Parametric sensitivity analysis and reduction of a detailed nutritional model of plant cell cultures

Mailier, Johan; Delmotte, A; Cloutier, Michel; Jolicœur, Mario; Wouwer, Alain Vande

doi:10.1002/bit.23030

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…This lack of sensitivity may partially come from the experimental space used to calibrate and to challenge the model. Although these non-sensitive parameters are biologically relevant, describing existing active pathways and enzymatic reactions, they may require expanded experimental culture conditions to be solicited, as we proposed in a recent work [59]. For space limitation only, sensitivity results for parental culture are shown while the other cultures exhibited almost the same results.…”

Section: Resultsmentioning

confidence: 96%

Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform

et al. 2014

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Monoclonal antibody producing Chinese hamster ovary (CHO) cells have been shown to undergo metabolic changes when engineered to produce high titers of recombinant proteins. In this work, we have studied the distinct metabolism of CHO cell clones harboring an efficient inducible expression system, based on the cumate gene switch, and displaying different expression levels, high and low productivities, compared to that of the parental cells from which they were derived. A kinetic model for CHO cell metabolism was further developed to include metabolic regulation. Model calibration was performed using intracellular and extracellular metabolite profiles obtained from shake flask batch cultures. Model simulations of intracellular fluxes and ratios known as biomarkers revealed significant changes correlated with clonal variation but not to the recombinant protein expression level. Metabolic flux distribution mostly differs in the reactions involving pyruvate metabolism, with an increased net flux of pyruvate into the tricarboxylic acid (TCA) cycle in the high-producer clone, either being induced or non-induced with cumate. More specifically, CHO cell metabolism in this clone was characterized by an efficient utilization of glucose and a high pyruvate dehydrogenase flux. Moreover, the high-producer clone shows a high rate of anaplerosis from pyruvate to oxaloacetate, through pyruvate carboxylase and from glutamate to α-ketoglutarate, through glutamate dehydrogenase, and a reduced rate of cataplerosis from malate to pyruvate, through malic enzyme. Indeed, the increase of flux through pyruvate carboxylase was not driven by an increased anabolic demand. It is in fact linked to an increase of the TCA cycle global flux, which allows better regulation of higher redox and more efficient metabolic states. To the best of our knowledge, this is the first time a dynamic in silico platform is proposed to analyze and compare the metabolomic behavior of different CHO clones.

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Section: Resultsmentioning

confidence: 96%

Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform

et al. 2014

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“…[5] Examples of calculation of the sensitivity functions can be found in the literature. [7,13] After sensitivity analysis, a model reduction could be conducted by removing the least significant parameters or fixing their values from the literature.…”

Section: Sensitivity Functionsmentioning

confidence: 99%

“…Parameter identification becomes a difficult task because of the complexity of models as well as the lack of sufficient and reliable experimental data. [7] Moreover, the shape of the sensitivity function as a function of time allows for choosing the time range during which the identification of a parameter can be the most accurate. [5] Sensitivity analysis appears as a powerful tool to determine the parameters that have the biggest influence on model outputs, while avoiding over-parameterization and reducing the complexity of the model, as discussed by Bastin and Dochain.…”

Section: Introductionmentioning

confidence: 99%

“…[6] Sensitivity analysis has recently been applied to biological models such as plant cell cultures. [7] Moreover, the shape of the sensitivity function as a function of time allows for choosing the time range during which the identification of a parameter can be the most accurate. In fact, the higher the amplitude of a sensitivity function during a certain time period, the more the parameter identification will be accurate in this time interval.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification and state estimation of a microalgae dynamical model in sulphur deprived conditions: Global sensitivity analysis, optimization criterion, extended Kalman filter

2014

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In this article, a dynamic model describing the growth of the green microalgae Chlamydomonas reinhardtii, under light attenuation and sulphurdeprived conditions leading to hydrogen production in a photobioreactor is presented. The strong interactions between biological and physical phenomena require complex mathematical expressions with an important number of parameters. This article presents a global identification procedure in three steps using data from batch experiments. First, it includes the application of a sensitivity function analysis, which allows one to determine the parameters having the greatest influence on model outputs. Secondly, the most influential parameters were identified by using the classical least-squares cost function. This stage is applied to the experimental data collected from a lab-scale batch photobioreactor. Finally, the implementation of an Extended Kalman Filter estimating the biomass concentration, extracellular and intracellular sulphur concentrations is presented. Thereby, the observer uses on-line measurements provided by a mass spectrometer measuring the outlet gas composition (O 2 , CO 2 ). Software sensor performances and limits are illustrated in simulation and with experimental data.

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“…In doing so, however, there is a risk of over-parameterization. [3] In this study, we applied parameter estimation techniques [4,5] in reconstructing the Monod model to decipher growth kinetics of Shewanella oneidensis MR-1. MR-1 is an environmentally important bacterium that plays important roles in bioremediation, microbial fuel cells and ecological carbon cycling.…”

Section: Introductionmentioning

confidence: 99%

Construction of a parsimonious kinetic model to capture microbial dynamics via parameter estimation

Feng

Tang

Dolan

2013

Inverse Problems in Science and Engineering

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Understanding microbial kinetic behaviour is important for bioprocessing engineering, such as chemical synthesis and bioremediation. However, development of proper models to capture complicated microbial kinetics is a challenging task. In this study, we demonstrate a comparative analysis of the complexity and accuracy tradeoff for modelling the growth of Shewanella oneidensis MR-1 in a batch culture. Based on a series of analyses, including residual analysis, scaled sensitivity coefficient analysis, parameter correlation analysis and the F-test, we estimated model parameters to construct a parsimonious Monod-based model that used the fewest parameters for best simulation of Shewanella growth using different carbon substrates. Sequential analysis was also applied to identify the time window for estimating each parameter in the kinetic model. This study shows that statistics-based parameter estimation is an efficient method to successively reconstruct and fine tune kinetic models for complex biological systems. IntroductionThe relation between microbial growth rates and substrate concentrations (often referred to as microbial growth kinetics) has been studied and substantiated for over a century.[1] Numerous models, including the Monod equation, [2] have been established to describe the growth kinetics. In spite of the wide applications of the Monod model, it does not always lead to a good description of a given bioprocess. One criticism of the Monod model is that the two parameters in the model, that is, the maximum specific growth rate (μ max ) and substrate saturation constant (K s ), can be correlated in estimation, which results in reported values that differ by several orders of magnitude.[1] In addition, the complicated culture conditions (e.g. growth with mixed substrates rather than a single substrate) make the original Monod model insufficient to reflect every aspect of growth kinetics. To overcome this problem, the model has to be refined by introducing

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Parametric sensitivity analysis and reduction of a detailed nutritional model of plant cell cultures

Cited by 12 publications

References 11 publications

Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform

Analyzing Clonal Variation of Monoclonal Antibody-Producing CHO Cell Lines Using an In Silico Metabolomic Platform

Parameter identification and state estimation of a microalgae dynamical model in sulphur deprived conditions: Global sensitivity analysis, optimization criterion, extended Kalman filter

Construction of a parsimonious kinetic model to capture microbial dynamics via parameter estimation

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