A measure of concentration robustness in a biochemical reaction network and its application on system identification

Ye, Jianxiong; Li, An; Zhai, Jingang

doi:10.1016/j.apm.2017.07.026

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Biological robustness is a basic characterization of biological systems, which has also been accepted by researchers in the field [19,24,25,34]. Many researchers have tried to give a quantitative definition of biological robustness and achieved some desired results [32,40]. The core idea of quantitative definition is to quantify the changes in system performance by perturbing system parameters.…”

Section: (Communicated By Changzhi Wu)mentioning

confidence: 99%

Identification and robustness analysis of nonlinear hybrid dynamical system of genetic regulation in continuous culture

Qi¹,

Wang²,

Feng³

et al. 2020

Journal of Industrial &Amp; Management Optimization

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Section: (Communicated By Changzhi Wu)mentioning

confidence: 99%

Identification and robustness analysis of nonlinear hybrid dynamical system of genetic regulation in continuous culture

Qi¹,

Wang²,

Feng³

et al. 2020

Journal of Industrial &Amp; Management Optimization

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“…Sun et al [5] presented a fourteendimensional nonlinear dynamic system to describe the continuous fermentation and multiplicity analysis, considering two regulated negative-feedback mechanisms of repression and enzyme inhibition. Ye et al [6] studied the concentration robustness of this fourteen-dimensional glycerol metabolism system and proposed a robustness index to measure the concentration robustness of the considered system. By defining the time-varying metabolic network structure as an integer-valued function, Wang et al [7] modeled glycerol metabolism in continuous fermentation as a fourteen-dimensional nonlinear mixedinteger dynamic system and identified the dynamic network structure and kinetic parameters.…”

Section: Introductionmentioning

confidence: 99%

“…By defining the time-varying metabolic network structure as an integer-valued function, Wang et al [7] modeled glycerol metabolism in continuous fermentation as a fourteen-dimensional nonlinear mixedinteger dynamic system and identified the dynamic network structure and kinetic parameters. In above works [5,6,7], dilution rate of the glycerol was both considered as a constant. However, the demand of glycerol may vary at different fermentation stages [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Intelligence Algorithms in Continuous Fermentation of Glycerol

Wang

Ruan

2021

IEEE Access

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The bioconversion of 1,3-propanediol from glycerol by Klebsiella pneumoniae can be described by a nonlinear dynamic system. Some work has been done on the identification and optimization of the system, in which the dilution rate of glycerol is considered as a constant. However, the demand of glycerol may vary at different fermentation stages, it is reasonable to view the glycerol metabolic system with dilution rate varying with time. In this paper, we model the glycerol metabolic process as a fourteendimensional nonlinear dynamical system, where the dilution rate is considered varying with time. Then an optimal discrete-valued control problem for maximizing the average concentration of 1,3-propanediol in the fermentation process is established. To solve the optimization problem, auxiliary control and an exact penalty function are used to convert this problem into a large-scale parameter optimization problem. For better balancing local and global search ability, a competitive particle swarm algorithm with time-varying control factors is proposed which is proved to be faster and more stable than the traditional competitive particle swarm algorithm. Numerical experiments are conducted to show the rationality, effectiveness and applicability of the method proposed.

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“…Analysis and application of biological robustness as performance index were studied in [26]. A measure of concentration robustness in system identification was proposed in [31]. Various optimal control strategies have also been reported such as bi-objective optimization [7], stochastic optimal control [27], robust optimal controls [8,34], mixed-integer minimax optimization [28] and optimal state-delay control [10].…”

mentioning

confidence: 99%

Multistage optimal control for microbial fed-batch fermentation process

Sun

Gong

et al. 2022

JIMO

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In this paper, we consider multistage optimal control of bioconversion glycerol to 1,3-propanediol(1,3-PD) in fed-batch fermentation process. To maximize the productivity of 1,3-PD, the whole fermentation process is divided into three stages according to the characteristics of microbial growth. Stages 2 and 3 are discussed mainly. The main aim of stage 2 is to restrict accumulation of 3-hydroxypropionaldehyde and maximize the biomass in the shortest time, and the purpose of stage 3 is to get high productivity of 1,3-PD. With these different objectives, multi-objective optimal control problems are proposed in stages 2 and 3. In order to solve the above optimal control problems, the multi-objective problems are transformed to the corresponding single-objective problems using the mass balance equation of biomass and normalization of the objective. Furthermore, the single-objective optimal control problems are transformed to two-level optimization problems by the control parametrization technique. Finally, numerical solution methods combined an improved Particle Swarm Optimization with penalty function method are developed to solve the resulting optimization problems. Numerical results show that the productivity of 1,3-PD is higher than the reported results.

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A measure of concentration robustness in a biochemical reaction network and its application on system identification

Cited by 11 publications

References 27 publications

Identification and robustness analysis of nonlinear hybrid dynamical system of genetic regulation in continuous culture

Identification and robustness analysis of nonlinear hybrid dynamical system of genetic regulation in continuous culture

Design and Application of Intelligence Algorithms in Continuous Fermentation of Glycerol

Multistage optimal control for microbial fed-batch fermentation process

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