eChapters

Two schemes of labeling are in common use today. One variant labels a single sample, either radioactively or fluorescently. Radioactive labeling is used, e.g., in conjunction with hybridization on nylon membranes (Lennon and Lehrach, 1991). The company Affymetrix synthesizes sets of short oligomers on a glass wafer and uses a single fluorescent label (Lipshutz et al., 1999). Alternatively, two samples are labeled with a green and a red fluorescent dye, respectively. The mixture of the two mRNA preparations is then hybridized simultaneously to a common array on a glass slide. This technology is usually referred to as the Stanford technology (David Duggan et al., 1999). Quantification utilizes a laser scanner that determines the intensities of each of the two labels over the entire array. Recently, companies like Agilent have immobilized long oligomers of 60 to 70 base pairs length and used two-color labeling.

Appendix A: Diagrammatic Presentation of Processes Involved in Obtaining an Indian Patent

Abstract

Accurate forecasting of the rice yields is very important for the organization to make a better planning and decision making. The aim of the study is to investigate the ability of neural network for yield prediction of rice in Andaman and Nicobar Islands. Field experiments were conducted at the various regions of Andaman and Nicobar Islands during 1980 to 2002 and data collected was subjected to Artificial Neural Networks (ANN) approach for developing a meaningful yield prediction for four varieties of rice C14-8, Quing Livan No.1, Taichung , Sen Yu, Jaya under island conditions with the objective to estimate the crop production a few days or months before harvesting of crop. The observations of all experiments viz., Varietal spectrum, Weather parameters , Yield components, Soil, Abiotic stressors, Biotic stressors for the period of twenty years from 1980 to 2002 were collected from the basic records of experimental field. All the data were used for training data set. Prediction of crop yield, mainly strategic plant of rice has been an interesting research area to agronomists since long for its importance in national and international economic programming. Application of ANN was found to be significant tool for prediction of yield in rice.

Bioinformatics is the application of information technology to the field of molecular biology. The term Bioinformatics was coined by Paulien Hogewag, in 1978 for the study of informatic processes in biotic systems. Bioinformatics nowadays, entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Bioinformatics deals with storing and searching of large biological data sets and analysis of these data sets using computers to determine various relationships. 
The rapid developments in genomic and other molecular research technologies and developments in information technologies have been combined to produce tremendous amount of information related to molecular biologists. Now bioinformatics has developed and deals with many other types of biological data like protein structures, sequence alignment, gene finding, genome assembly, gene expression profiles, structure alignment, protein structure prediction, protein–protein interactions and the modelling of evolution. Being an interface between modern biology and informatics, it involves detection, expansion and implementation of computational algorithms and some of the software tools used to provide applications primarily in agriculture and healthcare divisions. In developing countries, bioinformatics play a major role in agricultural area where it can be used for increasing the nutritional content, and increasing the volume of the agricultural products etc.

Introduction 
Plant disease has been highly impacted by the development of new technology. The degree of the impact depends on how much the technology affects the component steps of the system. Many of the steps in plant disease diagnosis and management depend heavily on accurate and timely information and so can greatly benefit from the development of improved methods of accessing and disseminating information. Computers provide a wide range of tools which are extensively used in plant disease diagnosis and management. In fact, they will be a key to improve farm practices in the coming decades as it is scientifically pertinent to organize farm information in spatial databases because agricultural systems are inherently spatial. Biological and physical aspects of agricultural system create spatial heterogeneity, and as a result, patchiness is the rule in the occurrence and distribution of plant pathogens and disease (Campbell and Madden, 1990). In the area of plant diseases computers are useful

Biotechnology in the food processing sector targets the selection and improvement of microorganisms, including bacteria, yeasts and moulds with the objectives of improving process control, yields and efficiency as well as the quality, safety and consistency of bioprocessed products. They perform a series of processes such as fermentation, hydrolysis, etc. and even spoilage of food, depending upon the species of organisms, nature of raw material (food), environmental factors (temperature, pH, salt concentration, etc.), duration, etc.

Fermentation
Is the process of bioconversion of organic substances by microorganisms and/or enzymes (complex proteins) of microbial, plant or animal origin. It is one of the oldest forms of food preservation which is applied globally.

ANN is a Mathematical model or Computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. Neural Networks are non-linear statistical data modeling tools. That can be used to model complex relationships between inputs and outputs or to find patterns in data.

Bioinformatics is a discipline that deals with computation of biological information. Bioinformatics is a newly emerging interdisciplinary research area spanning a range of specialties that include biology, biophysics, computer science, mathematics & statistics. It makes use of scientific and technological advances in the area of computer science, information technology and communication technology to solve complex problems in life sciences particularly problem in Biotechnology.

Explosive growth in biological data from large sequencing projects that are producing nucleotide sequences continuously at a faster rate and the content of the nucleotide database is doubling in every 14 months. The latest release of Gene bank (v.102) exceeded one billion base pairs. To cope with this huge volume of data, a new scientific discipline has emerged comprising bioinformatics, biocomputing & computational biology.

Introduction

After the advent of Post Genomic era, Biological data is accumulating at an electrifying pace every day. Storage and analysis of these data serve as a major source for scientific research to understand the phenomena of origin, evolution and existence of various forms of life. The management of these data requires different statistical techniques used for retrieval, storage, analysis and extraction of precise information from small as well as large biological datasets. Application of statistical tools in Bioinformatics especially in fisheries data has been reported (Roy and Martha, 2008).

Abstract

The protein classification prediction is an important Problem in molecular biology, and one that has attracted a lot of attention, because there is a gap between sequence and structure of proteins from various resources. The proteins can be usually classified into one of the four structural classes; they are all-a, all-b, a/b and a+b. To address the problem of protein classification, many different algorithms and efforts have been made so far. In this paper, a new rule based data mining method for the prediction of protein structural classes is constructed using Rough Sets algorithm. Rough Sets Theory (RST) is a new mathematical approach to study the realistic information (Z. Pawlak, 1982). It focuses on knowledge reasoning by the whole set directly approximating to the incomplete and uncertain information. There are studies on the knowledge reduction and features selection of mass data. To apply rough sets algorithm, a decision table is constructed using Amino acid compositions and eight physicochemical properties data as conditional attributes. The generated decision rules after reducing the decision system can be used to classify new objects.The classification results of Rough Sets is verified by using self-consistency and jackknife tests on the datasets constructed by G.P. Zhou and comparison is made with some of the previous works.

In recent years, a new branch of biological science, called as bio-informatics receives more attention among its users and calls for application of computational statistical algorithms to a greater extent and needs employment of more sophisticated mathematical/statistical tools. Though biological science and information technology forms a major part in bio-informatics research as applied to agriculture, statistical tools act as their backbone. Its utility is more felt starting from the stage of framing of hypothesis till analyzing/interpreting results of a scientific experiment.  It can also be viewed as a three way multi-disciplinary approach with biotechnology, information technology and statistical technology as its three faces, with the principles of statistics applied to biological science through an interface information technology.

Influenza a Pandemics

Natural Gene Flow in Plants

More than 70% of plant species, for example potato, oat, rape-seed, wheat, and kiwifruit have come from spontaneous interspecific or intergeneric hybridization although the extent of natural hybridization differs among the plant genera and families. It creates genetic variation and produce recombinant genotypes that may exhibit heterosis.The stabilization of these hybrids often through polyploidy (autopolyploidy or allopolyploidy) can result in the formation of new biological species (Masterson, 1994), thereby, having impact on both ecological and evolutionary processes (Martinsen et al., 2001). In addition, species genomes are often differentially permeable to introgression if they are under simple genetic control (unlike many genes contribute to hybrid fitness with linkages) and certain portion of the genome can readily incorporate alien alleles. Introgression is a selective process of gene flow that filters deleterious genes and takes only beneficial genes (incorporating the alleles from one taxon into other).

A gene microarray is a (mxn) matrix, containing n-experimental conditions, also called attributes for m-genes. Gene clustering refers to partitioning a microarray of genes based on the commonality of attributes. Genes belonging to a cluster often do not coexpress or coregulate. This calls for biclustering that segregates the (mxn) matrix into small windows. In other words biclustering refers to simultaneous clustering of rows and columns of the microarray. This chapter reviews some of the well known biclustering algorithms, and proposes an alternative solution to the problem using evolutionary multi-objective optimization technique.

Introduction

Clustering is process to automatically partition a set of p-dimensional data points (p³1) into classes, where the data in each class have similarity with respect to a given distance metric. Each class usually has an ideal member that possesses the necessary characteristics of the class.

INTRODUCTION

Insects are the major group of arthropods and the most diverse group of animals across the Earth. Though some of them are beneficial to environment and to humans, many insects are considered as pests as they transmit diseases and damage the crops and forests. Chemical, biological, cultural and mechanical methods are being used to eradicate, control or manage these pests. The main strategy used for controlling pests has been the use of chemical pesticides because of their long residual action and wide spectrum toxicity (Casida and Quistad, 1998). However, the indiscriminate usage of chemical pesticides are toxic to mammals, birds and fish, also pollute the environment, besides the development of pest resistance and the emergence of secondary recalcitrant insect pest populations (Li et al., 2007). These negative impacts prompted the change of strategies in managing the pests by resorting to ecofriendly and cost effective pest control strategies (Ferre et al., 1991). 

Introduction

Saffron is a naturally derived plant product from the dried stigma of the Crocus sativus flower  (Gresta et al., 2009).The saffron (Crocus sativus L.) is an Iridaceae family member, herbal perennial, sterile (2n=3x=24) geophyte, traditionally propagated by vegetative corms. Its cultivation dates back to late Bronze Age in the Mediterranean region (Negbi, 1999) in Anatolia since the ancient times (Arslan et al., 2007) and for at least 3500 years in Egypt and Middle East. Saffron contains more than 150 volatile and aroma yielding compounds. The chemical components mainly include carotenoids, crocin, picrocrocin, safranal etc (Abdullaev, 1993, Tarantalis et al., 1995, Karaoglu 
et al., 2007). Saffron is mostly used as spice, food colorant, dye or perfume. The dried stigmas introduce unique color, taste and fragrance and thus making it the world’s most expensive spice (Baghlian et al., 2010). Saffron has been used in folklore herbal medicines for centuries (Basker and Negbi, 1983).

Bioinformatics is a recently emerged frontier discipline, which helps in analyzing biological data with the help of information technology and statistical software packages. In a broader sense, bioinformatics is the application of computer technology in management of biological information. This interdisciplinary scientific tool having no barriers between different branches of science like biology, biotechnology, mathematics, computer science and information technology, has great influence on advanced bio-science research, particularly dealing with genes, proteins and cell biology.

Bioinformatics is the branch of study that deals with the use of information technology (IT) for managing data of biological science. Bioinformatics drew public attention with the announcement of the draft sequence of human genome by the then US President Bill Clinton along with Craig Venter and Francis Collins in 2000 (Uma and Sathyanathan, 2004).

Since the dawn of the Industrial Revolution, mankind has been introducing numerous hazardous compounds into the environment at an exponential rate.  Environmental pollutants have become a major global concern. The modern growth of industrialization, urbanization, modern agricultural development, energy generation, have resulted in indiscriminate exploitation of natural resources for fulfilling the human desires and need, which have contributed in disturbing the ecological balance on which the quality of our environment depends.
 
Technological revolution has brought new changes in products and processes in industry. The waste generated from the development of products and processes are of concern to the environmental list. A variety of pollutants such as heavy metals, xenobiotics, polycyclic aromatic hydrocarbons (PAHs), chlorinated and nitro-aromatic compounds are found to be highly toxic, mutagenic and carcinogenic for living organisms (Zhang and Bennett. 2005; Samanta et al., 2002).

Bioinformatics is a multidisciplinary approach that combines computational and biological expertise to analyze biological data to advance research and development and it drew public attention with the announcement of the draft sequence of human genome by the then US President, Bill Clinton along with Craig Venter and Francis Collins in 2000. Bioinformatics and life sciences research are gaining attention in the government, industries and academic sectors. India was the first country in the world to establish a biotechnology information system (BTIS) network in 1987 through the Dept. of Biotechnology and created an infrastructure that enables it to harness biotechnology through the application of bioinformatics. Through this branch of science, biological data are acquired, stored and then analyzed. Bioinformatics can be used to store huge bioscience data for their future uses. By exploring computational tools, databases and new strategies for pests and diseases, resistant genes can be identified and plants with desired traits can be engineered to enhance agricultural productivity. Bioinformatics can be utilized in two ways for agriculture, viz., (i) at molecular level for application in breeding strategies and (ii) agri-informatics for land use planning, farmers window, early warning system, impact assessment and environmental applications.