Glucoamylase immobilized on montmorillonite: Synthesis, characterization and starch hydrolysis activity in a fixed bed reactor

Glucoamylase from Aspergillus Niger was immobilized on montmorillonite clay (K-10) by two procedures, adsorption and covalent binding. The immobilized enzymes were characterized using XRD, surface area measurements and 27Al MAS NMR and the activity of the immobilized enzymes for starch hydrolysis was tested in a fixed bed reactor (FBR). XRD shows that enzyme intercalates into the inter-lamellar space of the clay matrix with a layer expansion up to 2.25 nm. Covalently bound glucoamylase demonstrates a sharp decrease in surface area and pore volume that suggests binding of the enzyme at the pore entrance. NMR studies reveal the involvement of octahedral and tetrahedral Al during immobilization. The performance characteristics in FBR were evaluated. Effectiveness factor (η) for FBR is greater than unity demonstrating that activity of enzyme is more than that of the free enzyme. The Michaelis constant (Km) for covalently bound glucoamylase was lower than that for free enzyme, i.e., the affinity for substrate improves upon immobilization. This shows that diffusional effects are completely eliminated in the FBR. Both immobilized systems showed almost 100% initial activity after 96 h of continuous operation. Covalent binding demonstrated better operational stability.

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.

Ionomer Compatibilized Low Density Polyethylene - Tapioca Starch Blends Biodegradability and Photodegradability

Biodegradation is the chemical degradation of materials brought about by the action of naturally occurring microorganisms. Biodegradation is a relatively rapid process under suitable conditions of moisture, temperature and oxygen availability. The logic behind blending biopolymers such as starch with inert polymers like polyethylene is that if the biopolymer component is present in sufficient amount, and if it is removed by microorganisms in the waste disposal environment, then the base inert plastic should slowly degrade and disappear. The present work focuses on the preparation of biodegradable and photodegradable blends based on low density polyethylene incorporating small quantities of ionomers as compatibilizers. The thesis consists of eight chapters. The first chapter presents an introduction to the present research work and literature survey. The details of the materials used and the experimental procedures undertaken for the study are described in the second chapter. Preparation and characterization of low density polyethylene (LDPE)-biopolymer (starch/dextrin) blends are described in the third chapter. The result of investigations on the effect of polyethylene-co-methacrylic acid ionomers on the compatibility of LDPE and starch are reported in chapter 4. Chapter 5 has been divided into two parts. The first part deals with the effect of metal oxides on the photodegradation of LDPE. The second part describes the function of metal stearates on the photodegradation of LDPE. The results of the investigations on the role of various metal oxides as pro-oxidants on the degradation of ionomer compatibilized LDPE-starch blends are reported in chapter 6. Chapter 7 deals with the results of investigations on the role of various metal stearates as pro-oxidants on the degradation of ionomer compatibilized LDPE-starch blends. The conclusion of the investigations is presented in the last chapter of the thesis.

Biodegradability Studies on LDPE-Starch Blends using Amylase-Producing Vibrios

Low-density polyethylene, (LDPE) was mixed with two grades of tapioca starch–lowgrade and high-grade. Various compositions were prepared and mechanical and thermal studies performed. The biodegradability of these samples was checked using a culture medium containing Vibrios (an amylase-producing bacteria), which was isolated from a marine benthic environment. The soil burial test and reprocessability of these samples were checked. The studies on biodegradability show that these blends are partially biodegradable. These low-density polyethylene-starch blends are reprocessable without sacrificing much of their mechanical properties

A novel Sigma–Delta based parallel analogue-to-residue converter

Animportant step in the residue number system(RNS) based signal processing is the conversion of signal into residue domain. Many implementations of this conversion have been proposed for various goals, and one of the implementations is by a direct conversion from an analogue input. A novel approach for analogue-to-residue conversion is proposed in this research using the most popular Sigma–Delta analogue-to-digital converter (SD-ADC). In this approach, the front end is the same as in traditional SD-ADC that uses Sigma–Delta (SD) modulator with appropriate dynamic range, but the filtering is doneby a filter implemented usingRNSarithmetic. Hence, the natural output of the filter is an RNS representation of the input signal. The resolution, conversion speed, hardware complexity and cost of implementation of the proposed SD based analogue-to-residue converter are compared with the existing analogue-to-residue converters based on Nyquist rate ADCs