Investigations on novel polymer nano composites and bio-compatible manganese doped ZnS nanocrystals for photonic and bio-imaging applications

Fluorescence is a powerful tool in biological research, the relevance of which relies greatly on the availability of sensitive and selective fluorescent probes. Nanometer sized fluorescent semiconductor materials have attracted considerable attention in recent years due to the high luminescence intensity, low photobleaching, large Stokes’ shift and high photochemical stability. The optical and spectroscopic features of nanoparticles make them very convincing alternatives to traditional fluorophores in a range of applications. Efficient surface capping agents make these nanocrystals bio-compatible. They can provide a novel platform on which many biomolecules such as DNA, RNA and proteins can be covalently linked. In the second phase of the present work, bio-compatible, fluorescent, manganese doped ZnS (ZnS:Mn) nanocrystals suitable for bioimaging applications have been developed and their cytocompatibility has been assessed. Functionalization of ZnS:Mn nanocrystals by safe materials results in considerable reduction of toxicity and allows conjugation with specific biomolecules. The highly fluorescent, bio-compatible and water- dispersible ZnS:Mn nanocrystals are found to be ideal fluorescent probes for biological labeling

“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.