The transformation of banana with potential virus resistance genes

One approach to reducing the yield losses caused by banana viral diseases is the use of genetic engineering and pathogen-derived resistance strategies to generate resistant cultivars. The development of transgenic virus resistance requires an efficient banana transformation method, particularly for commercially important 'Cavendish' type cultivars such as 'Grand Nain'. Prior to this study, only two examples of the stable transformation of banana had been reported, both of which demonstrated the principle of transformation but did not characterise transgenic plants in terms of the efficiency at which individual transgenic lines were generated, relative activities of promoters in stably transformed plants, and the stability of transgene expression. The aim of this study was to develop more efficient transformation methods for banana, assess the activity of some commonly used and also novel promoters in stably transformed plants, and transform banana with genes that could potentially confer resistance to banana bunchy top nanovirus (BBTV) and banana bract mosaic potyvirus (BBrMV). A regeneration system using immature male flowers as the explant was established. The frequency of somatic embryogenesis in male flower explants was influenced by the season in which the inflorescences were harvested. Further, the media requirements of various banana cultivars in respect to the 2,4-D concentration in the initiation media also differed. Following the optimisation of these and other parameters, embryogenic cell suspensions of several banana (Musa spp.) cultivars including 'Grand Nain' (AAA), 'Williams' (AAA), 'SH-3362' (AA), 'Goldfinger' (AAAB) and 'Bluggoe' (ABB) were successfully generated. Highly efficient transformation methods were developed for both 'Bluggoe' and 'Grand Nain'; this is the first report of microprojectile bombardment transformation of the commercially important 'Grand Nain' cultivar. Following bombardment of embryogenic suspension cells, regeneration was monitored from single transfom1ed cells to whole plants using a reporter gene encoding the green fluorescent protein (gfp). Selection with kanamycin enabled the regeneration of a greater number of plants than with geneticin, while still preventing the regeneration of non-transformed plants. Southern hybridisation confirmed the neomycin phosphotransferase gene (npt II) was stably integrated into the banana genome and that multiple transgenic lines were derived from single bombardments. The activity, stability and tissue specificity of the cauliflower mosaic virus 358 (CaMV 35S) and maize polyubiquitin-1 (Ubi-1) promoters were examined. In stably transformed banana, the Ubi-1 promoter provided approximately six-fold higher p-glucuronidase (GUS) activity than the CaMV 35S promoter, and both promoters remained active in glasshouse grown plants for the six months they were observed. The intergenic regions ofBBTV DNA-I to -6 were isolated and fused to either the uidA (GUS) or gfjJ reporter genes to assess their promoter activities. BBTV promoter activity was detected in banana embryogenic cells using the gfp reporter gene. Promoters derived from BBTV DNA-4 and -5 generated the highest levels of transient activity, which were greater than that generated by the maize Ubi-1 promoter. In transgenic banana plants, the activity of the BBTV DNA-6 promoter (BT6.1) was restricted to the phloem of leaves and roots, stomata and root meristems. The activity of the BT6.1 promoter was enhanced by the inclusion of intron-containing fragments derived from the maize Ubi-1, rice Act-1, and sugarcane rbcS 5' untranslated regions in GUS reporter gene constructs. In transient assays in banana, the rice Act-1 and maize Ubi-1 introns provided the most significant enhancement, increasing expression levels 300-fold and 100-fold, respectively. The sugarcane rbcS intron increased expression about 10-fold. In stably transformed banana plants, the maize Ubi-1 intron enhanced BT6.1 promoter activity to levels similar to that of the CaMV 35S promoter, but did not appear to alter the tissue specificity of the promoter. Both 'Grand Nain' and 'Bluggoe' were transformed with constructs that could potentially confer resistance to BBTV and BBrMV, including constructs containing BBTV DNA-1 major and internal genes, BBTV DNA-5 gene, and the BBrMV coat protein-coding region all under the control of the Ubi-1 promoter, while the BT6 promoter was used to drive the npt II selectable marker gene. At least 30 transgenic lines containing each construct were identified and replicates of each line are currently being generated by micropropagation in preparation for virus challenge.

Genetic Analysis of Tolerance to Rice Tungro Bacilliform Virus in Rice (Oryza sativa L.) Through Agroinoculation

Balimau Putih [an Indonesian cultivar tolerant to rice tungro bacilliform virus (RTBV)] was crossed with IR64 (RTBV, susceptible variety) to produce the three filial generations F1, F2 and F3. Agroinoculation was used to introduce RTBV into the test plants. RTBV tolerance was based on the RTBV level in plants by analysis of coat protein using enzyme-linked immunosorbent assay. The level of RTBV in cv. Balimau Putih was significantly lower than that of IR64 and the susceptible control, Taichung Native 1. Mean RTBV levels of the F1, F2 and F3 populations were comparable with one another and with the average of the parents. Results indicate that there was no dominance and an additive gene action may control the expression of tolerance to RTBV. Tolerance based on the level of RTBV coat protein was highly heritable (0.67) as estimated using the mean values of F3 lines, suggesting that selection for tolerance to RTBV can be performed in the early selfing generations using the technique employed in this study. The RTBV level had a negative correlation with plant height, but positive relationship with disease index value

Optimization of long rural feeders using a genetic algorithm

This paper describes the optimization of conductor size and the voltage regulator location & magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Dynamic coast control of train movement with genetic algorithm

The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.

Railway junction conflict resolution by genetic algorithm

In general, simple and traditional methods are applied to resolve traffic conflicts at railway junctions. They are, however, either inefficient or computationally demanding. A simple genetic algorithm is presented to enable a search for a near optimal resolution to be carried out while meeting the constraints on generation evolution and minimising the search time.

Signalling layout for fixed-block railway lines with real-coded genetic algorithms.

Signalling layout design is one of the keys to railway operations with fixed-block signalling system and it also carries direct effect on overall train efficiency and safety. Based on an analysis to system objectives, this paper presents an optimization model with two objectives in order to devise an efficient signalling layout scheme. Taking into account the present railway line design practices in China, the paper describes steps of the computer-based signalling layout optimisation with real-coded genetic algorithms. A computer-aided system, based on train movement simulator, has also been employed to assist the optimisation process. A case study on a practical railway line has been conducted to make comparisons between the proposed GA-based approach and the current practices. The results illustrate the improved performance of the proposed approach in reducing signal block joints and shortening minimum train service headway.

Coast control of train movement with genetic algorithms

Railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. We present an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigate the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. The multiple coasting point control with hierarchical genetic algorithm (HGA) is then proposed to integrate the determination of the number of coasting points.

Coasting control of train operation by genetic algorithm

Balancing between the provision of high quality of service and running within a tight budget is one of the biggest challenges for most metro railway operators around the world. Conventionally, one possible approach for the operator to adjust the time schedule is to alter the stop time at stations, if other system constraints, such as traction equipment characteristic, are not taken into account. Yet it is not an effective, flexible and economical method because the run-time of a train simply cannot be extended without limitation, and a balance between run-time and energy consumption has to be maintained. Modification or installation of a new signalling system not only increases the capital cost, but also affects the normal train service. Therefore, in order to procure a more effective, flexible and economical means to improve the quality of service, optimisation of train performance by coasting point identification has become more attractive and popular. However, identifying the necessary starting points for coasting under the constraints of current service conditions is no simple task because train movement is attributed by a large number of factors, most of which are non-linear and inter-dependent. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting points and investigates the possible improvement on computation time and fitness of genes.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Optimisation of a class of transportation problems using genetic algorithms

This paper presents a Genetic Algorithms (GA) approach to search the optimized path for a class of transportation problems. The formulation of the problems for suitable application of GA will be discussed. Exchanging genetic information in the sense of neighborhoods will be introduced for generation reproduction. The performance of the GA will be evaluated by computer simulation. The proposed algorithm use simple coding with population size 1 converged in reasonable optimality within several minutes.