Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques

Maggio, Jimena Di; Paulo, Cecilia Inés; Estrada, Vanina; Perotti, Nora Ines; Ricci, Juan C. Díaz; Díaz, María Soledad

doi:10.1016/j.bej.2013.12.012

Cited by 9 publications

(10 citation statements)

References 31 publications

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“…Moreover, the kinetic models contain large pathways that need large kinetic parameters, which have been used to detect the concentration changes in the model response. These kinetic models, mostly, are nonlinear and the task of kinetic parameters estimation is a hard problem due to identifiability, interdependence among parameters, and poor data quality [8]; where the metabolic model's response errors in system biology are called parameter estimation problem. This problem is mostly solved by sensitivity analysis and global optimization algorithm [3].…”

Section: Introductionmentioning

confidence: 99%

“…These studies stated that nine kinetic parameters were highly affecting the model response of Embden-Meyerhof, pentose phosphate, and phosphotransferase system. Yet, they formulated dynamic parameter estimation problem to estimate the kinetic parameters sensitivity result using Control Vector Parameterization Approach [8]. The dynamic recursive estimator has been used for the estimation of six parameters and was applied to the model heat shock response in E. coli and the model of a synthesis gene regulation system.…”

Section: Introductionmentioning

confidence: 99%

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Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli

Azrag¹,

Kadir²,

Kabir³

et al. 2019

IJMLC

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In the last few decades, the metabolic model of E.coli has attracted the attention of many researchers in the area of biological system modeling. Metabolic models are constructed using mass-balance equations with kinetic-rate computation to simulate the behavior of the metabolic system over time. However, in the development of the metabolic model, large-scale kinetic parameters affect the model response if the parameter values are not assigned accurately, which, in turn, propagates the errors in the ordinary differential equations (ODEs) -the mass balance equations associated with the model. This situation emphasizes the need to adopt a global optimization technique to compute the kinetic parameters such that the errorsthe discrepancy between actual biological data and the model response -are minimized. In this work, the PSO algorithm has been adopted to estimate the kinetic parameters by minimizing the errors of the large-scale of metabolic model response of E. coli with reference to real experimental data. Seven highly sensitive kinetic parameters in the model response were considered in the optimization problem. Estimation of the 7 th kinetic parameters by the PSO method provides a good performance of the model in terms of accuracy. Index Terms-Kinetic parameters, dynamic metabolic model, escherichia coli, PSO algorithm. Science) from the Kyushu Institute of Technology, Japan, in 2010. She currently a senior lecturer at the Universiti Malaysia Pahang, Malaysia, since 2002. Her research interests cover the modelling and simulation, serious games and image processing. Muhammad Nomani Kabir obtained his MSc in 2003 in computational sciences in engineering and his PhD in 2007 in computer science from the University of Braunschweig, Germany. He is currently a senior lecturer at the Faculty of Computer Systems and Software Engineering, University Malaysia Pahang (UMP), Malaysia. He has been a member of IAENG since 2014. He is working as a project leader and a member on many funded research projects from UMP and national grants. He published over 40 journal and conference papers. His research interests include issues related to information security, modelling and simulation, computational methods, image processing and computer networks. Aqeel S. Jaber was born in Iraq in 1977. He received the B.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli

Azrag¹,

Kadir²,

Kabir³

et al. 2019

IJMLC

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“…In this work, we present an extended mathematical model of an extension of the dynamic model for the central carbon metabolism of E. coli K-12 W3110 [14,16]. The proposed model comprises the EmbdenMeyerhof-Parnas pathway, the pentose phosphate pathway, the phosphotransferase system and fermentation reactions (Fig.…”

Section: Mathematical Model Of E Coli Metabolismmentioning

confidence: 99%

“…1). [14]. Mass balances and kinetic rate equations are shown in Tables A1 and A2 of Supporting Information, respectively.…”

Section: Mathematical Model Of E Coli Metabolismmentioning

confidence: 99%

“…In this work, we propose an eigenvalue optimization approach to ensure stability of the glycolysis, the pentose-phosphate pathway, the phosphotransferase system and fermentation reactions of E. coli K-12 W3110, on a detailed kinetic model [14]. The nonlinear optimization problem, corresponding to the right hand side of differential equations, rate equations and stability constraints, is solved with reduced space Successive Quadratic Programming techniques with program IPOPT [15].…”

Section: Introductionmentioning

confidence: 99%

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Design of stable metabolic networks

Maggio

Blanco

Bandoni

et al. 2017

Engineering in Life Sciences

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In this work, we propose eigenvalue optimization combined with Lyapunov theory concepts to ensure stability of the Embden-Meyerhof-Parnas pathway, the pentosephosphate pathway, the phosphotransferase system and fermentation reactions of Escherichia coli. We address the design of a metabolic network for the maximization of different metabolite production rates. The first case study focuses on serine production, based on a model that consists of 18 differential equations corresponding to dynamic mass balances for extracellular glucose and intracellular metabolites, and thirty kinetic rate expressions. A second case study addresses the design problem to maximize ethanol production, based on a dynamic model that involves mass balances for 25 metabolites and 38 kinetic rate equations. The nonlinear optimization problem including stability constraints has been solved with reduced space Successive Quadratic Programming techniques. Numerical results provide useful insights on the stability properties of the studied kinetic models.

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CO2 Biofixation by Microalgae

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Parameter estimation in kinetic models for large scale biotechnological systems with advanced mathematical programming techniques

Cited by 9 publications

References 31 publications

Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli

Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli

Design of stable metabolic networks

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