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Yang L, Li J, Zhang Y, Chen L, Ouyang Z, Liao D, Zhao F, Han S. Characterization of the enzyme kinetics of EMP and HMP pathway in Corynebacterium glutamicum: reference for modeling metabolic networks. Front Bioeng Biotechnol 2023; 11:1296880. [PMID: 38090711 PMCID: PMC10713844 DOI: 10.3389/fbioe.2023.1296880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/13/2023] [Indexed: 04/04/2024] Open
Abstract
The model of intracellular metabolic network based on enzyme kinetics parameters plays an important role in understanding the intracellular metabolic process of Corynebacterium glutamicum, and constructing such a model requires a large number of enzymological parameters. In this work, the genes encoding the relevant enzymes of the EMP and HMP metabolic pathways from Corynebacterium glutamicum ATCC 13032 were cloned, and engineered strains for protein expression with E.coli BL21 and P.pastoris X33 as hosts were constructed. The twelve enzymes (GLK, GPI, TPI, GAPDH, PGK, PMGA, ENO, ZWF, RPI, RPE, TKT, and TAL) were successfully expressed and purified by Ni2+ chelate affinity chromatography in their active forms. In addition, the kinetic parameters (V max, K m, and K cat) of these enzymes were measured and calculated at the same pH and temperature. The kinetic parameters of enzymes associated with EMP and the HMP pathway were determined systematically and completely for the first time in C.glutamicum. These kinetic parameters enable the prediction of key enzymes and rate-limiting steps within the metabolic pathway, and support the construction of a metabolic network model for important metabolic pathways in C.glutamicum. Such analyses and models aid in understanding the metabolic behavior of the organism and can guide the efficient production of high-value chemicals using C.glutamicum as a host.
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Affiliation(s)
- Liu Yang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Junyi Li
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Yaping Zhang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Linlin Chen
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Zhilin Ouyang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Daocheng Liao
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Fengguang Zhao
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
- School of Light Industry and Engineering, South China University of Technology, Guangzhou, China
| | - Shuangyan Han
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
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Estimation of Small-Scale Kinetic Parameters of Escherichia coli (E. coli) Model by Enhanced Segment Particle Swarm Optimization Algorithm ESe-PSO. Processes (Basel) 2023. [DOI: 10.3390/pr11010126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
The ability to create “structured models” of biological simulations is becoming more and more commonplace. Although computer simulations can be used to estimate the model, they are restricted by the lack of experimentally available parameter values, which must be approximated. In this study, an Enhanced Segment Particle Swarm Optimization (ESe-PSO) algorithm that can estimate the values of small-scale kinetic parameters is described and applied to E. coli’s main metabolic network as a model system. The glycolysis, phosphotransferase system, pentose phosphate, the TCA cycle, gluconeogenesis, glyoxylate pathways, and acetate formation pathways of Escherichia coli are represented by the Differential Algebraic Equations (DAE) system for the metabolic network. However, this algorithm uses segments to organize particle movements and the dynamic inertia weight (ω) to increase the algorithm’s exploration and exploitation potential. As an alternative to the state-of-the-art algorithm, this adjustment improves estimation accuracy. The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. As a result of this innovative approach, it was concluded that small-scale and even entire cell kinetic model parameters can be developed.
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Maeda K, Hatae A, Sakai Y, Boogerd FC, Kurata H. MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling. BMC Bioinformatics 2022; 23:455. [PMID: 36319952 PMCID: PMC9624028 DOI: 10.1186/s12859-022-05009-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/26/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (Km), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem). RESULTS To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for Km estimation of kinetic modeling. First, we use a machine learning-based Km predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted Km values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R2 = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping Km values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated Km values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated Km values, which were close to the measured values. CONCLUSIONS MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based Km predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.
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Affiliation(s)
- Kazuhiro Maeda
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Aoi Hatae
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Yukie Sakai
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
| | - Fred C. Boogerd
- grid.12380.380000 0004 1754 9227Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O
- 2 Building, Amsterdam, The Netherlands
| | - Hiroyuki Kurata
- grid.258806.10000 0001 2110 1386Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502 Japan
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4
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An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli. Processes (Basel) 2020. [DOI: 10.3390/pr8080963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.
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Chen B, Yang Y, Liu J. Performance optimization of aircraft deicing equipment based on genetic algorithm. Sci Prog 2020; 103:36850419877735. [PMID: 31829870 PMCID: PMC10453669 DOI: 10.1177/0036850419877735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Using deicing fluids is the main way of aircraft ground deicing, which plays an important role in ensuring flights' safety. However, most of the airports use deicing fluids excessively to ensure the quality and efficiency of aircraft ground deicing, which will not only cause a large amount of deicing fluids wasted but also pollute water resources and the environment. Finding the optimal solution between deicing efficiency and deicing fluids consumption through effective methods is necessary. This article analyzes the energy conversion process of aircraft ground deicing, establishes multi-parameter optimization model for deicing, and optimizes the consumption of deicing fluids. The physical quantity, including the flow rate and the temperature of deicing fluids, is found as the main influence of the deicing time, which is the most concerned problem in the actual operation. Under the precondition of ensuring the deicing efficiency, the optimized parameters such as different ambient temperature, wing area, and icing thickness are obtained by genetic algorithm. The trend between the parameters with the change of environment has also been analyzed. Finally, the actual using condition in the capital airport and the optimized results are compared, and the results show that the usage of deicing fluids reduced 13% to 24%.
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Affiliation(s)
- Bin Chen
- Institute of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
- Ground Support Equipment Research Base, Civil Aviation University of China, Tianjin, China
| | - Yalei Yang
- Institute of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
- Ground Support Equipment Research Base, Civil Aviation University of China, Tianjin, China
| | - Jianhua Liu
- Institute of Electronic Information and Automation, Civil Aviation University of China, Tianjin, China
- Ground Support Equipment Research Base, Civil Aviation University of China, Tianjin, China
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Maeda K, Westerhoff HV, Kurata H, Boogerd FC. Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network. NPJ Syst Biol Appl 2019; 5:14. [PMID: 30993002 PMCID: PMC6461619 DOI: 10.1038/s41540-019-0091-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 03/12/2019] [Indexed: 11/17/2022] Open
Abstract
The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a 'rubber band method' to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.
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Affiliation(s)
- Kazuhiro Maeda
- Frontier Research Academy for Young Researchers, Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
| | - Hans V. Westerhoff
- Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O|2 building, Amsterdam, Netherlands
- Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, Iizuka, Fukuoka Japan
| | - Fred C. Boogerd
- Department of Molecular Cell Biology, Faculty of Science, VU University Amsterdam, O|2 building, Amsterdam, Netherlands
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7
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van Breda W, Hoogendoorn M, Eiben AE, Berking M. Assessment of temporal predictive models for health care using a formal method. Comput Biol Med 2017. [PMID: 28651070 DOI: 10.1016/j.compbiomed.2017.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Recent developments in the field of sensor devices provide new possibilities to measure a variety of health related aspects in a precise and fine-grained manner. Subsequently, more empirical data will be generated than ever before. While this greatly improves the opportunities for creating accurate predictive models, other types of models besides the more traditional machine learning approaches can provide insights into temporal relationships in the data. Models that express temporal relationships between states in a mathematical manner are examples of such models. However, the evaluation methods traditionally used in the field of predictive modeling are not appropriate for those models, making it difficult to distinguish them in terms of validity. Appropriate assessment methodology is therefore necessary to drive the research of mathematical modeling forward. In this paper we investigate the applicability of such a formalized method. The method takes into account important model aspects, namely descriptive and predictive capability, parameter sensitivity and model complexity. As a case study the method is applied to a mathematical model in the domain of mental health, showing that the method generates useful insights into the behavior of the model.
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Affiliation(s)
- Ward van Breda
- VU University Amsterdam, Department of Computer Science, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands.
| | - Mark Hoogendoorn
- VU University Amsterdam, Department of Computer Science, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands
| | - A E Eiben
- VU University Amsterdam, Department of Computer Science, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands
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8
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Stalidzans E, Mozga I, Sulins J, Zikmanis P. Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:978-985. [PMID: 27071188 DOI: 10.1109/tcbb.2016.2550451] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.
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9
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Xiaoyi W, Junyang Y, Yan S, Tingli S, Li W, Jiping X. Research on hybrid mechanism modeling of algal bloom formation in urban lakes and reservoirs. Ecol Modell 2016. [DOI: 10.1016/j.ecolmodel.2016.03.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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10
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Chen YC, Yuan RS, Ao P, Xu MJ, Zhu XM. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach. Phys Rev E 2016; 93:062409. [PMID: 27415300 DOI: 10.1103/physreve.93.062409] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Indexed: 01/21/2023]
Abstract
While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth.
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Affiliation(s)
- Yong-Cong Chen
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.,SmartWin Technology, 67 Tranmere Avenue, Carnegie, VIC 3163, Australia
| | - Ruo-Shi Yuan
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Ping Ao
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Min-Juan Xu
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiao-Mei Zhu
- Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, China.,GeneMath, 5525 27th Avenue N.E., Seattle, Washington 98105, USA
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11
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Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes. Curr Opin Biotechnol 2015; 36:146-53. [PMID: 26342586 DOI: 10.1016/j.copbio.2015.08.019] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 08/10/2015] [Accepted: 08/14/2015] [Indexed: 11/24/2022]
Abstract
The overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area. We introduce here a set of requirements that will ensure the model quality, we examine the current kinetic models with respect to these requirements, and we propose a general workflow for constructing models that satisfy these requirements.
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12
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Adam Kunna M, Abdul Kadir TA, Jaber AS, Odili JB. Large-Scale Kinetic Parameter Identification of Metabolic Network Model of <i>E. coli</i> Using PSO. ACTA ACUST UNITED AC 2015. [DOI: 10.4236/abb.2015.62012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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13
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Khodayari A, Zomorrodi AR, Liao JC, Maranas CD. A kinetic model of Escherichia coli core metabolism satisfying multiple sets of mutant flux data. Metab Eng 2014; 25:50-62. [DOI: 10.1016/j.ymben.2014.05.014] [Citation(s) in RCA: 145] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 04/17/2014] [Accepted: 05/28/2014] [Indexed: 01/27/2023]
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14
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Talukder S, Sen S, Chakraborti P, Metzler R, Banik SK, Chaudhury P. Breathing dynamics based parameter sensitivity analysis of hetero-polymeric DNA. J Chem Phys 2014; 140:125101. [PMID: 24697480 DOI: 10.1063/1.4869112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
We study the parameter sensitivity of hetero-polymeric DNA within the purview of DNA breathing dynamics. The degree of correlation between the mean bubble size and the model parameters is estimated for this purpose for three different DNA sequences. The analysis leads us to a better understanding of the sequence dependent nature of the breathing dynamics of hetero-polymeric DNA. Out of the 14 model parameters for DNA stability in the statistical Poland-Scheraga approach, the hydrogen bond interaction ε(hb)(AT) for an AT base pair and the ring factor ξ turn out to be the most sensitive parameters. In addition, the stacking interaction ε(st)(TA-TA) for an TA-TA nearest neighbor pair of base-pairs is found to be the most sensitive one among all stacking interactions. Moreover, we also establish that the nature of stacking interaction has a deciding effect on the DNA breathing dynamics, not the number of times a particular stacking interaction appears in a sequence. We show that the sensitivity analysis can be used as an effective measure to guide a stochastic optimization technique to find the kinetic rate constants related to the dynamics as opposed to the case where the rate constants are measured using the conventional unbiased way of optimization.
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Affiliation(s)
- Srijeeta Talukder
- Department of Chemistry, University of Calcutta, 92 A P C Road, Kolkata 700 009, India
| | - Shrabani Sen
- Department of Chemistry, University of Calcutta, 92 A P C Road, Kolkata 700 009, India
| | - Prantik Chakraborti
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700 009, India
| | - Ralf Metzler
- Institute for Physics and Astronomy, University of Potsdam, D-14476 Potsdam-Golm, Germany and Physics Department, Tampere University of Technology, FI-33101 Tampere, Finland
| | - Suman K Banik
- Department of Chemistry, Bose Institute, 93/1 A P C Road, Kolkata 700 009, India
| | - Pinaki Chaudhury
- Department of Chemistry, University of Calcutta, 92 A P C Road, Kolkata 700 009, India
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15
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k-OptForce: integrating kinetics with flux balance analysis for strain design. PLoS Comput Biol 2014; 10:e1003487. [PMID: 24586136 PMCID: PMC3930495 DOI: 10.1371/journal.pcbi.1003487] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2013] [Accepted: 01/10/2014] [Indexed: 11/19/2022] Open
Abstract
Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.
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16
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Tian Z, Fauré A, Mori H, Matsuno H. Identification of key regulators in glycogen utilization in E. coli based on the simulations from a hybrid functional Petri net model. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 6:S1. [PMID: 24565082 PMCID: PMC4029488 DOI: 10.1186/1752-0509-7-s6-s1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
BACKGROUND Glycogen and glucose are two sugar sources available during the lag phase of E. coli, but the mechanism that regulates their utilization is still unclear. METHODS Attempting to unveil the relationship between glucose and glycogen, we propose an integrated hybrid functional Petri net (HFPN) model including glycolysis, PTS, glycogen metabolic pathway, and their internal regulatory systems. RESULTS AND CONCLUSIONS By comparing known biological results to this model, basic necessary regulatory mechanism for utilizing glucose and glycogen were identified as a feedback circuit in which HPr and EIIAGlc play key roles. Based on this regulatory HFPN model, we discuss the process of glycogen utilization in E. coli in the context of a systematic understanding of carbohydrate metabolism.
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