scholarly journals Conditional Robust Calibration (CRC): a new computational Bayesian methodology for model parameters estimation and identifiability analysis

2017 ◽  
Author(s):  
Fortunato Bianconi ◽  
Chiara Antonini ◽  
Lorenzo Tomassoni ◽  
Paolo Valigi
Keyword(s):  
Systems Biology ◽  
Parameter Space ◽  
Model Calibration ◽  
Time Course ◽  
Initial Conditions ◽  
Profile Likelihood ◽  
Computational Cost ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Identifiability Analysis

AbstractComputational modeling is a remarkable and common tool to quantitatively describe a biological process. However, most model parameters, such as kinetics parameters, initial conditions and scale factors, are usually unknown because they cannot be directly measured.Therefore, key issues in Systems Biology are model calibration and identifiability analysis, i.e. estimate parameters from experimental data and assess how well those parameters are determined by the dimension and quality of the data.Currently in the Systems Biology and Computational Biology communities, the existing methodologies for parameter estimation are divided in two classes: frequentist methods and Bayesian methods. The first ones are based on the optimization of a cost function while the second ones estimate the posterior distribution of model parameters through different sampling techniques.In this work, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for model calibration and identifiability analysis. The algorithm is an iterative procedure based on parameter space sampling and on the definition of multiple objective functions related to each output variables. The method estimates step by step the probability density function (pdf) of parameters conditioned to the experimental measures and it returns as output a subset in the parameter space that best reproduce the dataset.We apply CRC to six Ordinary Differential Equations (ODE) models with different characteristics and complexity to test its performances compared with profile likelihood (PL) and Approximate Bayesian Computation Sequential Montecarlo (ABC-SMC) approaches. The datasets selected for calibration are time course measurements of different nature: noisy or noiseless, real or in silico.Compared with PL, our approach finds a more robust solution because parameter identifiability is inferred by conditional pdfs of estimated parameters. Compared with ABC-SMC, we have found a more precise solution with a reduced computational cost.

scholarly journals A new Bayesian methodology for nonlinear model calibration in Computational Systems Biology

2019 ◽  
Author(s):  
Fortunato Bianconi ◽  
Lorenzo Tomassoni ◽  
Chiara Antonini ◽  
Paolo Valigi
Keyword(s):  
Systems Biology ◽  
Nonlinear Model ◽  
Model Calibration ◽  
Sequential Monte Carlo ◽  
Profile Likelihood ◽  
Computational Cost ◽  
Robustness Analysis ◽  
Model Parameters ◽  
Computational Systems Biology ◽  
Proposal Distribution

AbstractComputational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies for parameter estimation are divided in two classes: frequentist and Bayesian methods. The first ones optimize a cost function while the second ones estimate the parameter posterior distribution through different sampling techniques. Here, we present an innovative Bayesian method, called Conditional Robust Calibration (CRC), for nonlinear model calibration and robustness analysis using omics data. CRC is an iterative algorithm based on the sampling of a proposal distribution and on the definition of multiple objective functions, one for each observable. CRC estimates the probability density function (pdf) of parameters conditioned to the experimental measures and it performs a robustness analysis, quantifying how much each parameter influences the observables behavior. We apply CRC to three Ordinary Differential Equations (ODE) models to test its performances compared to the other state of the art approaches, namely Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) and Delayed Rejection Adaptive Metropolis (DRAM). Compared with these methods, CRC finds a robust solution with a reduced computational cost. CRC is developed as a set of Matlab functions (version R2018), whose fundamental source code is freely available at https://github.com/fortunatobianconi/CRC.

scholarly journals Confidence intervals by constrained optimization—An algorithm and software package for practical identifiability analysis in systems biology

2020 ◽  
Vol 16 (12) ◽  
pp. e1008495
Author(s):  
Ivan Borisov ◽  
Evgeny Metelkin
Keyword(s):  
Systems Biology ◽  
Confidence Intervals ◽  
Software Package ◽  
Profile Likelihood ◽  
Computational Cost ◽  
Computational Time ◽  
Identifiability Analysis ◽  
Practical Identifiability ◽  
Link Type ◽  
Speed Up

Practical identifiability of Systems Biology models has received a lot of attention in recent scientific research. It addresses the crucial question for models’ predictability: how accurately can the models’ parameters be recovered from available experimental data. The methods based on profile likelihood are among the most reliable methods of practical identification. However, these methods are often computationally demanding or lead to inaccurate estimations of parameters’ confidence intervals. Development of methods, which can accurately produce parameters’ confidence intervals in reasonable computational time, is of utmost importance for Systems Biology and QSP modeling. We propose an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, designed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model, discussed in the current article. The CICO algorithm is implemented in a software package freely available in Julia (https://github.com/insysbio/LikelihoodProfiler.jl) and Python (https://github.com/insysbio/LikelihoodProfiler.py).

scholarly journals Contribution of Thermal Infrared Remote Sensing Data in Multiobjective Calibration of a Dual-Source SVAT Model

2006 ◽  
Vol 7 (3) ◽  
pp. 404-420 ◽  
Author(s):  
Benoit Coudert ◽  
Catherine Ottlé ◽  
Brice Boudevillain ◽  
Jérôme Demarty ◽  
Pierre Guillevic
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Model Calibration ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Thermal Infrared ◽  
Model Parameters ◽  
Thermal Infrared Remote Sensing ◽  
Sensing Data ◽  
Dual Source

Abstract This study fits within the overall research on the usage of space remote sensing data to constrain land surface models (LSMs) (also called SVAT models for soil–vegetation–atmosphere transfer). The goal of this paper is to analyze the potential of using thermal infrared (TIR) remote sensing data for LSM calibration. LSMs are characterized by a large number of parameters and initial conditions that have to be specified. This model calibration is generally performed at a local scale by minimization between measurements and time series difference. Recent studies have shed light on the use of multiobjective approaches for performing calibration and for analyzing the model’s sensitivity to input parameters. Such an approach has been implemented in the SEtHyS LSM (for “Suivi de l’Etat Hydrique des Sols,” the French acronym for soil moisture monitoring) with the objective of assessing the information contributed by having knowledge of the remote sensing surface brightness temperature. For this purpose, the model calibration was performed in three different cases at field scale corresponding to different calibration design. The analysis of these numerical experiments permits the authors to show the contribution and the limits of TIR remote sensing data for LSM calibration, in various environmental conditions. The perspectives underline the potential of using a dynamic calibration methodology, taking advantage of the time-varying model parameters’ influence.

scholarly journals Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

2021 ◽  
Author(s):  
Ernesto A. B. F. Lima ◽  
Danial Faghihi ◽  
Russel Philley ◽  
Jianchen Yang ◽  
John Virostko ◽  
...  
Keyword(s):  
Experimental Data ◽  
Cancer Cells ◽  
Temporal Evolution ◽  
Initial Conditions ◽  
Individual Cell ◽  
Computational Cost ◽  
Coarse Grained ◽  
Model Parameters ◽  
Dead Cell ◽  
Agent Based

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity due to probabilistic phenotypic transitions, and numerous model parameters that are difficult to measure directly are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) calibrated with a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days.   Given this model and data, we perform a global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM with a reduced parameter space is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties in model parameters and observational data. The results indicate that the cgABM can reliably predict the spatiotemporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61 [[EQUATION]] 2.01 and 5.78 [[EQUATION]] 1.13, respectively.

scholarly journals Parametric state-time reduction for the transient analysis of multi-physical systems

2018 ◽  
Author(s):  
Ward Rottiers ◽  
Frank Naets ◽  
Wim Desmet
Keyword(s):  
Initial Conditions ◽  
Computational Cost ◽  
Order Reduction ◽  
Model Parameters ◽  
Temporal Dimension ◽  
Time Model ◽  
Model Order ◽  
State Time ◽  
Time Discretisation ◽  
Transient Simulations

This research focusses on computational cost reduction of transient simulations in many-query applications with varying model parameters, initial conditions and parametrised excitations. The typical model reduction approach is limited to the reduction of the dimension of the states or spatial variables, whereas the state-time model order reduction (STMOR) approach reduces both the states and the temporal dimension concurrently. A major novelty in the investigated STMOR approach is that the reduction occurs after time discretisation on an algebraic system of equations (AE).

scholarly journals Optimal Calibration of Evaporation Models against Penman–Monteith Equation

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1484
Author(s):  
Dagmar Dlouhá ◽  
Viktor Dubovský ◽  
Lukáš Pospíšil
Keyword(s):  
Model Calibration ◽  
Heat Storage ◽  
Free Water ◽  
Complex Model ◽  
Model Parameters ◽  
Net Radiation ◽  
Statistical Measures ◽  
Pit Lakes ◽  
Evaporation Models ◽  
Monteith Equation

We present an approach for the calibration of simplified evaporation model parameters based on the optimization of parameters against the most complex model for evaporation estimation, i.e., the Penman–Monteith equation. This model computes the evaporation from several input quantities, such as air temperature, wind speed, heat storage, net radiation etc. However, sometimes all these values are not available, therefore we must use simplified models. Our interest in free water surface evaporation is given by the need for ongoing hydric reclamation of the former Ležáky–Most quarry, i.e., the ongoing restoration of the land that has been mined to a natural and economically usable state. For emerging pit lakes, the prediction of evaporation and the level of water plays a crucial role. We examine the methodology on several popular models and standard statistical measures. The presented approach can be applied in a general model calibration process subject to any theoretical or measured evaporation.

Use of ionic currents to identify and estimate parameters in models of channel blockade

1990 ◽  
Vol 259 (2) ◽  
pp. H626-H634
Author(s):  
C. F. Starmer ◽  
V. V. Nesterenko ◽  
F. R. Gilliam ◽  
A. O. Grant
Keyword(s):  
Time Course ◽  
Experimental Studies ◽  
Ionic Currents ◽  
Blocking Agent ◽  
Model Parameters ◽  
Protein Conformations ◽  
Channel Blockade ◽  
Channel Blocking ◽  
Individual Contributions ◽  
Modulated Receptor

Models of ion channel blockade are frequently validated with observations of ionic currents resulting from electrical or chemical stimulation. Model parameters for some models (modulated receptor hypothesis) cannot be uniquely determined from ionic currents. The time course of ionic currents reflects the activation (fraction of available channels that conduct in the presence of excitation) and availability of channels (the ability of the protein to make a transition to a conducting conformation and where this conformation is not complexed with a drug). In the presence of a channel blocking agent, the voltage dependence of availability appears modified and has been interpreted as evidence that drug-complexed channels exhibit modified transition rates between channel protein conformations. Because blockade and availability both modify ionic currents, their individual contributions to macroscopic conductance cannot be resolved from ionic currents except when constant affinity binding to a bindable site is assumed. Experimental studies of nimodipine block of calcium channels and lidocaine block of sodium channels illustrate these concepts.

A mathematical model of cardiovascular dynamics for the diagnosis and prognosis of hemorrhagic shock

2021 ◽  
Author(s):  
Laura D’Orsi ◽  
Luciano Curcio ◽  
Fabio Cibella ◽  
Alessandro Borri ◽  
Lilach Gavish ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cardiac Output ◽  
Arterial Pressure ◽  
Hemorrhagic Shock ◽  
Time Course ◽  
Differential Algebraic Equations ◽  
Model Parameters ◽  
Algebraic Equations ◽  
Cardiovascular Dynamics ◽  
Compensation Mechanisms

Abstract A variety of mathematical models of the cardiovascular system have been suggested over several years in order to describe the time-course of a series of physiological variables (i.e. heart rate, cardiac output, arterial pressure) relevant for the compensation mechanisms to perturbations, such as severe haemorrhage. The current study provides a simple but realistic mathematical description of cardiovascular dynamics that may be useful in the assessment and prognosis of hemorrhagic shock. The present work proposes a first version of a differential-algebraic equations model, the model dynamical ODE model for haemorrhage (dODEg). The model consists of 10 differential and 14 algebraic equations, incorporating 61 model parameters. This model is capable of replicating the changes in heart rate, mean arterial pressure and cardiac output after the onset of bleeding observed in four experimental animal preparations and fits well to the experimental data. By predicting the time-course of the physiological response after haemorrhage, the dODEg model presented here may be of significant value for the quantitative assessment of conventional or novel therapeutic regimens. The model may be applied to the prediction of survivability and to the determination of the urgency of evacuation towards definitive surgical treatment in the operational setting.

Kinetic Model of Adiabatic Temperature Rise of Mass Concrete

2021 ◽  
Vol 13 (5) ◽  
pp. 771-780
Author(s):  
Shou-Kai Chen ◽  
Bo-Wen Xu
Keyword(s):  
Temperature Field ◽  
Temperature Rise ◽  
Initial Conditions ◽  
Dynamic Change ◽  
Adiabatic Temperature ◽  
Model Parameters ◽  
Hydration Reaction ◽  
Mass Concrete ◽  
Adiabatic Temperature Rise ◽  
Proposed Model

The adiabatic temperature rise model of mass concrete is very important for temperature field simulation, same to crack resistance capacity and temperature control of concrete structures. In this research, a thermal kinetics analysis was performed to study the exothermic hydration reaction process of concrete, and an adiabatic temperature rise model was proposed. The proposed model considers influencing factors, including initial temperature, temperature history, activation energy, and the completion degree of adiabatic temperature rise and is theoretically mature and definitive in physical meaning. It was performed on different initial temperatures for adiabatic temperature rise test; the data were employed in a regression analysis of the model parameters and initial conditions. The same function was applied to describe the dynamic change of the adiabatic temperature rise rates for different initial temperatures and different temperature changing processes and subsequently employed in a finite element analysis of the concrete temperature field. The test results indicated that the proposed model adequately fits the data of the adiabatic temperature rise test, which included different initial temperatures, and accurately predicts the changing pattern of adiabatic temperature rise of concrete at different initial temperatures. Compared with the results using the traditional age-based adiabatic temperature rise model, the results of a calculation example revealed that the simulated calculation results using the proposed model can accurately reflect the temperature change pattern of concrete in heat dissipation conditions.

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