient Temperature ◽  
Surface Model ◽  
Model Parameters ◽  
Heat Source Model

In this paper the technique of parameter identification is investigated to reconstruct the 3D transient temperature field for the simulation of laser beam welding. The reconstruction bases on volume heat source models and makes use of experimental data. The parameter identification leads to an inverse heat conduction problem which cannot be solved exactly but in terms of an optimal alignment of the simulation and experimental data. To solve the inverse problem, methods of nonlinear optimization are applied to minimize a problem dependent objective function.In particular the objective function is generated based on the Response Surface Model (RSM) technique. Sampling points on the RSM are determined by means of Finite-Element-Analysis (FEA). The scope of this research paper is the evaluation and comparison of gradient based and stochastic optimization algorithms. The proposed parameter identification makes it possible to determine the heat source model parameters in an automated way. The methodology is applied on welds of dissimilar material joints.

Study on the Dynamic Models of Systems Biology from the View of Engineering

2012 ◽  
Vol 220-223 ◽  
pp. 952-957
Author(s):  
Chen Liu ◽  
Xiao Yan Liu
Keyword(s):  
Mathematical Model ◽  
Systems Biology ◽  
Dynamic Process ◽  
Biological System ◽  
Dynamic Models ◽  
Model Parameters ◽  
State Variables ◽  
Biochemical Pathway ◽  
The Right ◽  
The Mathematical Model

From the view of engineering, based on expatiating the features of systems biology, the paper discusses the workflows and the research emphasis of systems biology. It also explains how to model and analyze the dynamic process of signal transmitting network for a biological system by an example. Based on the complexity and uncertainty of the mathematical model, the right methods are chosen to realize the effective estimation of state variables and model parameters for the biochemical pathway.

Parameter Identification for HAPC System in Hot Tandem Mill

2010 ◽  
Vol 154-155 ◽  
pp. 781-786
Author(s):  
Xu Li ◽  
Wen Xue Zhang ◽  
Dian Hua Zhang ◽  
Dan Yan
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Parameter Identification ◽  
Actual Data ◽  
Automation System ◽  
Model Parameters ◽  
Simulation Data ◽  
Matlab Software ◽  
Identification Method ◽  
Curve Fit

Under condition that exact values of model parameters can not be calculated accurately in hot tandem mill system and change with the time passing, model parameters are identified by adopting identification method based on the parameter model and sampling the datum on site; Basic automation system is used for the sampling of actual data, MATLAB software is adopted for curve fit. By comparing the experimental data and simulation data, the consequence of simulation testifies the accuracy of identified mathematical model.

scholarly journals Saccharomyces cerevisiae FED-BATCH FERMENTATION AND ARTIFICIAL INTELLIGENCE METHOD FOR ADJUSTING MODEL PARAMETERS TO EXPERIMENTAL DATA

2019 ◽  
pp. 111-117
Author(s):  
Marco César Prado Soares ◽  
Gabriel Fernandes Luz ◽  
Aline Carvalho da Costa ◽  
Matheus Kauê Gomes ◽  
Beatriz Ferreira Mendes ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Saccharomyces Cerevisiae ◽  
Batch Fermentation ◽  
Model Parameters ◽  
Fed Batch ◽  
Fed Batch Fermentation

scholarly journals Comparison of Bayesian Methods on Parameter Identification for a Viscoplastic Model with Damage

Metals ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 876 ◽  
Author(s):  
Ehsan Adeli ◽  
Bojana Rosić ◽  
Hermann G. Matthies ◽  
Sven Reinstädler ◽  
Dieter Dinkler
Keyword(s):  
Experimental Data ◽  
Parameter Identification ◽  
Measurement Errors ◽  
Surface Displacement ◽  
Life Time ◽  
Material Model ◽  
Model Parameters ◽  
Bayesian Approaches ◽  
Model Response ◽  
The Difference

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore, identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the focus is on the identification of material parameters of a viscoplastic damaging material using a stochastic simulation technique to generate artificial data which exhibit the same stochastic behavior as experimental data. It is proposed to use Bayesian inverse methods for parameter identification and therefore the model and damage parameters are identified by applying the Transitional Markov Chain Monte Carlo Method (TMCMC) and Gauss-Markov-Kalman filter (GMKF) approach. Identified parameters by using these two Bayesian approaches are compared with the true parameters in the simulation and with each other, and the efficiency of the identification methods is discussed. The aim of this study is to observe which one of the mentioned methods is more suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, using a limited surface displacement measurement vector and see how much information is indeed needed to estimate the parameters accurately.

scholarly journals Modified Bouc-Wen Model Based on Fractional Derivative and Application in Magnetorheological Elastomer

2021 ◽  
Vol 8 ◽  
Author(s):  
Shaopu Yang ◽  
Peng Wang ◽  
Yongqiang Liu ◽  
Xufeng Dong ◽  
Yu Tong ◽  
...  
Keyword(s):  
Experimental Data ◽  
Magnetic Flux ◽  
Fractional Derivative ◽  
Parameter Identification ◽  
Strain Amplitude ◽  
Model Parameters ◽  
Loading Conditions ◽  
Magnetorheological Elastomer ◽  
Nonlinear Viscoelastic ◽  
Wide Range

To accurately characterize the mechanical behavior of magnetorheological elastomer (MRE) under a wide range of strain amplitude, excitation frequency, and magnetic field, the viscoelastic fractional derivative was introduced, and a modified Bouc-Wen model based on fractional derivative for MRE in a nonlinear viscoelastic region was established. The Bouc-Wen model can accurately describe the hysteretic characteristics of the MRE nonlinear viscoelastic region, but it cannot accurately simulate magneto-viscoelasticity and frequency dependence. The fractional derivative can express this characteristic with fewer parameters and higher accuracy. The model’s validity was verified by fitting the experimental data of stress and strain measured in shear mode. By analyzing the coupling relationship between the model parameters and strain amplitude, frequency, and magnetic flux densities, a method of parameter identification under multi-loading conditions was proposed, and the modified model parameters were identified. The results reveal that the modified Bouc-Wen model can accurately characterize the mechanical properties of the nonlinear viscoelastic region of MRE, and the fitting accuracy is significantly improved compared with the Bouc-Wen model. The expression of the model parameters obtained from the method of parameter identification under multi-loading conditions is accurate in a wide range of strain amplitude, frequency, and magnetic flux density. The fitness values of simulation data and experimental data under identified and non-identified conditions exceed 90%, showcasing the effectiveness of the modified Bouc-Wen model and the feasibility of the parameter identification method under multi-loading conditions.

scholarly journals Mini-batch optimization enables training of ODE models on large-scale datasets

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Paul Stapor ◽  
Leonard Schmiester ◽  
Christoph Wierling ◽  
Simon Merkt ◽  
Dilan Pathirana ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Dynamic Modelling ◽  
Optimization Methods ◽  
Scale Model ◽  
Model Parameters ◽  
Scaling Properties ◽  
Ode Models ◽  
Cellular Signal ◽  
Order Of Magnitude

AbstractQuantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Achieving Procedure of a Kinetic Model to Predict the Influence of the Process Global Temperature on a Technological Alcoholic Fermentation II. An Arrhenius type Kinetic Model

2008 ◽  
Vol 59 (4) ◽  
Author(s):  
Neculai Catalin Lungu ◽  
Maria Alexandroaei
Keyword(s):  
Experimental Data ◽  
Saccharomyces Cerevisiae ◽  
Kinetic Model ◽  
Economic Efficiency ◽  
Fermentation Process ◽  
Alcoholic Fermentation ◽  
Global Temperature ◽  
Medium Temperature ◽  
Biotechnological Process

The aim of the present work is to offer a practical methodology to realise an Arrhenius type kinetic model for a biotechnological process of alcoholic fermentation based on the Saccharomyces cerevisiae yeast. Using the experimental data we can correlate the medium temperature of fermentation with the time needed for a fermentation process under imposed conditions of economic efficiency.

Sign in / Sign up

Export Citation Format

Share Document