An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks

Maeda, Kazuhiro; Fukano, Yuya; Yamamichi, Shunsuke; Nitta, Daichi; Kurata, Hiroyuki

doi:10.1007/s00449-010-0486-7

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Since the number of undetermined parameters increases with increased network size, the values of many parameters must be estimated so that the model can reproduce the experimental data. Massive calculation power with constrained evolutionary search methods [ 35 , 36 ] was required to estimate the unknown values of many parameters. We iterated 4 × 10 7 simulations to estimate 351 parameters using a genetic algorithm over 8 days with 101 cores of Intel Xeon E5-2670 v3 2.3 GHz on a super computer.…”

Section: Discussionmentioning

confidence: 99%

“…The parameter estimation problem is formulated as a constrained optimization problem: where is the objective function that evaluates the difference between the estimated parameters and literature-based parameters. consists of multiple if–then rule-based score functions that evaluate whether the simulated behavior is consistent with experimental data [ 35 ]. We categorized search parameters into three groups: Classes I, II and III.…”

Section: Methodsmentioning

confidence: 99%

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Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

et al. 2016

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BackgroundA kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters.ResultsIn this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture.ConclusionsThis model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-016-0511-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

et al. 2016

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“…Multiple and conflicting objectives appear often in the design and optimisation of dynamic bioprocesses (e.g., [12,23,24,33,39]). The resulting multi-objective optimisation problems yield a set of so-called Pareto optimal solutions instead of one single optimal solution in singleobjective optimisation problems [29].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal control of dynamic bioprocesses using ACADO Toolkit

Logist

Telen

Houska

et al. 2012

Bioprocess Biosyst Eng

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The optimal design and operation of dynamic bioprocesses gives in practice often rise to optimisation problems with multiple and conflicting objectives. As a result typically not a single optimal solution but a set of Pareto optimal solutions exist. From this set of Pareto optimal solutions, one has to be chosen by the decision maker. Hence, efficient approaches are required for a fast and accurate generation of the Pareto set such that the decision maker can easily and systematically evaluate optimal alternatives. In the current paper the multi-objective optimisation of several dynamic bioprocess examples is performed using the freely available ACADO Multi-Objective Toolkit ( http://www.acadotoolkit.org ). This toolkit integrates efficient multiple objective scalarisation strategies (e.g., Normal Boundary Intersection and (Enhanced) Normalised Normal Constraint) with fast deterministic approaches for dynamic optimisation (e.g., single and multiple shooting). It has been found that the toolkit is able to efficiently and accurately produce the Pareto sets for all bioprocess examples. The resulting Pareto sets are added as supplementary material to this paper.

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“…By repeating the selection and reproduction operations, RC-GAs evolve the population and eventually obtain the individuals (the sets of model parameters) that provide an optimal fit for the experimental data. Two RCGAs, UNDX/MGG and REX star /JGG, have been previously employed for parameter estimation [20], [21], [22], [23], [24], [25], [26], [27]. UNDX/MGG prevents premature convergence (population trapped in the local optima in the early stage of the search), whereas REX star /JGG evaluates the search space and moves the population in a favorable direction.…”

Section: Rcgasmentioning

confidence: 99%

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

Maeda

Boogerd

Kurata

2021

IPSJ Transactions on Bioinformatics

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Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REX star /JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

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An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks

Cited by 13 publications

References 41 publications

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli

Multi-objective optimal control of dynamic bioprocesses using ACADO Toolkit

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models

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