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Qi W, Li XX, Guo YH, Bao YZ, Wang N, Luo XG, Yu CD, Zhang TC. Integrated metabonomic-proteomic analysis reveals the effect of glucose stress on metabolic adaptation of Lactococcus lactis ssp. lactis CICC23200. J Dairy Sci 2020; 103:7834-7850. [PMID: 32684472 DOI: 10.3168/jds.2019-17810] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Accepted: 04/14/2020] [Indexed: 12/30/2022]
Abstract
A combined proteomic and metabonomic approach was used to investigate the metabolism of Lactococcus lactis ssp. lactis subjected to glucose stress treatment. A proteomic method was used to determine 1,427 altered proteins, including 278 proteins with increased expression and 255 proteins with decreased expression. A metabonomic approach was adopted to identify 98 altered metabolites, including 62 metabolites with increased expression and 26 metabolites with decreased expression. The integrated analysis indicated that the RNA and DNA mismatch repair process and energy metabolism were enhanced in response to high-glucose stress in L. lactis. Lactococcus lactis responded to glucose stress by up-regulating oxidoreductase activity, which acted on glycosyl bonds, hydrolase activity, and organic acid transmembrane transporter activity. This led to an improvement in the metabolic flux from glucose to pyruvate, lactate, acetate, and maltose. Down-regulation of amino acid transmembrane transporter, aminoacyl-transfer RNA ligase, hydroxymethyl-, formyl-, and related transferase activities resulted in a decrease in the nitrogen metabolism-associated metabolic pathway, which might be related to inhibition of the production of biogenic amines. Overall, we highlight the response of metabolism to glucose stress and provide potential possibilities for the reduced formation of biogenic amines in improved level of sugar in the dairy fermentation industry. Moreover, according to the demand for industrial production, sugar concentration in fermented foods should be higher, or lower, than a set value that is dependent on bacterial strain and biogenic amine yield.
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Affiliation(s)
- Wei Qi
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China.
| | - Xiao-Xue Li
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China
| | - Yao-Hua Guo
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China
| | - Yan-Zhou Bao
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China
| | - Nan Wang
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China
| | - Xue-Gang Luo
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China
| | - Chun-Di Yu
- College of Food Science and Engineering, Qingdao Agricultural University, Qingdao 266109, P.R. China
| | - Tong-Cun Zhang
- State Key Laboratory of Food Nutrition and Safety, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science & Technology, Ministry of Education, Tianjin 300457, P.R. China; National Engineering Laboratory for Industrial Enzymes, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; Tianjin Key Laboratory of Industrial Microbiology, Tianjin University of Science & Technology, Tianjin 300457, P.R. China; College of Biotechnology, Tianjin University of Science & Technology, Tianjin 300457, P.R. 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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Saeed MT, Ahmad J, Baumbach J, Pauling J, Shafi A, Paracha RZ, Hayat A, Ali A. Parameter estimation of qualitative biological regulatory networks on high performance computing hardware. BMC SYSTEMS BIOLOGY 2018; 12:146. [PMID: 30594246 PMCID: PMC6311083 DOI: 10.1186/s12918-018-0670-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 12/04/2018] [Indexed: 12/28/2022]
Abstract
BACKGROUND Biological Regulatory Networks (BRNs) are responsible for developmental and maintenance related functions in organisms. These functions are implemented by the dynamics of BRNs and are sensitive to regulations enforced by specific activators and inhibitors. The logical modeling formalism by René Thomas incorporates this sensitivity with a set of logical parameters modulated by available regulators, varying with time. With the increase in complexity of BRNs in terms of number of entities and their interactions, the task of parameters estimation becomes computationally expensive with existing sequential SMBioNET tool. We extend the existing sequential implementation of SMBioNET by using a data decomposition approach using a Java messaging library called MPJ Express. The approach divides the parameters space into different regions and each region is then explored in parallel on High Performance Computing (HPC) hardware. RESULTS The performance of the parallel approach is evaluated on BRNs of different sizes, and experimental results on multicore and cluster computers showed almost linear speed-up. This parallel code can be executed on a wide range of concurrent hardware including laptops equipped with multicore processors, and specialized distributed memory computer systems. To demonstrate the application of parallel implementation, we selected a case study of Hexosamine Biosynthetic Pathway (HBP) in cancer progression to identify potential therapeutic targets against cancer. A set of logical parameters were computed for HBP model that directs the biological system to a state of recovery. Furthermore, the parameters also suggest a potential therapeutic intervention that restores homeostasis. Additionally, the performance of parallel application was also evaluated on a network (comprising of 23 entities) of Fibroblast Growth Factor Signalling in Drosophila melanogaster. CONCLUSIONS Qualitative modeling framework is widely used for investigating dynamics of biological regulatory networks. However, computation of model parameters in qualitative modeling is computationally intensive. In this work, we presented results of our Java based parallel implementation that provides almost linear speed-up on both multicore and cluster platforms. The parallel implementation is available at https://psmbionet.github.io .
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Affiliation(s)
- Muhammad Tariq Saeed
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Jamil Ahmad
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan. .,UNIVERSITY OF MALAKAND, Chakdara, Khyber Pakhtunkhwa, 18000, Pakistan.
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Maximus-von-Imhof-Forum 3, Freising, 85354, Germany
| | - Josch Pauling
- Computational Lipidomics group, Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan, Maximus-von-Imhof-Forum 3, 85354, Freising, Germany
| | - Aamir Shafi
- Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, Pakistan
| | - Rehan Zafar Paracha
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Asad Hayat
- Research Centre for Modeling and Simulation (RCMS), NUST, Islamabad, 44000, Pakistan
| | - Amjad Ali
- Atta-ur-Rahman School of Applied Bio sciences (ASAB), NUST, Islamabad, 44000, Pakistan
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Bergmann FT, Hoops S, Klahn B, Kummer U, Mendes P, Pahle J, Sahle S. COPASI and its applications in biotechnology. J Biotechnol 2017; 261:215-220. [PMID: 28655634 DOI: 10.1016/j.jbiotec.2017.06.1200] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 06/19/2017] [Accepted: 06/22/2017] [Indexed: 12/28/2022]
Abstract
COPASI is software used for the creation, modification, simulation and computational analysis of kinetic models in various fields. It is open-source, available for all major platforms and provides a user-friendly graphical user interface, but is also controllable via the command line and scripting languages. These are likely reasons for its wide acceptance. We begin this review with a short introduction describing the general approaches and techniques used in computational modeling in the biosciences. Next we introduce the COPASI package, and its capabilities, before looking at typical applications of COPASI in biotechnology.
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Affiliation(s)
| | - Stefan Hoops
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA
| | - Brian Klahn
- Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA
| | - Ursula Kummer
- BioQuant/COS, Heidelberg University, Heidelberg, Germany
| | - Pedro Mendes
- Center for Quantitative Medicine, UConn Health, Farmington, CT, USA
| | - Jürgen Pahle
- BIOMS/BioQuant, Heidelberg University, Heidelberg, Germany
| | - Sven Sahle
- BioQuant/COS, Heidelberg University, Heidelberg, Germany
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Jahan N, Maeda K, Matsuoka Y, Sugimoto Y, Kurata H. Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli. Microb Cell Fact 2016; 15:112. [PMID: 27329289 PMCID: PMC4915146 DOI: 10.1186/s12934-016-0511-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 06/08/2016] [Indexed: 01/17/2023] Open
Abstract
Background A 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. Results In 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. Conclusions This 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 material The 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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Affiliation(s)
- Nusrat Jahan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Kazuhiro Maeda
- Frontier Research Academy for Young Researchers, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata, Kitakyushu, Fukuoka, 804-8550, Japan.,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Yurie Sugimoto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan. .,Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
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Matsuoka Y, Shimizu K. Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. BIORESOUR BIOPROCESS 2015. [DOI: 10.1186/s40643-014-0031-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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Kurata H, Maeda K, Matsuoka Y. Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2014. [DOI: 10.1252/jcej.13we152] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
- Biomedical Informatics R&D Center, Kyushu Institute of Technology
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
| | - Yu Matsuoka
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology
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van Heeswijk WC, Westerhoff HV, Boogerd FC. Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective. Microbiol Mol Biol Rev 2013; 77:628-95. [PMID: 24296575 PMCID: PMC3973380 DOI: 10.1128/mmbr.00025-13] [Citation(s) in RCA: 154] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.
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da Rocha RA, Weschenfelder TA, de Castilhos F, de Souza EM, Huergo LF, Mitchell DA. Mathematical model of the binding of allosteric effectors to the Escherichia coli PII signal transduction protein GlnB. Biochemistry 2013; 52:2683-93. [PMID: 23517273 DOI: 10.1021/bi301659r] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
PII proteins are important regulators of nitrogen metabolism in a wide variety of organisms: the binding of the allosteric effectors ATP, ADP, and 2-oxoglutarate (2-OG) to PII proteins affects their ability to interact with target proteins. We modeled the simultaneous binding of ATP, ADP, and 2-OG to one PII protein, namely GlnB of Escherichia coli, using a modeling approach that allows the prediction of the proportions of individual binding states. Four models with different binding rules were compared. We selected one of these models (that assumes that the binding of the first nucleotide to GlnB makes it harder for subsequent nucleotides to bind) and used it to explore how physiological concentrations of ATP, ADP, and 2-OG would affect the proportions of those states of GlnB that interact with the target proteins ATase and NtrB. Our simulations indicate that GlnB can, as suggested by previous researchers, act as a sensor of both 2-OG and the ATP:ADP ratio. We conclude that our modeling approach will be an important tool in future studies concerning the PII binding states and their interactions with target proteins.
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Affiliation(s)
- Ricardo Alves da Rocha
- Departamento de Bioquímica e Biologia Molecular, Universidade Federal do Paraná, Cx.P. 19046 Centro Politécnico, Curitiba 81531-980, Paraná, Brazil
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Barnat J, Brim L, Krejcí A, Streck A, Safránek D, Vejnár M, Vejpustek T. On parameter synthesis by parallel model checking. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:693-705. [PMID: 21788679 DOI: 10.1109/tcbb.2011.110] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
An important problem in current computational systems biology is to analyze models of biological systems dynamics under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the algorithm, show its scalability, and examine its applicability on several biological models.
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Affiliation(s)
- Jirí Barnat
- Faculty of Informatics, Masaryk University, Botanická 68a, Brno 60200, Czech Republic.
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Boogerd FC, Ma H, Bruggeman FJ, van Heeswijk WC, García-Contreras R, Molenaar D, Krab K, Westerhoff HV. AmtB-mediated NH3
transport in prokaryotes must be active and as a consequence regulation of transport by GlnK is mandatory to limit futile cycling of NH4+/NH3. FEBS Lett 2010; 585:23-8. [DOI: 10.1016/j.febslet.2010.11.055] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2010] [Revised: 11/29/2010] [Accepted: 11/29/2010] [Indexed: 12/19/2022]
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