Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.

Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithms

Ancillary service plays a key role in maintaining operation security of the power system in a competitive electricity market. The spinning reserve is one of the most important ancillary services that should be provided effectively. This paper presents the design of an integrated market for energy and spinning reserve service with particular emphasis on coordinated dispatch of bulk power and spinning reserve services. A new market dispatching mechanism has been developed to minimize the cost of service while maintaining system security. Genetic algorithms (GA) are used for finding the global optimal solutions for this dispatch problem. Case studies and corresponding analyses have been carried out to demonstrate and discuss the efficiency and usefulness of the proposed method.

Genetic algorithm based distance spectrum technique for performance union bound of space-time trellis coded OFDM

This paper derives the performance union bound of space-time trellis codes in orthogonal frequency division multiplexing system (STTC-OFDM) over quasi-static frequency selective fading channels based on the distance spectrum technique. The distance spectrum is the enumeration of the codeword difference measures and their multiplicities by exhausted searching through all the possible error event paths. Exhaustive search approach can be used for low memory order STTC with small frame size. However with moderate memory order STTC and moderate frame size the computational cost of exhaustive search increases exponentially, and may become impractical for high memory order STTCs. This requires advanced computational techniques such as Genetic Algorithms (GAS). In this paper, a GA with sharing function method is used to locate the multiple solutions of the distance spectrum for high memory order STTCs. Simulation evaluates the performance union bound and the complexity comparison of non-GA aided and GA aided distance spectrum techniques. It shows that the union bound give a close performance measure at high signal-to-noise ratio (SNR). It also shows that GA sharing function method based distance spectrum technique requires much less computational time as compared with exhaustive search approach but with satisfactory accuracy.

Optimal design of a regenerative dynamic dynamometer using genetic algorithms

This paper presents an approach for optimal design of a fully regenerative dynamic dynamometer using genetic algorithms. The proposed dynamometer system includes an energy storage mechanism to adaptively absorb the energy variations following the dynamometer transients. This allows the minimum power electronics requirement at the mains power supply grid to compensate for the losses. The overall dynamometer system is a dynamic complex system and design of the system is a multi-objective problem, which requires advanced optimisation techniques such as genetic algorithms. The case study of designing and simulation of the dynamometer system indicates that the genetic algorithm based approach is able to locate a best available solution in view of system performance and computational costs.

Genetic basis of inosine triphosphate pyrophosphohydrolase (ITPA) deficiency

Inosine triphosphate pyrophosphohydrolase (ITPase) deficiency is a common inherited condition characterized by the abnormal accumulation of inosine triphosphate (ITP) in erythrocytes. The genetic basis and pathological consequences of ITPase deficiency are unknown. We have characterized the genomic structure of the ITPA gene, showing that it has eight exons. Five single nucleotide polymorphisms were identified, three silent (138GMA, 561GMA, 708GMA) and two associated with ITPase deficiency (94CMA, IVS2+21AMC). Homozygotes for the 94CMA missense mutation (Pro32 to Thr) had zero erythrocyte ITPase activity, whereas 94CMA heterozygotes averaged 22.5% of the control mean, a level of activity consistent with impaired subunit association of a dimeric enzyme. ITPase activity of IVS2+21AMC homozygotes averaged 60% of the control mean. In order to explore further the relationship between mutations and enzyme activity, we examined the association between genotype and ITPase activity in 100 healthy controls. Ten subjects were heterozygous for 94CMA (allele frequency: 0.06), 24 were heterozygotes for IVS2+21AMC (allele frequency: 0.13) and two were compound heterozygous for these mutations. The activities of IVS2+21AMC heterozygotes and 94CMA/IVS2+21AMC compound heterozygotes were 60% and 10%, respectively, of the normal control mean, suggesting that the intron mutation affects enzyme activity. In all cases when ITPase activity was below the normal range, one or both mutations were found. The ITPA genotype did not correspond to any identifiable red cell phenotype. A possible relationship between ITPase deficiency and increased drug toxicity of purine analogue drugs is proposed.

The Genetic Speciation of Archaeological Fish Bone: A Feasibility Study from Southeast Queensland

Current genetic methods enable highly specific identification of DNA from modern fish bone. The applicability of these methods to the identification of archaeological fish bone was investigated through a study of a sample from late Holocene southeast Queensland sites. The resultant overall success rate of 2% indicates that DNA analysis is, as yet, not feasible for identifying fish bone from any given site. Taphonomic issues influencing the potential of genetic identification methods are raised and discussed in light of this result.

Research Quality, Publications and Impact in Hydraulic Engineering into the 21st Century. Publish or Perish, Commercial versus Open Access, Internet versus Libraries

One of the main objectives of the first International Junior Researcher and Engineer Workshop on Hydraulic Structures is to provide an opportunity for young researchers and engineers to present their research. But a research project is only completed when it has been published and shared with the community. Referees and peer experts play an important role to control the research quality. While some new electronic tools provide further means to disseminate some research information, the quality and impact of the works remain linked with some thorough expert-review process and the publications in international scientific journals and books. Importantly unethical publishing standards are not acceptable and cheating is despicable.

A hybrid probabilistic criterion for market-based transmission expansion planning

Market-based transmission expansion planning gives information to investors on where is the most cost efficient place to invest and brings benefits to those who invest in this grid. However, both market issue and power system adequacy problems are system planers’ concern. In this paper, a hybrid probabilistic criterion of Expected Economical Loss (EEL) is proposed as an index to evaluate the systems’ overall expected economical losses during system operation in a competitive market. It stands on both investors’ and planner’s point of view and will further improves the traditional reliability cost. By applying EEL, it is possible for system planners to obtain a clear idea regarding the transmission network’s bottleneck and the amount of losses arises from this weak point. Sequentially, it enables planners to assess the worth of providing reliable services. Also, the EEL will contain valuable information for moneymen to undertake their investment. This index could truly reflect the random behaviors of power systems and uncertainties from electricity market. The performance of the EEL index is enhanced by applying Normalized Coefficient of Probability (NCP), so it can be utilized in large real power systems. A numerical example is carried out on IEEE Reliability Test System (RTS), which will show how the EEL can predict the current system bottleneck under future operational conditions and how to use EEL as one of planning objectives to determine future optimal plans. A well-known simulation method, Monte Carlo simulation, is employed to achieve the probabilistic characteristic of electricity market and Genetic Algorithms (GAs) is used as a multi-objective optimization tool.

Development of a physical and genetic map of the virulent Wolbachia strain wMelPop

We report here the construction of a physical and genetic map of the virulent Wolbachia strain, wMelPop. This map was determined by ordering 28 chromosome fragments that resulted from digestion with the restriction endonucleases FseI, ApaI, SmaI, and AscI and were resolved by pulsed-field gel electrophoresis. Southern hybridization was done with 53 Wolbachia-specific genes as probes in order to determine the relative positions of these restriction fragments and use them to serve as markers. Comparison of the resulting map with the whole genome sequence of the closely related benign Wolbachia strain, wMel, shows that the two genomes are largely conserved in gene organization with the exception of a single inversion in the chromosome.

Control of vector-borne disease by genetic manipulation of insect populations: technological requirements and research priorities

The possibility of controlling vector-borne disease through the development and release of transgenic insect vectors has recently gained popular support and is being actively pursued by a number of research laboratories around the world. Several technical problems must be solved before such a strategy could be implemented: genes encoding refractory traits (traits that render the insect unable to transmit the pathogen) must be identified, a transformation system for important vector species has to be developed, and a strategy to spread the refractory trait into natural vector populations must be designed. Recent advances in this field of research make it seem likely that this technology will be available in the near future. In this paper we review recent progress in this area as well as argue that care should be taken in selecting the most appropriate disease system with which to first attempt this form of intervention. Much attention is currently being given to the application of this technology to the control of malaria, transmitted by Anopheles gambiae in Africa. While malaria is undoubtedly the most important vector-borne disease in the world and its control should remain an important goal, we maintain that the complex epidemiology of malaria together with the intense transmission rates in Africa may make it unsuitable for the first application of this technology. Diseases such as African trypanosomiasis, transmitted by the tsetse fly, or unstable malaria in India may provide more appropriate initial targets to evaluate the potential of this form of intervention.