S-system approach to modeling recombinant Escherichia coli growth by hybrid differential evolution with data collocation

Ko, Chih-Lung; Wang, Feng‐Sheng; Chao, Yun-Peng; Chen, Te-Wei

doi:10.1016/j.bej.2005.08.032

Cited by 22 publications

(18 citation statements)

References 23 publications

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“…In this section, the identifiability analyses explained above are applied to two inverse modeling problems: a GMA model of L. lactis and an S-system model of E. coli metabolism (Ko et al, 2006). The quantity and quality of the data in these models arguably represent the best-case scenario of an inverse modeling problem in this area, where time-series concentrations of all metabolites within the subsystem are available and the model dimensionality is small (<10).…”

Section: Resultsmentioning

confidence: 99%

“…The second case study describes the growth of a recombinant E. coli strain BL21/pBAW2 under two initial glucose concentrations (40 g/L and 50 g/L) (Ko et al, 2006). The S-system model consists of 5 state variables, all of which were measured until glucose was fully consumed at 1 h −1 sampling rate (up to 14 h).…”

Section: Case Study 2: Modeling Recombinant E Coli Growthmentioning

confidence: 99%

“…This paper aims to fill this gap. In particular, existing and newly developed methods are applied to investigate the parameter identifiability in two previously published power-law models of metabolic networks: lactate production in L. lactis (GMA) and E. coli metabolism (S-system) (Ko et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Parameter identifiability of power-law biochemical system models

Sridharan

2010

Journal of Biotechnology

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Section: Resultsmentioning

confidence: 99%

Section: Case Study 2: Modeling Recombinant E Coli Growthmentioning

confidence: 99%

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Parameter identifiability of power-law biochemical system models

Sridharan

2010

Journal of Biotechnology

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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

146

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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“…Optimization therefore requires a reliable dynamic model of the process, which typically includes rate equations for biomass formation, substrate consumption and product formation. Such kinetic models are generally estimated through batch fermentation processes, and are then applied to a fed-batch process to identify the optimal control policy [5][6][7][8][9][10][11][12][13][14]. A dilution term is included in the governed material balance equations for the fed-batch fermentation process.…”

Section: Introductionmentioning

confidence: 99%

Run‐to‐Run Optimization of Fed‐Batch Fermentation for Ethanol Production

2010

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A two-stage design approach is introduced to determine the optimal feed rate, fed glucose concentration and fermentation time to maximize ethanol productivity by using Saccharomyces diastaticus LORRE 316. The first stage is applied to determine a kinetic model using batch time-series data. Two runs were carried out in the second stage to achieve the maximum ethanol production rate for the fedbatch fermentation process. The computational results from the kinetic model using batch and fed-batch time-series data are in better agreement with the fedbatch experiments than those from the kinetic model using batch time-series data. For cross-validation, an extra fed-batch experiment with constant feed rate was carried out for comparison with the optimal fed-batch result. According to the experimental results, this optimal approach was able to improve productivity by at least 12.56 % compared with that using the constant feed rate.

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S-system approach to modeling recombinant Escherichia coli growth by hybrid differential evolution with data collocation

Cited by 22 publications

References 23 publications

Parameter identifiability of power-law biochemical system models

Parameter identifiability of power-law biochemical system models

Biochemical Systems Theory: A Review

Run‐to‐Run Optimization of Fed‐Batch Fermentation for Ethanol Production

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