On-line estimation of biomass and intracellular protein for recombinant Escherichia coli cultivated in batch and fed-batch modes

Ko, Chih-Lung; Wang, Feng‐Sheng

doi:10.1016/j.jcice.2007.04.005

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…The state variables defined were biomass concentration X (g L –1 ), substrate concentrations, S b and S f , for the batch and fed-batch phase (g L –1 ), respectively, DO concentration, DO (%), oxygen in the gas phase, O 2 (%), and product concentration, P (g L –1 ). The substrate mass balance for S b was active during the batch phase (0– t b ), and the mass balance for S f was active during the fed-batch phase ( t b – t f ), where S b and S f represent the concentration of the two substrates, glucose and glycerol, in the batch and fed-batch phase, respectively Equations – represent the model equations. where μ b , μ f , q S b , q S f , q O 2,b , q O 2,f , and q P are the specific rates of biomass growth (h –1 ), substrate consumption (g g –1 h –1 ), oxygen consumption (g g –1 h –1 ), and product formation (g g –1 h –1 ), respectively, and the subscripts b and f refer to the batch phase and fed-batch phase, respectively.…”

Section: Modeling and Optimization For E Coli Fermentationmentioning

confidence: 99%

“…The highly complex nature of biological processes and dynamic nonlinearity in the responses are the major factors limiting the development of a reliable process model. In spite of its demerits, the model development for a therapeutic protein production process garners much attention owing to its prediction capability and has been attempted by many researchers. − Additional challenge includes lack of availability of reliable sensors to measure all the process variables, and a pertinent solution would be the development of soft sensors. ,, In brief, application of advanced monitoring tools and a well-designed process model could significantly enhance consistency in the process and product quality through appropriate real-time control actions. ,, …”

Section: Introductionmentioning

confidence: 99%

“…9−11 Additional challenge includes lack of availability of reliable sensors to measure all the process variables, and a pertinent solution would be the development of soft sensors. 10,12,13 In brief, application of advanced monitoring tools and a well-designed process model could significantly enhance consistency in the process and product quality through appropriate real-time control actions. 3,14,15 The bioreactor operation is essentially the most important step in the recombinant protein production process, as the physiological changes in the biomass growth conditions are considered to be closely related to the product quantity and quality.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization for Enhanced Production of Therapeutic Proteins in Escherichia coli: Application of Real-Time Dielectric Spectroscopy

Swaminathan

Priyanka

Rathore

et al. 2020

Ind. Eng. Chem. Res.

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This study focuses on optimization of the substrate feeding profile for enhanced production of ranibizumab, a recombinant therapeutic protein, in Escherichia coli. An established process analyzer, dielectric spectroscopy, was deployed to monitor the production process and to estimate real-time biomass. A validated mechanistic model was employed to formulate a multiobjective optimization (MOO) problem. The substrate flow rate during the fed-batch phase (F) was taken as the decision variable for MOO. The Pareto front resulting from MOO revealed that for a minimum broth volume (V) of 1.96 L, a maximum of 58.8 g of total biomass (XV) could be generated. The total biomass obtained from the optimal substrate feeding profile was 20.6% higher than the experimentally achieved total biomass. Enhanced productivity was achieved by the proposed MOO formulation, which facilitates the choice of any operating point from the Pareto front based on downstream expenses of the therapeutic product.

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Section: Modeling and Optimization For E Coli Fermentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization for Enhanced Production of Therapeutic Proteins in Escherichia coli: Application of Real-Time Dielectric Spectroscopy

Swaminathan

Priyanka

Rathore

et al. 2020

Ind. Eng. Chem. Res.

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“…Conceptually similar modeling studies addressed the production of amino acids, enzymes, proteins, and other valuable organics in E. coli [58,[521][522][523]. Lall and Mitchell used an S-system model to characterize the reduction of metal in the bacterium Shewanella oneidensis [524].…”

Section: Microbial Studiesmentioning

confidence: 99%

Biochemical Systems Theory: A Review

Voit

2013

ISRN Biomathematics

149

154

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Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. is paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. e paper is intended as a guide for investigators entering the fascinating �eld of biological systems analysis and as a resource for practitioners and experts. PreambleBiochemical systems theory (BST) is a mathematical and computational framework for analyzing and simulating systems. It was originally developed for biochemical pathways but by now has become much more widely applied to systems throughout biology and beyond. BST is called "canonical, " which means that model construction, diagnosis, and analysis follow stringent rules, which will be discussed throughout this paper. e key ingredient of BST is the power-law representation of all processes in a system.BST has been the focus of a number of books [1-6], including some in Chinese and Japanese [7][8][9]. e most detailed modern text dedicated speci�cally to BST is [3]. Moreover, since its inception, numerous reviews have portrayed the evolving state of the art in BST. Most of these reviews summarized methodological advances for the analysis of biochemical pathway systems . Others compared BST models with alternative modeling frameworks [32,[51][52][53][54][55][56][57][58][59][60][61][62][63]; some of these comparisons will be discussed later. Yet other reviews focused on specialty areas, such as customized methods for optimizing BST models (e.g., [64][65][66][67]) or estimating their parameters (e.g., [68][69][70][71][72]), strategies for discovering design and operating principles in natural system (e.g., [73][74][75][76][77][78]), and even the use of BST models in statistics [79][80][81]. A historical account of the �rst twenty years of BST was presented in [82].Supporting the methodological developments in the �eld, several soware packages were developed for different aspects of BST analysis. e earliest was ESSYNS [83][84][85], which supported all standard steady-state analyses and also contained a numerical solver that, at the time, was many times faster than any off-the-shelf soware [86,87]; see also [88]. Utilizing advances in computing and an extension of the solver from ESSYNS, Ferreira created the very user-friendly package PLAS, which is openly available and still very widely used [89] (see also [3]). Yamada and colleagues developed soware to translate E-cell models into BST models in PLAS format [90]. Okamoto's group created the soware BestKit, which permits the graphical design and translation of models, along with their analysis [91][92][93]. Vera and colleagues developed the soware tool PLMaddon for analyzing BST models within SBML [94]; see also [95]. It expands the Matlab SBToolbox with speci�c functionalities for power-law repr...

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“…Neural networks, which were developed by analogy with the functioning of neurons in living beings (Ko and Wang, 2007;Works, 1989), constitute one other powerful tool for modelling complex systems. The most salient feature of neural networks is their ability to exactly map non-linear behaviour via a series of input (independent variable) and output (dependent variable) data without the need for an exact knowledge of the functional relationships between the two data sets (Emmanouilides and Petyrou, 1997).…”

Section: Introductionmentioning

confidence: 99%