Qualitative and Quantitative Optimal Experimental Design for Parameter Identification of a MAP Kinase Model

Schenkendorf, René; Mangold, Michael

doi:10.3182/20110828-6-it-1002.02882

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…It is analogous to the concept of the so-called unscented transformation presented by [37], which describes the parameter uncertainty with some deterministic sample points and approximate the statistics of outputs with the corresponding model evaluations, but has different deterministic sample points, associated weights and higher accuracy [34]. The PEM has been successfully applied in the field of sensitivity analysis [38] and optimal experimental design [39][40][41] to quantify the influence of measurement imperfections on system identification. A brief introduction to the PEM is given in Section 3.1.…”

Section: Point Estimate Methodsmentioning

confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

2018

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Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design.

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Section: Point Estimate Methodsmentioning

confidence: 99%

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

2018

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“…Main research problems considered (Dargatz, 2010) Bayesian inference for biochemical models involving diffusion (Mu, 2010) rate and state estimation in S-system and linear fractional model (LFM) (Palmisano, 2010) software tools for modeling and parameter estimation in BRNs (Mazur, 2012) inference via stochastic sampling and Bayesian learning framework (Srivastava, 2012) stochastic simulations of BRNs combined with likelihood based parameter estimation, confidence intervals, sensitivity analysis (Gupta, 2013) parameter estimation in deterministic and stochastic BRNs, inference with model reduction, mostly MCMC methods (Hasenauer, 2013) Bayesian estimation and uncertainty analysis of population heterogeneity and proliferation dynamics (Linder, 2013) penalized LS algorithm and diffusion and linear noise approximations and algebraic statistical models (Flassig, 2014) model identification for large scale gene regulatory networks (Liu, 2014) approximate Bayesian inference methods and sensitivity analysis (Moritz, 2014) structural identification and parameter estimation for modular and layered type of modes (Paul, 2014) analysis of MCMC based methods (Ruess, 2014) optimum estimation and experiment design assuming ML and Bayesian inference and Fisher information (Schenkendorf, 2014) quantification of parameter uncertainty, optimal experiment design for parameter estimation and model selection (Smadbeck, 2014) moment closure methods, model reduction, stability and spectral analysis of BRNs Langevin equation, moment closure approximations, representations of stochastic RDME (Zechner, 2014) inference from heterogeneous snapshot and time-lapse data (Galagali, 2016) Bayesian and non-Bayesian inference in BRNs, adaptive MCMC methods, network-aware inference, inference for approximated BRNs (Hussain, 2016) sequential probability ratio test, Bayesian model checking, automated and formal verification, parameter discovery (Lakatos, 2017) multivariate moment closure and reachability analysis (Liao, 2017) tensor representation and analysis of BRNs of ABC methods can be found in (Drovandi et al, 2016). The basic idea is to find parameter values which generate the same statistics as the observed data.…”

Section: Thesismentioning

confidence: 99%

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

2019

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The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.

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“…Please note that the derived samples are not used to quantify the information content [16,46,47], but to quantify the uncertainty of the local sensitivities or to directly derive global parameter sensitivities. In [48,49], the performance of the PEM for calculating global parameter sensitivities and uncertainty analysis is discussed in more detail. Here, the PEM provides appropriate approximations at low computational costs compared to Monte Carlo simulations.…”

Section: Implementation Aspectsmentioning

confidence: 99%

The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design

et al. 2018

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In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under study with suitable model parameter estimates. Often, the model parameters identified by experimental data, however, pose severe uncertainties leading to incorrect or biased inferences. This applies in particular in the field of pharmaceutical manufacturing, where usually the measurement data are limited in quantity and quality when analyzing novel active pharmaceutical ingredients.Optimally designed experiments, in turn, aim to increase the quality of the gathered data in the most efficient way. Any improvement in data quality results in more precise parameter estimates and more reliable model candidates. The applied methods for parameter sensitivity analyses and design criteria are crucial for the effectiveness of the optimal experimental design. In this work, different design measures based on global parameter sensitivities are critically compared with state-of-the-art concepts that follow simplifying linearization principles. The efficient implementation of the proposed sensitivity measures is explicitly addressed to be applicable to complex chemical engineering problems of practical relevance. As a case study, the homogeneous synthesis of 3,4-dihydro-1H-1-benzazepine-2,5-dione, a scaffold for the preparation of various protein kinase inhibitors, is analyzed followed by a more complex model of biochemical reactions. In both studies, the model-based optimal experimental design benefits from global parameter sensitivities combined with proper design measures.

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Qualitative and Quantitative Optimal Experimental Design for Parameter Identification of a MAP Kinase Model

Cited by 10 publications

References 18 publications

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks

The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design

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