Population Genetic Structure of the Marine Penaeid Prawn- Penaeus Indicus H. Milne Edwards 1837

The thesis contains the results of an investigation on the " Population Genetic Structure of the Penaeus indicus " from southeast and southwest coasts of India. The P.indicus, popularly known as the Indian white prawn, is distributed widely in the Indo-Pacific, starting from New South wales in Australia in the east to the east coast of Africa in the west. Its heavy demand in the export market, the species has been exploited intensively from all along its areas of distribution in Indian waters. The population genetic characteristics of the species were examined by three independent but complementary techniques, namely, morphometrics (truss network), biochemical genetics (isozyme electrophoresis ) and molecular genetics (RFLP and RAPD). The east and west coast populations of the species may be genetically different. Due to certain constraints, the results obtained from the studies of restriction fragment length 70 polymorphism (RFLP) were limited. The significant difference in the number of bands in the sample populations strongly suggests that these two populations have considerably different population genetic structures

“Reconstruction of Gene Regulatory Network from Expression Profile of Plasma RNA Data of Colorectal Cancer Patients using Soft Computing Techniques”

Microarray data analysis is one of data mining tool which is used to extract meaningful information hidden in biological data. One of the major focuses on microarray data analysis is the reconstruction of gene regulatory network that may be used to provide a broader understanding on the functioning of complex cellular systems. Since cancer is a genetic disease arising from the abnormal gene function, the identification of cancerous genes and the regulatory pathways they control will provide a better platform for understanding the tumor formation and development. The major focus of this thesis is to understand the regulation of genes responsible for the development of cancer, particularly colorectal cancer by analyzing the microarray expression data. In this thesis, four computational algorithms namely fuzzy logic algorithm, modified genetic algorithm, dynamic neural fuzzy network and Takagi Sugeno Kang-type recurrent neural fuzzy network are used to extract cancer specific gene regulatory network from plasma RNA dataset of colorectal cancer patients. Plasma RNA is highly attractive for cancer analysis since it requires a collection of small amount of blood and it can be obtained at any time in repetitive fashion allowing the analysis of disease progression and treatment response.

A Multi-Objective Antenna Placement Genetic Algorithm for Matched Array Synthesis on Complex Platforms

A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR