Identification of bottleneck enzymes with negative dynamic sensitivities: Ethanol fermentation systems as case studies

Sriyudthsak, Kansuporn; Shiraishi, Fumihidé

doi:10.1016/j.jbiotec.2010.01.015

Cited by 8 publications

(17 citation statements)

References 27 publications

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“…Identification of the bottleneck enzyme is necessary for this purpose. The BR indicator expresses the degree of the “bottleneck” and can be used to identify the bottleneck enzyme (Sriyudthsak and Shiraishi, 2010a , b , c ;Sriyudthsak et al, 2015 ). The BR indicator is calculated by:

…”

Section: Systems Analysismentioning

confidence: 99%

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

Sriyudthsak

Shiraishi

Hirai

2016

Front. Mol. Biosci.

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The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.

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…”

Section: Systems Analysismentioning

confidence: 99%

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

Sriyudthsak

Shiraishi

Hirai

2016

Front. Mol. Biosci.

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“…Shi and colleagues studied the balance between robustness and fragility in biochemical networks [406]. Several articles expanded the concepts of gains and sensitivities to questions of dynamics [124][125][126][127][407][408][409][410][411][412]. ese analyses assess how changes in initial values affect the transient responses of a system.…”

Section: Sensitivity and Robustnessmentioning

confidence: 99%

“…In particular, it showed how traditional enzyme kinetic information can be validly converted into power-law models that offer additional modes of analysis and novel insights. is more complex model was later used as a test bed for various optimization and estimation [3,4,255,319,409,[510][511][512][513][514][515][516][517][518][519][520].…”

Section: Microbial Studiesmentioning

confidence: 99%

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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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“…[7][8][9][10][11][12][13][14][15][16][17] It is therefore essential to establish an easy method for calculation of the dynamic sensitivities with always-reliable accuracy. If the system has a steady state, the sensitivities can be efficiently calculated by using biochemical systems theory.…”

Section: Introductionmentioning

confidence: 99%

Highly reliable computation of dynamic sensitivities in metabolic reaction systems by a variable‐step Taylor series method

Shiraishi

Egashira

Iwata

et al. 2011

Asia-Pacific J Chem Eng

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Dynamic sensitivities are useful to characterize time-varying systems efficiently. In metabolic reaction systems, however, analysis using these values is not so popular. This is mainly due to the following two reasons. One is that the calculation of dynamic sensitivities requires us to derive differential equations for sensitivities from those for metabolite concentrations by partial differentiation, and it is not easy for experimentalists to perform this mathematical operation. The other is that the metabolic reaction systems are mostly described by stiff differential equations, from which it may not be easy to obtain reliable numerical solutions. We have previously developed software for calculation of dynamic sensitivities (SOFTCADS), in which one can calculate dynamic sensitivities with high accuracy by setting only differential equations for metabolite concentrations. This paper further improves the algorithm of SOFTCADS to enhance its performance. The results clearly show that regardless of the degree of stiffness, the improved SOFTCADS provides dynamic sensitivities with the super high accuracy that is comparable to the machine accuracy and also completes the calculation in a shorter time.

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Identification of bottleneck enzymes with negative dynamic sensitivities: Ethanol fermentation systems as case studies

Cited by 8 publications

References 27 publications

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

Mathematical Modeling and Dynamic Simulation of Metabolic Reaction Systems Using Metabolome Time Series Data

Biochemical Systems Theory: A Review

Highly reliable computation of dynamic sensitivities in metabolic reaction systems by a variable‐step Taylor series method

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