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Qian J, Ye C. Development and applications of genome-scale metabolic network models. ADVANCES IN APPLIED MICROBIOLOGY 2024; 126:1-26. [PMID: 38637105 DOI: 10.1016/bs.aambs.2024.02.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
The genome-scale metabolic network model is an effective tool for characterizing the gene-protein-response relationship in the entire metabolic pathway of an organism. By combining various algorithms, the genome-scale metabolic network model can effectively simulate the influence of a specific environment on the physiological state of cells, optimize the culture conditions of strains, and predict the targets of genetic modification to achieve targeted modification of strains. In this review, we summarize the whole process of model building, sort out the various tools that may be involved in the model building process, and explain the role of various algorithms in model analysis. In addition, we also summarized the application of GSMM in network characteristics, cell phenotypes, metabolic engineering, etc. Finally, we discuss the current challenges facing GSMM.
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Affiliation(s)
- Jinyi Qian
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China
| | - Chao Ye
- Ministry of Education Key Laboratory of NSLSCS, Nanjing Normal University, Nanjing, PR China; School of Food Science and Pharmaceutical Engineering, Nanjing Normal University, Nanjing, PR China.
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2
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Kishk A, Pacheco MP, Sauter T. DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling. iScience 2021; 24:103331. [PMID: 34723158 PMCID: PMC8536485 DOI: 10.1016/j.isci.2021.103331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/15/2022] Open
Abstract
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
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Affiliation(s)
- Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, Ibrahim Z, Napis S, Sinnott RO. Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli. J Integr Bioinform 2020; 17:jib-2019-0073. [PMID: 32374287 PMCID: PMC7734505 DOI: 10.1515/jib-2019-0073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 01/18/2020] [Indexed: 11/15/2022] Open
Abstract
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
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Affiliation(s)
- Mee K Lee
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu Kelantan, Malaysia.,Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600 Jeli Kelantan, Malaysia
| | - Yee Wen Choon
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Kauthar Mohd Daud
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Nurul Athirah Nasarudin
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Arfian Ismail
- Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Pahang, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Suhaimi Napis
- Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Richard O Sinnott
- School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3052 Australia
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Arif MA, Mohamad MS, Abd Latif MS, Deris S, Remli MA, Mohd Daud K, Ibrahim Z, Omatu S, Corchado JM. A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models. Comput Biol Med 2018; 102:112-119. [DOI: 10.1016/j.compbiomed.2018.09.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 09/16/2018] [Accepted: 09/16/2018] [Indexed: 10/28/2022]
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5
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Mutturi S. FOCuS: a metaheuristic algorithm for computing knockouts from genome-scale models for strain optimization. MOLECULAR BIOSYSTEMS 2018; 13:1355-1363. [PMID: 28530276 DOI: 10.1039/c7mb00204a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Although handful tools are available for constraint-based flux analysis to generate knockout strains, most of these are either based on bilevel-MIP or its modifications. However, metaheuristic approaches that are known for their flexibility and scalability have been less studied. Moreover, in the existing tools, sectioning of search space to find optimal knocks has not been considered. Herein, a novel computational procedure, termed as FOCuS (Flower-pOllination coupled Clonal Selection algorithm), was developed to find the optimal reaction knockouts from a metabolic network to maximize the production of specific metabolites. FOCuS derives its benefits from nature-inspired flower pollination algorithm and artificial immune system-inspired clonal selection algorithm to converge to an optimal solution. To evaluate the performance of FOCuS, reported results obtained from both MIP and other metaheuristic-based tools were compared in selected case studies. The results demonstrated the robustness of FOCuS irrespective of the size of metabolic network and number of knockouts. Moreover, sectioning of search space coupled with pooling of priority reactions based on their contribution to objective function for generating smaller search space significantly reduced the computational time.
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Affiliation(s)
- Sarma Mutturi
- Department of Microbiology and Fermentation Technology, CSIR - Central Food Technological Research Institute, Mysuru 570 020, Karnataka, India.
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6
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Advances in analytical tools for high throughput strain engineering. Curr Opin Biotechnol 2018; 54:33-40. [PMID: 29448095 DOI: 10.1016/j.copbio.2018.01.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 01/24/2018] [Accepted: 01/28/2018] [Indexed: 01/09/2023]
Abstract
The emergence of inexpensive, base-perfect genome editing is revolutionising biology. Modern industrial biotechnology exploits the advances in genome editing in combination with automation, analytics and data integration to build high-throughput automated strain engineering pipelines also known as biofoundries. Biofoundries replace the slow and inconsistent artisanal processes used to build microbial cell factories with an automated design-build-test cycle, considerably reducing the time needed to deliver commercially viable strains. Testing and hence learning remains relatively shallow, but recent advances in analytical chemistry promise to increase the depth of characterization possible. Analytics combined with models of cellular physiology in automated systems biology pipelines should enable deeper learning and hence a steeper pitch of the learning cycle. This review explores the progress, advances and remaining bottlenecks of analytical tools for high throughput strain engineering.
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7
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Park SY, Yang D, Ha SH, Lee SY. Metabolic Engineering of Microorganisms for the Production of Natural Compounds. ACTA ACUST UNITED AC 2017. [DOI: 10.1002/adbi.201700190] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Seon Young Park
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Dongsoo Yang
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Shin Hee Ha
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory; Department of Chemical and Biomolecular Engineering (BK21 Plus Program); Institute for the BioCentury; Korea Advanced Institute of Science and Technology (KAIST); Daejeon 34141 Republic of Korea
- BioProcess Engineering Research Center; KAIST; Daejeon 34141 Republic of Korea
- BioInformatics Research Center; KAIST; Daejeon 34141 Republic of Korea
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8
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Ye C, Xu N, Dong C, Ye Y, Zou X, Chen X, Guo F, Liu L. IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models. Sci Rep 2017; 7:727. [PMID: 28389638 PMCID: PMC5429687 DOI: 10.1038/s41598-017-00820-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 03/16/2017] [Indexed: 11/12/2022] Open
Abstract
Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.
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Affiliation(s)
- Chao Ye
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Nan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Chuan Dong
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, No. 4, 2nd Section, North Jianshe Road, Chengdu, Sichuan, 610054, China
| | - Yuannong Ye
- School of Biology and Engineering, Guizhou Medical University, Dongqing Road, Huaxi District, Guiyang, Guizhou, 550025, China
- School of Big Health, Guizhou Medical University, Dongqing Road, Huaxi District, Guiyang, Guizhou, 550025, China
| | - Xuan Zou
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Xiulai Chen
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China
| | - Fengbiao Guo
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, No. 4, 2nd Section, North Jianshe Road, Chengdu, Sichuan, 610054, China
| | - Liming Liu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
- The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu, 214122, China.
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9
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Application of theoretical methods to increase succinate production in engineered strains. Bioprocess Biosyst Eng 2016; 40:479-497. [PMID: 28040871 DOI: 10.1007/s00449-016-1729-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 12/16/2016] [Indexed: 12/19/2022]
Abstract
Computational methods have enabled the discovery of non-intuitive strategies to enhance the production of a variety of target molecules. In the case of succinate production, reviews covering the topic have not yet analyzed the impact and future potential that such methods may have. In this work, we review the application of computational methods to the production of succinic acid. We found that while a total of 26 theoretical studies were published between 2002 and 2016, only 10 studies reported the successful experimental implementation of any kind of theoretical knowledge. None of the experimental studies reported an exact application of the computational predictions. However, the combination of computational analysis with complementary strategies, such as directed evolution and comparative genome analysis, serves as a proof of concept and demonstrates that successful metabolic engineering can be guided by rational computational methods.
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10
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Carbonell P, Currin A, Jervis AJ, Rattray NJW, Swainston N, Yan C, Takano E, Breitling R. Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle. Nat Prod Rep 2016; 33:925-32. [PMID: 27185383 PMCID: PMC5063057 DOI: 10.1039/c6np00018e] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/11/2022]
Abstract
Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.
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Affiliation(s)
- Pablo Carbonell
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Andrew Currin
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Adrian J. Jervis
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Nicholas J. W. Rattray
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Neil Swainston
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Cunyu Yan
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Eriko Takano
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
| | - Rainer Breitling
- Manchester Centre for Fine and Specialty Chemicals (SYNBIOCHEM) , Manchester Institute of Biotechnology , University of Manchester , Manchester M1 7DN , UK . ;
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11
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Wu SG, Shimizu K, Tang JKH, Tang YJ. Facilitate Collaborations among Synthetic Biology, Metabolic Engineering and Machine Learning. CHEMBIOENG REVIEWS 2016. [DOI: 10.1002/cben.201500024] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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12
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Liu R, Bassalo MC, Zeitoun RI, Gill RT. Genome scale engineering techniques for metabolic engineering. Metab Eng 2015; 32:143-154. [PMID: 26453944 DOI: 10.1016/j.ymben.2015.09.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 08/15/2015] [Accepted: 09/02/2015] [Indexed: 12/18/2022]
Abstract
Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.
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Affiliation(s)
- Rongming Liu
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Marcelo C Bassalo
- Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ramsey I Zeitoun
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
| | - Ryan T Gill
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO 80309, United States.
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Co-evolution of strain design methods based on flux balance and elementary mode analysis. Metab Eng Commun 2015; 2:85-92. [PMID: 34150512 PMCID: PMC8193246 DOI: 10.1016/j.meteno.2015.04.001] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 04/17/2015] [Accepted: 04/29/2015] [Indexed: 01/16/2023] Open
Abstract
More than a decade ago, the first genome-scale metabolic models for two of the most relevant microbes for biotechnology applications, Escherichia coli and Saccaromyces cerevisiae, were published. Shortly after followed the publication of OptKnock, the first strain design method using bilevel optimization to couple cellular growth with the production of a target product. This initiated the development of a family of strain design methods based on the concept of flux balance analysis. Another family of strain design methods, based on the concept of elementary mode analysis, has also been growing. Although the computation of elementary modes is hindered by computational complexity, recent breakthroughs have allowed applying elementary mode analysis at the genome scale. Here we review and compare strain design methods and look back at the last 10 years of in silico strain design with constraint-based models. We highlight some features of the different approaches and discuss the utilization of these methods in successful in vivo metabolic engineering applications. Computational strain design methods are divided into two main families. We trace the evolutionary history of these two families. Surveyed successful cases of model-guided strain design for industrial applications. Most proposed methods have not yet been tested in real applications. Agreement between in silico and in vivo results shows potential of tested methods.
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Identifying a gene knockout strategy using a hybrid of the bat algorithm and flux balance analysis to enhance the production of succinate and lactate in Escherichia coli. BIOTECHNOL BIOPROC E 2015. [DOI: 10.1007/s12257-014-0466-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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15
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aCGH-MAS: analysis of aCGH by means of multiagent system. BIOMED RESEARCH INTERNATIONAL 2015; 2015:194624. [PMID: 25874203 PMCID: PMC4385609 DOI: 10.1155/2015/194624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2014] [Revised: 10/31/2014] [Accepted: 11/17/2014] [Indexed: 12/02/2022]
Abstract
There are currently different techniques, such as CGH arrays, to study genetic variations in patients. CGH arrays analyze gains and losses in different regions in the chromosome. Regions with gains or losses in pathologies are important for selecting relevant genes or CNVs (copy-number variations) associated with the variations detected within chromosomes. Information corresponding to mutations, genes, proteins, variations, CNVs, and diseases can be found in different databases and it would be of interest to incorporate information of different sources to extract relevant information. This work proposes a multiagent system to manage the information of aCGH arrays, with the aim of providing an intuitive and extensible system to analyze and interpret the results. The agent roles integrate statistical techniques to select relevant variations and visualization techniques for the interpretation of the final results and to extract relevant information from different sources of information by applying a CBR system.
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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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