Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"565",leadTitle:null,fullTitle:"Breast Reconstruction - Current Techniques",title:"Breast Reconstruction",subtitle:"Current Techniques",reviewType:"peer-reviewed",abstract:"Breast reconstruction is a fascinating and complex field which combines reconstructive and aesthetic principles in the search for the best results possible. 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1. Introduction
Numerical modeling of free surface flow across real-life applications is gaining momentum. These model domains are characterized by thousands of computational cells, and the physical characteristics have varying complexities. Over the last three decades, with the growth of computational and visualization resources, multiple numerical models have been developed for solving the free surface flow equations across one, two, and three dimensions. While some of these models are available for free in the public domain, others are licensed by their respective vendors. Based on the assumptions used in these models, the complexity of flow equations can range from Bernoulli’s energy equation to three-dimensional unsteady Navier-Stokes equations. The models continue to evolve as the physics of flow is better understood, and the need for accurately predicting the flow variables across large spatial and temporal domains as their values is an important factor in the hydraulic design of structures and other related applications. Computational fluid dynamics (CFD) models that focus on solving the complete Navier-Stokes equations, rather than the energy equation or shallow water equations which are used in the hydraulic models, are also gaining popularity among the hydraulic modeling community.
The goal of this chapter is to evaluate the DHM results with a few industry-wide established software and experimental data to underscore the advantages and limitations in the models. To this end, we have chosen one critical application each from one-dimensional and two-dimensional flows.
2. One-dimensional application
2.1 Flows with hydraulic jump
Modeling flows with hydraulic jump where the flow transits from super critical to subcritical has been used by different researchers [1, 2, 3, 4] to test the reliability of their numerical formulations. Hydraulic jump is often created inflows to dissipate the flow energy, which can otherwise among others, erode the channels. They occur in gravity flows and are characterized by a large variation in flow depth and velocity before (Froude number > 1) and after (Froude number < 1) the jump. While capturing the internal flow details like bubble breakup, turbulence characteristics, tracking the water surface, aeration, fluid mixing, and turbulence is not possible using the shallow water equations, these equations can, however, predict the location and the flow depths before and after of the jump at steady state, which are important variables in the design calculations.
2.2 Experimental setup and model variables
A dataset from a series of experiments [5] that were conducted in the hydraulics laboratory at California State University, Fullerton, to simulate the location of steady-state hydraulic jump, was used for validating the models. The rectangular open channel flume was 15.2 m long, 0.46 m wide and 0.6 m in height. The channel sides are of glass, while the bottom interface with water is a metal sheet with a Manning roughness coefficient of 0.01. The bottom slope of the channel can be changed by tilting the flume, and in this investigation, it was set to 0.012. The flow discharge is 0.036 m3/s.
Boundary conditions need to be consistent with the physics of flow and appropriately complement the flow Equations [6]. The number of boundary conditions at the two ends of the flow domain is governed by the local Froude number. From a mathematical perspective, a boundary condition is a constraint imposed at the boundary node to arrive at a unique solution to a well-posed equation set. Specifying more or less than the required number may make the problem “ill-posed” and can lead to incorrect solutions. While one-dimensional supercritical flow requires superimposing two boundary conditions at the upstream end, a subcritical flow requires superimposing one boundary condition at the downstream end. In this simulation, at the upstream end, a flow depth of 0.04 m and flow discharge was specified. At the downstream end, a constant flow depth of 0.24 m was used. For this flow and boundary depth combination, the Froude numbers at the upstream and downstream end of the channel are 3.37 and 0.66, respectively.
2.3 Examined numerical models
The results of the DHM, RAS, WSPG, and TUFLOW models were compared with the experimental data. The other three models are briefly described below.
HEC-River Analysis System (RAS): HEC-RAS (steady state) model is based on the solution of the one-dimensional energy equation between two sections with energy losses given by Manning’s equation. The momentum equation can be used in situations where the water surface profile is rapidly varied as in hydraulic jump, hydraulics of bridges, and evaluating profiles at river confluences (stream junctions). RAS model also has modules to solve unsteady flows, sediment transport, and water quality analysis. In this work, the steady-state model was used. The model was developed by the US Army Corps of Engineers and can be downloaded for free [7].
Water Surface Pressure Gradient (WSPG): WSPG is one of the first models in computational hydraulics that was developed by the Los Angeles County Department of Public Works. The model solves the Bernoulli energy equation between any two cross sections, using the standard step method. The program computes uniform and nonuniform steady flow water surface profiles. As part of the solution, it can automatically identify a hydraulic jump in the channel reach. The model is currently distributed for a fee by civil design [8].
Two-dimensional unsteady flow (TUFLOW): The two-dimensional depth-averaged shallow water equations are solved in TUFLOW using a structured grid system with an alternating direction implicit scheme. The algorithm can capture flow transitions from supercritical to subcritical. TUFLOW incorporates the 1-D component (ESTRY software) or quasi-2D modeling system based on the full one-dimensional free surface flow Equations [9]. The model was developed by BMT WBM and can be downloaded for a fee.
2.4 Results
Figure 1 is a plot of the steady-state depth profile from the four models together with the experimental data. But for DHM, all other models satisfactorily predict the location and the flow depth before and after the jump. The reason as to why DHM could not capture the jump is because of the number of boundary conditions that the DHM permits from the end user. At the upstream end, DHM allows for only the flow discharge to be specified (and not two boundary conditions). The model had 18 grid elements in the computational domain. The upstream element is #1, and the end downstream element is #18. Element #14 corresponds to the end of the channel (length = 15.2 m). At element #1, the input discharge is 0.036 m3/s. At element #18, critical depth condition is specified, and the grid elevation was progressively raised such the depth at element #14 equals 0.24 m.
Figure 1.
Steady-state results for one-dimensional rapidly varying flow.
Because of this boundary condition limitation in the DHM, the jump is smoothened out in the solution. Although the downstream depth is consistent with other models, at the upstream end, the DHM predicted depth is higher than actual depth. DHM computed the flow transitioning from supercritical to subcritical without going through a hydraulic jump as required by theory and observed in the flume model. It can be concluded that DHM cannot be used in applications which require the prediction of the location of hydraulic jump.
3. Two-dimensional applications
3.1 Overland flow on a sloping domain
Overland flow is a dynamic response of the watershed to excess rainfall. Overland flow typically occurs as sheet flow on the land surface, and when the flow joins a channel, it is known as streamflow. The spatial and temporal distribution of two-dimensional overland flow variables is driven by the topographical characteristics of the domain and the boundary conditions. The nonhomogeneous surface characteristics and varying width of the natural watercourse path in the direction of flow make it an ideal case for comparing the results of different models.
Modeling overland flow has drawn the attention of many researchers. Since excess rainfall can cause flooding and mudslides which has the potential to cause loss of human life and disrupt the local economy, reliably predicting the flow variables for different precipitation scenarios can assist decision-makers and emergency personnel. Researchers have modeled these flows by solving the two-dimensional fully dynamic shall water Equations [10, 11] or their diffusion [12, 13] or kinematic approximations [14, 15].
3.1.1 Examined numerical models
The results of the DHM, extended DHM (EDHM), RAS, WSPG, and the CFD (OpenFOAM) models were compared. The back engine for EDHM is identical to DHM, with the primary difference between the two models being the larger array size of the variables in EDHM. When DHM was originally developed, the maximum array dimension was limited to 250, largely because of the available computational resources in 1980s. In EDHM, the dimension of all the arrays was increased to 9999. Since background information relating to RAS and WSPG has been given earlier, characteristic features of the CFD model, OpenFOAM, are summarized below.
OpenFOAM (Open-source Field Operation and Manipulation) is a freely available open-source software [16] that is gaining popularity across CFD applications. Its versatile C++ toolbox for the Linux operating system enables developing customized, efficient numerical solvers, and pre-/post-processing utilities for all kinds of CFD flows [17]. This code uses a tensorial approach following the widely known finite volume method (FVM), first used by McDonald [18]. Both structured and unstructured meshes can be used in the computational domain. The time integration can be done through backward Euler, steady-state solver, and Crank-Nicholson. The available gradient, divergence, Laplacian, and interpolation schemes are second-order central difference, fourth-order central difference, first-order upwind, and first-/second-order upwind. The turbulence models that can be used in OpenFOAM are LES, k-ɛ, and k-ɯ. The available solvers, options in specifying the boundary conditions, mesh generation tools, flow visualization software, and extensive documentation are making OpenFOAM popular among the CFD modeling community [2, 13, 19].
3.1.2 Study area and model variables
The study area is shown in Figure 2. Overland flow generated by a storm down a steep slope hits the flat main street after which a significant portion of the flow continues flowing North. The flow is supercritical from the inlet boundary location to the downstream of the main street. At the street downstream, there is a wall which reduces the flow velocity, thus forcing the flow to be critical. In this analysis, lateral flow on the main street was neglected.
Figure 2.
Map of the study area. The inflow and outflow boundary locations are identified by and .
In the models, the Manning roughness coefficient was set to 0.015 for the street portion and 0.03 for the earth. A uniform grid size of 15 ft was used, which resulted in a total of grids in the domain. The grids were oriented with the natural watercourse (NWC) path. The NWC ranged between 27 and 35 ft in the vicinity of the upstream end (southwest corner), and its width ranged between 45 and 60 ft in the vicinity where the water hits the main street. Having a 15 ft grid enabled us to cover the entire NWC path (Figure 3). The elevation at the center of the grid in the DHM was obtained from the topography map of the area. For the RAS and WSPG models, the required cross-sectional data was obtained from the top map. The rest of the input variables were consistent with the DHM data.
Figure 3.
DHM computational domain. The domain is aligned with the natural watercourse path and had 248 cells, which are 15 ft squares.
The intensity of rainfall and the bottom slope enhances the power of gravity-driven overland flow to make it supercritical (Froude number > 1). The available power in the water near the street can potentially push any moving or stationary automobiles. At the upstream end center cell, the flow hydrograph (Figure 4) was used as the boundary condition. The peak discharge at t = 0.5 hours is 755 cfs. At the downstream end, a critical depth boundary was specified. To keep the effect of downstream boundary minimal on the solution, the domain was extended by about 250 ft north of the main street.
Figure 4.
Input hydrograph at the upstream end.
Our focus was on predicting the flow depth at multiple probe locations on the main street. To conserve space, these results are plotted at two of the thirteen probe locations. These probe locations are 9 and 13 (Figure 5).
Figure 5.
Location of the probes along the center of the main street. The flow depth results were compared at probe locations 9 and 13.
3.1.3 Results
The DHM computational results include a variety of hydraulics relationships that are useful in further detailed analysis, such as flow velocity, flow depth, Froude number, and so forth. Of course, the code can be readily included in the DHM or as a post-processor routine, which enhances the DHM outcome. Of particular interest are the computational results from the DHM in comparison with the computational results produced by the CFD application. To display these computational results, hypothetical “probes” are inserted into the computational mesh where computational results are assembled and collated into a form suitable for visualization. The results from the visualization assessment are depicted below.
Figures 6 and 7 are plots comparing the flow depth, at probe location 9 and 13 on the main street. The data from DHM, EDHM, WSPG, RAS, and CFD (OpenFOAM) are plotted, for a specific CFD simulation time period. It is noted that the computational results are of high similarity, yet the computational effort ranges vastly. The DHM takes approximately 1 hour of CPU for the indicated simulation. In comparison, because of the varying small grid sizes in the domain, the CFD model required 2 weeks of CPU time using a parallel processor.
Figure 6.
Comparison of flow depth at probe 9 location.
Figure 7.
Comparison of flow depth at probe 13 location.
3.2 Open channel flow with a constriction
Numerically predicting the characteristics of flow through a channel with symmetric abrupt constriction (Figure 8) in the form of reduced channel width has drawn the attention of many researchers and has been part of any standard text book in hydraulics. The idea of developing DHM for this application was inspired after reading a recent paper [20] who tested a series of 2D models for multiple applications, one of which is flow through a constriction. Our focus was to estimate the DHM head loss at steady state and compare it with the published data.
Figure 8.
Definition sketch of the test problem along with the location of the two points (P1 and P2).
3.2.1 Examined numerical models
The results of the extended DHM (EDHM), Mike 21, TUFLOW, and HEC-RAS 2D models were compared, with the bench mark data from the equations provided by the Federal Highways Administration (FHA). Mike 21 and HEC-RAS 2D are briefly outlined here. Mike 21 solves the two-dimensional free surface flows where stratification can be neglected. It was originally developed for flow simulation in coastal areas, estuaries, and seas. The various modules of the system simulate hydrodynamics, advection-dispersion, short waves, sediment transport, water quality, eutrophication, and heavy metals [21].
HEC-RAS 2D (5.0.1) solves the two-dimensional Saint-Venant Equations [7] for shallow water flows using the full momentum computational method. The equations can model turbulence and Coriolis effects. For flow in sudden contraction, which is accompanied by high velocity, using the full momentum method in RAS 2D is recommended. The model uses an implicit finite volume solver.
3.2.2 Model variables
Figure 8 is the definition sketch of the test problem. The rectangular channel is 3100 ft long and 320 ft wide. The constriction is 60 × 60 ft. The channel length before constriction is 310 ft, and its length after constriction is 2730 ft. The computational domain in EDHM had 10 ft square cells, and the total number of cells was 9920. The longitudinal slope is 1%, the transverse slope is zero, and the model was run for a total of 1 hour. The upstream inflow is 1000 cfs. Since there are 30 cells at the upstream end, a uniform steady inflow of 33.3 cfs was specified at each of the cells. At the downstream end, a free overall boundary was specified. Constricting the flow area results in loss of energy. This loss of energy is reflected in a rise in energy gradient line and energy upstream of the constriction. Of interest is to estimate the head loss that occurs between points 1 and 2 (shown in Figure 8). The head loss (HL) equals WSE2 − WSE1, where WSE is the water surface elevation [20].
3.2.3 Results
Table 1 shows the comparison of the head loss value obtained from the models along with the published data of other models. It is noted that in this effort, the computational models are compared with respect to head loss (as given in [20]) through the constriction, and this is the primary form of assessment. The DHM WSE change value is within the range of other model predictions, although all the model predictions are above the FHA value.
WSE change (ft)
HEC-RAS 2D
1.27
TUFLOW
0.99
Mike 21
1.28
EDHM
1.16
FHA equation
0.8
Table 1.
Comparison of change in water surface elevation at constriction between EDHM and published data*.
Except for EDHM all other data were obtained from the literature [20].
4. Conclusions
Results from multiple computer models are compared with those of DHM for one- and two-dimensional flows. The considered one-dimensional flow was a mixed flow with a hydraulic jump. All the model results (DHM, WSPG, RAS, TUFLOW) were compared with the benchmark experimental data. Because of the way the boundary conditions are specified in the DHM, the model cannot simulate the hydraulic jump. For the two-dimensional overland flow, the model results (DHM, EDHM, TUFLOW, MIKE 21, WSPG, RAS, RAS2D, and the CFD model, OpenFOAM) were compared between themselves. The agreement of the predicted flow variables reinforces the reliability of the current model.
\n',keywords:"HEC-RAS, WSPG, TUFLOW, OpenFOAM, Froude number, hydraulic jump, overland flow",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72844.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72844.xml",downloadPdfUrl:"/chapter/pdf-download/72844",previewPdfUrl:"/chapter/pdf-preview/72844",totalDownloads:126,totalViews:0,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:null,dateReviewed:"June 17th 2020",datePrePublished:null,datePublished:"September 9th 2020",dateFinished:null,readingETA:"0",abstract:"In this chapter, the performance of DHM for one- and two-dimensional flows is compared with the results of HEC-RAS, HEC-RAS 2D, WSPG, TUFLOW, Mike 21, and OpenFOAM models. The latter four models are currently widely used in industry, and benchmarking their data with DHM can shed more light on the reliability of DHM. As the results indicate, for applications which do not violate the assumptions made in DHM, the results are in agreement.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72844",risUrl:"/chapter/ris/72844",book:{slug:"a-diffusion-hydrodynamic-model"},signatures:"Theodore V. Hromadka II and Prasada Rao",authors:[{id:"181008",title:"Dr.",name:"Theodore V.",middleName:"V.",surname:"Hromadka II",fullName:"Theodore V. Hromadka II",slug:"theodore-v.-hromadka-ii",email:"tedhromadka@yahoo.com",position:null,institution:{name:"United States Military Academy",institutionURL:null,country:{name:"United States of America"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. One-dimensional application",level:"1"},{id:"sec_2_2",title:"2.1 Flows with hydraulic jump",level:"2"},{id:"sec_3_2",title:"2.2 Experimental setup and model variables",level:"2"},{id:"sec_4_2",title:"2.3 Examined numerical models",level:"2"},{id:"sec_5_2",title:"2.4 Results",level:"2"},{id:"sec_7",title:"3. Two-dimensional applications",level:"1"},{id:"sec_7_2",title:"3.1 Overland flow on a sloping domain",level:"2"},{id:"sec_7_3",title:"3.1.1 Examined numerical models",level:"3"},{id:"sec_8_3",title:"3.1.2 Study area and model variables",level:"3"},{id:"sec_9_3",title:"3.1.3 Results",level:"3"},{id:"sec_11_2",title:"3.2 Open channel flow with a constriction",level:"2"},{id:"sec_11_3",title:"3.2.1 Examined numerical models",level:"3"},{id:"sec_12_3",title:"3.2.2 Model variables",level:"3"},{id:"sec_13_3",title:"Table 1.",level:"3"},{id:"sec_16",title:"4. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Viti N, Valero D, Gualtieri C. Numerical simulation of hydraulic jumps. Part 2: Recent results and future outlook. Water. 2018;11:28. DOI: 10.3390/w11010028'},{id:"B2",body:'Bayón Barrachina A, López Jiménez PA. Numerical analysis of hydraulic jumps using OpenFOAM. Journal of Hydroinformatics. 2015;17(4):662-678. DOI: 10.2166/hydro.2015.041'},{id:"B3",body:'Mortazavi M, Le Chenadec V, Moin P, Mani A. Direct numerical simulation of a turbulent hydraulic jump: Turbulence statistics and air entrainment. Journal of Fluid Mechanics. 2016;797:60-94'},{id:"B4",body:'Gharangik AM, Chaudhry MH. Numerical simulation of hydraulic jump. Journal of Hydraulic Engineering ASCE. 1991;117:1195'},{id:"B5",body:'Rao P, Hromadka TV II. Numerical modeling of rapidly varying flows using HEC-RAS and WSPG models. Technical Note. Springerplus. 2016;5:662. DOI: 10.1186/s40064-016-2199-0'},{id:"B6",body:'Hirsh C. Numerical Computation of Internal and External Flows. New York (NY): John Wiley & Sons; 1990'},{id:"B7",body:'U.S. Army Corps of Engineers (USACE). HEC-RAS River Analysis System. User’s Manual. Davis, CA: Hydrologic Engineering Center; 2019. Version 5.0.7. Available from: http://www.hec.usace.army.mil/software/hec-ras/'},{id:"B8",body:'Water Surface Pressure Gradient for Windows. Joseph E. Bonadiman & Associates, Inc.; Available from: https://civildesign.com/products/wspgw-water-surface-pressure-gradient-for-windows'},{id:"B9",body:'BMT-WBM, Australia. User’s Manual for TUFLOW. Spring Hill: WBM Oceanics Australia; 2019'},{id:"B10",body:'Costabile P, Costanzo C, Macchione F, Mercogliano P. Two-dimensional model for overland flow simulations: A case study. European Water. 2012;38:13-23'},{id:"B11",body:'Ponce VM. Modeling surface runoff with kinematic, diffusion, and dynamic waves. In: Singh VP, Kumar B, editors. Proceedings of the International Conference on Hydrology and Water Resources. New Delhi, India, December 1993. Dordrecht: Water Science and Technology Library, Springer; 1996. p. 16'},{id:"B12",body:'Kazezyılmaz-Alhan CM. An improved solution for diffusion waves to overland flow. Applied Mathematical Modelling. 2012;36:465-1472'},{id:"B13",body:'Santillana M. Analysis and numerical simulation of the diffusive wave approximation of the shallow water equations [PhD thesis]. Austin: University of Texas; 2008'},{id:"B14",body:'Liu Q , Chen L, Li J, Singh V. Two-dimensional kinematic wave model of overland-flow. Journal of Hydrology. 2004;291(10):28-41'},{id:"B15",body:'Tsai T, Yang J. Kinematic wave modelling of overland flow using characteristic method with cubic-spline interpolation. Advances in Water Resources. 2005;28(7):661-670'},{id:"B16",body:'The Free Software Foundation Inc. OpenFOAM: The Open Source CFD Toolbox User Guide. London, United Kingdom: The Free Software Foundation Inc; 2019. Available from: https://openfoam.org/'},{id:"B17",body:'Weller H, Tabor G, Jasak H, Fureby C. A tensorial approach to computational continuum mechanics using object-oriented techniques. Computers in Physics. 1998;12:620-631'},{id:"B18",body:'McDonald PW. The computation of transonic flow through two-dimensional gas turbine cascades. American Society of Mechanical Engineers. Paper No: 71-GT-89, V001T01A089; 1971. p. 7. DOI: 10.1115/71-GT-89'},{id:"B19",body:'Bayon A, Valero D, García-Bartual R, Jos F, Valles-Mor FJ, Lopez-Jim P. Performance assessment of OpenFOAM and FLOW-3D in the numerical modeling of a low Reynolds number hydraulic jump. Environmental Modelling & Software. 2016;80:322-335'},{id:"B20",body:'Paudel M, Roman SB, Pritchard J. A Comparative Study of HEC-RAS 2D, TUFLOW, & Mike 21. In: ASFPM 2016 Annual National Conference, Grand Rapids, MI. 2016'},{id:"B21",body:'DHI, MIKE 11 & MIKE 21 Flow Model. Scientific Documentations. 2019. Available from: https://www.mikepoweredbydhi.com'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Theodore V. Hromadka II",address:"ted@phdphdphd.com",affiliation:'
Department of Mathematical Sciences, United States Military Academy, USA
Department of Civil and Environmental Engineering, California State University, USA
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1. Introduction
Plants are essential natural resource for the survival and welfare of human being. The many uses of plants can be summarized in “TREES” word, an abbreviation for timber, restoration, ecological, educational and recreation, and source of sustenance [1]. Given the importance of plants, people have been upgrading both productivity and quality of cultivated plants. Plant breeding technique comes as an art, science, and business of manipulating the genetic pattern of plants by humans to develop superior cultivars, related to improving humankind, ranging from unintentional changes that are resulted by conventional selection to precision breeding by molecular tools [2, 3].
Plant breeding activities are considered to have been going on for at least 10,000 years, as long as the age of human civilization from nomadic hunter-gatherer to sedentary lifestyle [2]. The earlier farmers collected their best seeds to be replanted. This conscious human selection activity to get the best performing plant, although relatively simple, is the fundamental principle of phenotype-selection based. Later people incorporated some superior properties of different closely related parents through artificial mating or sexual hybridization, generating new genetic recombination which dramatically led to increased crop yields, easy cultivation, tolerant to environmental stresses, and resistant against pest [2, 3, 4].
The advance of molecular biology and genetic engineering provides new opportunities in plant breeding technology. Moreover, the application of molecular markers developed from QTL analysis enhances breeding efficiency by enabling marker-assisted selection for particular agronomic traits [5]. On the other hand, next generation sequencing (NGS) has revolutionized genomic and transcriptomic approaches to biology. These new sequencing tools are also valuable for discovery, validation, and assessment of genetic marker in populations. NGS technologies have conferred new opportunities for high-throughput genotyping in various plant species. Recent improvements in high-throughput sequencing have enable sequences to be used to detect and score single nucleotide polymorphisms (SNPs) by bypassing time-consuming process of marker development. However, genotype-by-sequencing (GBS), a series of genetic analyses that includes molecular marker discovery and genotyping using NGS technologies, has opened new possibilities in plant breeding and plant genetics studies, including linkage maps, genome-wide association studies, genomic selection, and genomic diversity studies [6]. Furthermore, horizontal gene transfer (HGT), also known as lateral gene transfer, refers to the movement of genetic information across normal mating barriers, between more or less distantly related organisms, and thus stands in distinction to the standard vertical transmission of genes from parent to offspring [7]. HGT from bacteria to plants has been restricted to Agrobacterium rhizogenes, and the related bacterium A. tumefaciens transforms a wide variety of host plants by transferring a segment of the large tumor-inducing plasmid, called T-DNA into host cells [8]. Most genetic engineering of plants uses AMT to introduce novel gene content.
The development of AMT technology is supported by research on RNA interference technology for functional analyses of genes involved in transformation mechanism or gene(s) of interest and gene editing technology that allows precise manipulation of targeted genome sequences [9]. Although there are several species of Agrobacterium that have the capability to cause tumors—such as A. rhizogenes that cause hairy roots disease, the discussion of Agrobacterium-mediated transformation in this chapter specifically refers to A. tumefaciens. The Agrobacterium-plant interaction is a complex process that its effectiveness and efficiency are affected by many factors. This review focuses on the mechanism of AMT and updates technology to increase the successfulness of plant-gene transformation mediated by A. tumefaciens.
The generation of transgenic plant mediated by A. tumefaciens basically mimics the event of naturally plant transformation. The dawn of natural plant transformation study began when fleshy rough roundish surface morphology on the roots’ crown (region joining root and shoot) of over 20 different fruit trees was observed [10]. To investigate the causative agent of these tumor-like outgrowths later named “crown gall disease,” infected root crown tissues were isolated. The bacteria described as Agrobacterium tumefaciens then were presented [11]. While these Gram-negative soil bacteria were inoculated to wounded young tissues of healthy plants, secondary tumors that cannot be distinguished from the crown gall were produced. On the other hand, the old tissues were not very susceptible [10].
The development of in vitro cultivation technique supports the study of secondary tumor. Explant derived from the interior of secondary tumor continued to unlimited proliferation in auxin in auxin and cytokinin lacking medium and synthesized unusual amino acid derivative; guanido amino acids octopine N2-(D-1-carboxyethyl)-L-arginine and nopaline N2-(1,3-dicarboxypropyl)-L-arginine [9, 12]. Both properties distinguished tumor cells than normal cells.
Bacterial isolation from secondary tumor cultures revealed that no one of these cultures has yielded any growth of A. tumefaciens. Injection of paste from these bacteria-free cultures to healthy plants exhibited no evidence of tumor initiation as it is observed due to paste injection from young primary tumors. The fact that volume of secondary tumor still increased—although bacteria were absent, indicated that bacteria only trigger tumorigenesis, not involved in the whole process. Somehow, the host cells were permanently transformed so that they were able to convert normal cells into neoplastic cells [13].
Further investigation to confirm the involvement of A. tumefaciens in early step of tumorigenesis was conducted through temperature challenge. Bacterial inoculation on periwinkle plant (Vinca rosea L.) at temperature of 32°C did not inducted swelling growth, even bacterial and plant cells grew well at that temperature. The bacterial inability to forming tumorous property was not influenced by host plant physiological disturbances as a whole but was solely dependent upon environmental change of bacterial inoculation site, in this case an elevated temperature.
Plants inoculated with bacteria at a temperature of 26°C for 5 days, before being held at 32°C, retained tumorigenic state. These periods provided sufficient opportunity for bacteria to interact with host plants, and then they drove the cellular alteration of the plants. Once the cellular alteration was fully complete, temperature at 32°C would not matter; plants were entirely independent in converting normal cells into neoplastic cell. The nature of plant oncogenic transformation as bacterial influence was known as “tumor-inducing principle” [13, 14].
The development of molecular technique made a breakthrough in the investigation of the origin of tumor-inducing principle. On the examination of the pathogenic strains of A. tumefaciens, one or several homolog large (±140–235 kbp in size) supercoiled circular plasmids were isolated [15]. None of the avirulent Agrobacterium strains tested belonged to such a plasmid. Based on its association in inducing tumors, the plasmid was called “tumor-inducing(Ti)-plasmid” [15, 16].
Ti-plasmid borne harbored by four apparently distinct genetic loci (Figure 1). Three loci that consist of genes regulating auxin, cytokinin, and opine synthase are located on T-DNA, a highly conserved DNA fragment defined by 25 bp repeat sequence borders on each end. The genes regulating auxin and cytokinin accumulation determine the oncogenicity of the plasmid. The gene regulating opine synthesis is necessary for expression in host plants as exclusive nutrition for A. tumefaciens [17]. The fourth locus, apart from T-DNA, controls the catabolism of opine compound [9, 20]. All genes carry the signal necessary for the expression in host plant cell. During bacterial infection to host cell, only T-DNA was cleaved out of the Ti-plasmid borne, transferred, and ultimately incorporated to DNA nuclear of host plant cells [18, 19]. Therefore, the complete Ti-plasmid was not found in tumor cells, given their relatively large size could not infiltrate host nuclear ore complex passively [15]. Genes regulating auxin and cytokinin synthesis modifying the phytohormone ratio in the host cell resulting uncontrolled cell proliferation that leading to tumor growth.
Figure 1.
Schematic representation of a Ti-plasmid borne (not to scale, modified from [20, 21]).
2.2 The natural pathogenesis of A. tumefaciens
AMT is a complicated mechanism, which includes (1) signal recognition from plant host to A. tumefaciens, (2) T-DNA processing, (3) T-DNA traveling in plant host cell, (4) T-DNA integrating to plant host genome, and (5) expression of T-DNA in the plant host cell. The mechanism of T-DNA transfer is facilitated by a set of virulent genes located on Ti-plasmid borne [approximately 35 virulent genes grouped in at least 8 operons, virA, virB, virC, virD, virE, virF, virG, and virH, encoding VirA, VirB, VirC, VirD, VirE, VirF, VirG, and VirH protein, respectively (Figure 1)], apart from T-DNA, whereas others are on chromosome (chromosomal virulent genes—chv) [22, 23].
2.2.1 Signal recognition
Agrobacterium-plant interaction occurs when a large plant-derived chemicals, which include organic acid compounds (pH 5.0–5.8) as routine secreted chemical and phenolic compounds as the wound-releasing chemical, are exposed to the bacteria. Signal recognition of A. tumefaciens to plant cell involves three systems (Figure 2) [24]. First, bacterial chemotaxis attracted by phenolic compounds that are exuded from fresh wound site of plants, such as acetosyringone and α-hydroxy acetosyringone [25]. Initially, bacteria engage with plant cell surface reversibly, stimulating adhesion production that causes adhesion through unipolar polysaccharide (UPP)-dependent polar attachment and UPP-nondependent attachment. This irreversible surface attachment establishes a site for multicellular biofilms formation, matrix elaboration, cell division, biofilm maturation, and “buddy daughter” cell dispersal [26]. Secondly, host signal compounds are also recognized by transmembrane protein receptors (VirA) on periplasmic space of bacterial cell that trigger phosphorylation of positive regulatory protein VirG [25, 26, 27]. Thirdly, protein ChvG/ChvI encoded by chromosomal virulent genes perceive acidity from sugar that activate basal expression of virG too [22]. The phosphorylated VirG leads to the activation of another virulence gene [25]. Monosaccharides enhance signaling process by binding with ChvE then synergize with VirA [28].
Figure 2.
Schematic representation of A. tumefaciens signal recognition mechanism, modified from [26].
2.2.2 T-DNA processing
The process of excising T-DNA from Ti-plasmids depends on VirD1 and VirD2 as endonucleases. The 25 bp border sequences on the bottom strand of T-DNA act as nicking site for VirD1 and VirD2. VirD1, a site-specific helicase, unwinds double-stranded T-DNA. A nuclease, VirD2, cuts the bottom strand of T-DNA from the right and left border, becoming single-stranded linear DNA termed T-strand [21, 29]. VirD2 then covalently caps the 5′ end of T-strand at the right border, forming the VirD2/T-strand complex [28]. The 3′ end of the nicked right border acts as a priming site for the bottom strand of T-DNA regeneration [29]. VirC1 binding the “overdrive” sequence near the right border of T-DNA (Figure 1) through its C-terminal ribbon-helix-helix DNA binding fold enhances the number of T-strand molecules [30, 31]. The right fraction of the 25 kb terminus sequence of T-DNA determines the director of DNA transfer [32] (Figure 3).
Figure 3.
T-strand generation from T-DNA, modified from [29].
2.2.3 T-DNA traveling
The T-DNA traveling to the host cannot separate from the role of VirD2 and VirE2. Both VirD2 and VirE2 have the C-terminal nuclear localization signal (NLS) sequence that piloted VirD2/T-strand to the host nucleus [33]. VirD2/T-strand, a rodlike structure, exits bacterial cells through Ti-pilus, type IV secretion system (T4SS), which is assembled by 11 VirB and VirD4 proteins [34]. The hydrophilic protein VirE2 is accumulated in the bacterial cytoplasm and translocated into the host cell through clathrin-mediated endocytosis. Besides helping transport the T-strand, VirE2 in the host cytoplasm coats along the T-strand noncovalently and form VirD2/T-strand/VirE2 (termed Ti-complex) to protect it from any nuclease digestive activity [35, 36, 37]. Ti-complex in the plant cytoplasm is trafficked to the plant nucleus via the endoplasmic reticulum network inside the plant cytoplasm (Figure 4) [24].
Figure 4.
T-complex traveling, modified from [38].
2.2.4 T-DNA integration
T-DNA integration followed by transgene expression is the final and crucial stage in the genetic transformation mediated by Agrobacterium. The molecular mechanism of T-DNA integration into host plant genome actually is still not fully understood. T-DNA integration occurs at random sites, not preferentially in transcriptional active or hypomethylated regions of the plant genome. Some important genes in the T-DNA integration process are limited to genes related to chromatin formation and histone modification [39].
VirE2 may play a role in T-strand targeting to chromatin by binding to the bZIP transcription factor VirE2 interacting protein 1 (VIP1). VIP1 mediates the association of VirE2/single-strand DNA with mononucleosomes, a unit of chromatin in the nucleus [39]. When T-complex arrived in the plant nucleus, its protein component should be disassembled by the ubiquitin-proteasome system so that the T-strand can be exposed. T-complex disassembling process and VirE2 degradation are assisted by VirF [40, 41].
VirD2 has no ligation activity, so T-strands are not likely to join directly with the host genome. Possibly, the host DNA polymerase copies the T-strand to form a double-stranded T-DNA, and then it joins with the site breaks of DNA host plant that it is caused by environmental stress due to Agrobacteria incubation or normal metabolic processes. Non-homologous end joining (NHEJ) as a way of repairing broken double-stranded DNA is proposed as the main pathway of bacterial plant DNA integration. NHEJ only requires little or no sequence homology on the damaged part, although in fact there are microhomology between the T-DNA and the integration points on the host chromosome [42] (Figure 5).
Figure 5.
T-DNA integration, modified from [42].
2.2.5 T-DNA expression
Bacterial T-DNA that integrates with plant genome cells faces two possible fates. First, the T-DNA is expressed, in various levels. Second, the T-DNA is only integrated but cannot be expressed. A broad range of transgene expression, from very high to totally silent, depends on species [12].
The expression on auxin and cytokinin coding genes in T-DNA causes the accumulation of both phytohormones. Phytohormone ratio abnormalities bring plant cell to uncontrolled cell proliferation, leading to tumor growth. The expression of opine synthesis coding genes produces opine—the type depends on bacterial strain, an exclusive nutrition for Agrobacterium. In young tissue, swelling is observed from the fourth or fifth day after bacterial inoculation, well-developed in a month, and growing rapidly until it reaches an inch or two in diameter for several months [10]. The overall mechanism of AMT is summarized in Figure 6.
Figure 6.
Overall mechanism schematic of AMT.
2.3 Agrobacterium-mediated transformation
2.3.1 The engineered A. tumefaciens Ti-plasmid
The wild-type Ti-plasmids are not suitable for being gene vectors because the T-DNA has oncogenes that cause tumor growth in host cells. Construction disarmed Ti-plasmid by deletion of oncogenes, and opine biosynthetic coding gene makes the plasmid non-oncogenic, the 25 bp of each repeat border sequence remaining. The promise of AMT relies on the substitution of T-DNA by any foreign DNA sequence so that A. tumefaciens can be a “vehicle” in insertion gene(s) of interest that are transmissible to the progeny [18, 43]. Selectable marker genes are inserted into the T-DNA in order to distinguish the transformed cells from normal cells, tandem with experimental transgenes. Some herbicide resistance markers that are commonly used are phosphinothricin, chlorsulfuron, sulfonamide, and glyphosate. The insertion of bacterial selectable marker, such as trimethoprim, streptomycin, spectinomycin, sulfonamides, bleomycin, hygromycin, kanamycin, neomycin, or gentamicin, evaluates the uptake engineered plasmid to bacterial cell [8].
Disarmed Ti-plasmid is difficult to be manipulated in vitro due to its large size. Since the virulence genes may act in trans on the T-DNA sequences in the same cell, it was transferred to small independently plasmid that has origin of replication for Agrobacterium, called “helper vector.” In addition, the elimination of virulence gene causes the Ti-plasmid to accommodate longer transgene. Furthermore, scientist constructs the binary vector, so called because it is designed to be replicate in multiple host (E. coli and A. tumefaciens). The binary vector consist of left and right borders, origin of replication for multiple host, selectable marker genes, and gene(s) of interest. This engineered plasmid is now used in plant genetic transformation.
AMT is a general method for genetic modification in many plant species. It is because it allows efficient insertion of stable, un-rearranged, single-copy sequences into plant genome. Two critical points for successful transformation were indicated: the use of actively dividing embryonic callus cells derived from the scutella of mature seeds as the starting material and the addition of a phenolic compound, acetosyringone, in the cocultivation steps [44, 45]. Moreover, Cheng et al. reported that there is no significant difference in the transformation efficiencies between immature embryos, pre-cultured ones, and embryogenic callus [46].
Several protocols of AMT have been reported either in Monocotyledoneae or Dicotyledoneae plants. In general, Agrobacterium-based method was used for transgenic plant. The protocol consists of seven steps, which can be briefly summarized as follows: stage (I) preparation of sterilize seed or samples and inoculum; stage (II) explant preparation, infection, and cocultivation with A. tumefaciens; stage (III) selection; stage (IV) regeneration; stage (V) acclimatization and molecular identification of T0; stage (VI) cultivation and self-crossing of T0; and stage (VII) T1 plant analysis (Figure 7).
Figure 7.
The protocol of Agrobacterium-mediated transformation.
2.3.2.1 Preparation of sterilize seed or samples and inoculum
Immature embryo was a common sample that is used for transformation. Some experience reported that transformation efficiencies depend on the genotype or variety [47, 48]. To obtain the immature embryo, seed is planted in sterile media (such as husk, compost, mixed soil, etc.) and grown in environmentally controlled growth rooms. Immature embryo is harvested after pollination, but it depends on the species. On the other hand, callus is also produce from hypocotyl or cotyledon explants.
Inoculum is prepared by culture A. tumefaciens strain that contains appropriate antibiotics. The bacteria are grown with a loop and suspend in specific media such as LB, LS-inf-AS medium. Inoculum should be prepared fresh. In some cases, the growth of A. tumefaciens in liquid culture before transformation is not necessary. On the other hand, the A. tumefaciens preparation and plasmid construction are able to follow the commercial plasmid and A. tumefaciens preparation.
2.3.2.2 Explant preparation, infection, and cocultivation with A. tumefaciens
The embryonic, immature embryo or callus is able to be used as the explants. The explant should be sterilized before the infection or transformation process. Both of the suspension of the embryos and bacteria are transferred to the new plate or empty petri dish. After wrapping the petri dish, the cocultivation step follows by incubating in the dark at 24–29°C for 2–7 days, depending on the species. The A. tumefaciens concentration and the infection time were found to be important factors in preventing explant turning necrosis and improving transformation efficiency. We can follow some recommendation from several protocols for specific species.
2.3.2.3 Selection
Selection is one of the critical factors in the success of transformation. The process of selection can be occurred after the stage of transformation, regeneration, or on T0 and T1 plant. Moreover, antibiotic selection is one of the methods to check the successful transformation. In addition to antibiotic selection, PCR should be used to confirm the presence of the targeted transgene in each transformant at each generation.
2.3.2.4 Regeneration
Regeneration of transformed plants occurred after the proliferation. The shoots grown out from the proliferation explants is pulled out and placed in a new medium. Generally, the regeneration stage is following the in vitro propagation methods which are divided into shoot regeneration and selection, cut and recut shoot regeneration, and root regeneration.
2.3.2.5 Acclimatization and molecular identification of T0
The acclimatization of T0 can occur after the roots grow strongly. The transgenic T0 plant can be grown directly in a soil or mixed media under the environmental controlled or green house.
2.3.2.6 Cultivation and self-crossing of T0
The primary transformant (T0) was obtained by A. tumefaciens transformation. After the study of transgene inheritance in successive generation, T1 seeds are produced by self-crossing pollination of the primary transformant (T0). The process of cultivation occurred in the greenhouse. The seed of primary transformant (T0) is harvested from T0 plant.
2.3.2.7 T1 plant analysis
T1 plant is the plant that obtained from the harvested seed of T0 plant. The analysis of T1 plant are referring to the morphological or physiological expression of the specific gene which is inserted.
3. Conclusions
Currently, AMT become a common tool for genetic engineering. The mechanism of AMT was affected by several factors and also depends on the species. On the other hand, several Agrobacterium and plasmid have been commercialized, and it was accelerating plant breeding technology. This chapter gave brief information related to AMT mechanism, including the history of crown gall disease caused by A. tumefaciens, the natural pathogenesis of A. tumefaciens, and the general protocol of Agrobacterium-mediated transformation in plants.
\n',keywords:"Agrobacterium tumefaciens, crown gall disease, natural pathogenesis, plant transformation, plant breeding technology",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71943.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71943.xml",downloadPdfUrl:"/chapter/pdf-download/71943",previewPdfUrl:"/chapter/pdf-preview/71943",totalDownloads:370,totalViews:0,totalCrossrefCites:0,dateSubmitted:"October 21st 2019",dateReviewed:"January 10th 2020",datePrePublished:"April 26th 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"Agrobacterium-mediated transformation (AMT) heavily relies on the capability of bacterial pathogen Agrobacterium tumefaciens in transferring foreign genes into a wide variety of host plants. Currently, AMT is the most commonly used method for generating transgenic plants. On the other hand, A. tumefaciens was very useful for plant breeding. It also accelerated the technology of plant breeding to obtain specific characters. Gene transfer from bacteria to plants is a complex mechanism that involves several functional steps. This chapter will give brief information related to AMT mechanism, including the history of crown gall disease, the natural pathogenesis of A. tumefaciens, and the general protocol of AMT.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71943",risUrl:"/chapter/ris/71943",signatures:"Risha Amilia Pratiwi and Muhammad Imam Surya",book:{id:"9712",title:"Genetic Transformation in Crops",subtitle:null,fullTitle:"Genetic Transformation in Crops",slug:"genetic-transformation-in-crops",publishedDate:"October 7th 2020",bookSignature:"Kin-Ying To",coverURL:"https://cdn.intechopen.com/books/images_new/9712.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"310646",title:"Dr.",name:"Kin-Ying",middleName:null,surname:"To",slug:"kin-ying-to",fullName:"Kin-Ying To"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Agrobacterium-mediated transformation mechanism",level:"1"},{id:"sec_2_2",title:"2.1 History of crown gall disease",level:"2"},{id:"sec_3_2",title:"2.2 The natural pathogenesis of A. tumefaciens",level:"2"},{id:"sec_3_3",title:"2.2.1 Signal recognition",level:"3"},{id:"sec_4_3",title:"2.2.2 T-DNA processing",level:"3"},{id:"sec_5_3",title:"2.2.3 T-DNA traveling",level:"3"},{id:"sec_6_3",title:"2.2.4 T-DNA integration",level:"3"},{id:"sec_7_3",title:"2.2.5 T-DNA expression",level:"3"},{id:"sec_9_2",title:"2.3 Agrobacterium-mediated transformation",level:"2"},{id:"sec_9_3",title:"2.3.1 The engineered A. tumefaciens Ti-plasmid",level:"3"},{id:"sec_10_3",title:"2.3.2 Agrobacterium-mediated transformation protocol",level:"3"},{id:"sec_10_4",title:"2.3.2.1 Preparation of sterilize seed or samples and inoculum",level:"4"},{id:"sec_11_4",title:"2.3.2.2 Explant preparation, infection, and cocultivation with A. tumefaciens",level:"4"},{id:"sec_12_4",title:"2.3.2.3 Selection",level:"4"},{id:"sec_13_4",title:"2.3.2.4 Regeneration",level:"4"},{id:"sec_14_4",title:"2.3.2.5 Acclimatization and molecular identification of T0",level:"4"},{id:"sec_15_4",title:"2.3.2.6 Cultivation and self-crossing of T0",level:"4"},{id:"sec_16_4",title:"2.3.2.7 T1 plant analysis",level:"4"},{id:"sec_20",title:"3. 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Current Biology. 1996;6(12):1567-1569'},{id:"B35",body:'Gietl C, Koukolicova-Nicola Z, Hohn B. Mobilization of T-DNA from agrobacterium to plant cells involves a protein that binds single-stranded DNA. Proceedings of the National Academy of Sciences of the United States of America. 1987;84:9006-9010'},{id:"B36",body:'Christie PJ, Ward JE, Winans SC, Nester EW. The Agrobacterium tumefaciens virE2 gene product is a single-stranded-DNA-binding protein that associates with T-DNA. Journal of Bacteriology. 1988;170(6):2659-2667'},{id:"B37",body:'Guo M, Ye J, Gao D, Xu N. Yang Jing. Agrobacterium-mediated horizontal gene transfer: Mechanism, biotechnological application, potential risk and forestalling strategy. Biotechnology Advances. 2018;37(1):259-270'},{id:"B38",body:'Ananiadou S, Sullivan D, Black W, Levow GA, Gillespie JJ, Mao C, et al. Named entity recognition for bacterial type IV secretion systems. PLoS One. 2011;6(3):1-10'},{id:"B39",body:'Gelvin SB. Integration of Agrobacterium T-DNA into the plant genome. Annual Review of Genetics. 2017;51(1):195-217'},{id:"B40",body:'Lacroix B, Citovsky V. Beyond Agrobacterium-Mediated transformation: Horizontal gene transfer from bacteria to eukaryotes. In: Gelvin S, editor. Agrobacterium Biology. Current Topics in Microbiology and Immunology. Cham: Springer; 2018. pp. 443-462'},{id:"B41",body:'Lacroix B, Citovsky V. Pathways of DNA transfer to plants from Agrobacterium tumefaciens and related bacterial species. Annual Review of Phytopathology. 2019;57(1):231-251'},{id:"B42",body:'Haika H, Nishizawa-Yokoi A, Toki S. The non-homologous end-joining pathway is involved in stable transformation in rice. Frontiers in Plant Science. 2014;5(560):1-5'},{id:"B43",body:'David C, Chilton M, Tempé J. Conservation of T-DNA in plants regenerated from hairy root cultures. Nature Biotechnology. 1984;2:73-76'},{id:"B44",body:'Hiei Y, Komari T, Kubo T. Transformation of rice mediated by Agrobacterium tumefaciens. Plant Molecular Biology. 1997;35:205-218'},{id:"B45",body:'Hiei Y, Ohta S, Komari T, Kumashiro T. Efficient transformation of rice (Oryza sativa L.) mediated by Agrobacterium and sequence analysis of the boundaries of the T-DNA. The Plant Journal. 1994;6(2):271-282'},{id:"B46",body:'Cheng M, Fry JE, Pang S, Zhou H, Hironaka CM, Duncan DR, et al. Genetic transformation of wheat mediated by Agrobacterium tumefaciens. Plant Physiology. 1997;115(3):971-980'},{id:"B47",body:'Jones HD, Doherty A, Wu H. Review of methodologies and a protocol for the agrobacterium-mediated transformation of wheat. Nature Protocols. 2006;1(6):2796-2802'},{id:"B48",body:'Nishimura A, Aichi I, Matsuoka M. A protocol for Agrobacterium-mediated transformation in rice. Nature Protocols. 2006;1(6):2796-2802'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Risha Amilia Pratiwi",address:"risha.amilia.pratiwi@lipi.go.id",affiliation:'
Cibodas Botanical Garden – Research Center for Plant Conservation and Botanic Gardens, Indonesian Institute of Sciences, Sindanglaya, Cipanas-Cianjur, Indonesia
Cibodas Botanical Garden – Research Center for Plant Conservation and Botanic Gardens, Indonesian Institute of Sciences, Sindanglaya, Cipanas-Cianjur, Indonesia
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