Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Coastal regulation zone rules in coastal panchayats (villages) of Kerala, India vis-a` -vis socio-economic impacts from the recently introduced peoples’ participatory program for local self-governance and sustainable development

Coastal Regulation Zone (CRZ) notification was issued by the Ministry of Environment and Forest of Government of India in February 1991 as a part of the Environmental Protection Act of 1986 to protect the coast from eroding and to preserve its natural resources. The initial notification did not distinguish the variability and diversity of various coastal states before enforcing it on the various states and Union Territories. Impact assessments were not carried out to assess its impact on socio-economic life of the coastal population. For the very same reason, it was unnoticed or rather ignored till 1994 when the Supreme Court of India made a land mark judgment on the fate of the coastal aquaculture which by then had established as an economically successful industry in many South Indian States. Coastal aquaculture in its modern form was a prohibited activity within CRZ. Lately, only various stakeholders of the coast realized the real impact of the CRZ rules on their property rights andbusiness. To overcome the initial drawbacks several amendments were made in the regulation to suit regional needs. In 1995, another great transformation took place in the State of Kerala as a part of the reorganization of the local self government institutions into a decentralized three tier system called ‘‘Panchayathi Raj System’’. In 1997, the state government also decided to transfer the power with the required budget outlay to the grass root level panchayats (villages) and municipalities to plan and implement the various projects in their localities with the full participation of the local people by constituting Grama Sabhas (Peoples’ Forum). It is called the ‘‘Peoples’ Planning Campaign’’(Peoples’ Participatory Programme—PPP for Local Level Self-Governance). The management of all the resources including the local natural resources was largely decentralized to the level of local communities and villages. Integrated, sustainable coastal zone management has become the concern of the local population. The paper assesses the socio-economic impact of the centrally enforced CRZ and the state sponsored PPP on the coastal community in Kerala and suggests measures to improve the system and living standards of the coastal people within the framework of CRZ.

Socio-Economic Aspects of Sustainable Ecotourism Development: The Case of Kerala

The paper is an attempt to shed light on the socio-economic aspects of the local communities on the development of ecotourism in Kerala. Most of the local communities in the ecotourism destinations are tribes who have been excluded from the mainstream society and are not a part of Kerala’s overall development setting. The paper also tries to situate the community perception on the sustainable livelihood of ecotourism sites of Kerala. Data for the study is obtained from a primary survey by dividing the ecotourism destinations in Kerala into three zones, 230 from south zone, 220 from central zone and 200 from north zone with a total sample size of 650 based on the notion of community based ecotourism initiatives of the state. The result of the study confirms that ecotourism has helped to enhance the livelihood of the marginalized community. With well-knit policies it is possible to tag ecotourism of Kerala as an important tourism destination in the global tourism map