eChapters

9.1 Introduction

The objective of this chapter is to design an icloud and cayenne based perimeter monitoring system for agricultural field. Mobile app is designed to monitor the sensory data. The complete system comprises of two sections field device and mobile app. Field device comprises of NodeMCU, battery, motion sensors and laser sensors. The system is designed to monitor the agriculture field perimeter with motion and laser sensors, which are deployed at different locations of field. The sensory data is transmitted through NodeMCU and received at mobile app which can be accessed by authorized person. The same data can be accessed on webserver from anywhere in the world. Fig.9.1 shows a block diagram for a system. Table.9.1 shows the component list.

8.1 Introduction

The objective of this chapter is to develop a smart control for site-specific management of fixed irrigation system with Blynk app. Mobile app is designed to control the water management in field. The complete system comprises of two sections field device and mobile app. Field device comprises of Arduino, Node MCU, Power supply, LCD, Relay board, soil moisture sensor, temperature & humidity sensor, water level sensor, motor1, motor2. The system is designed to establish control and communication with specific agricultural field to take sensory data from the sensors and control the PUMP IN motor and PUMP OUT motor with the help of mobile app. Fig.8.1 shows a block diagram for a system.

7.1 Introduction

This chapter describes the development of data logger of agricultural field parameter monitoring system with MATLAB. The system comprises of Arduino, water level sensor, humidity sensor, light sensor, soil moisture sensor, temperature sensor, smoke detector, power supply, PC with MATLAB. The system is designed to monitor the environment and agriculture field parameters. The field device is designed to collect all physical parameters and same is communicated serially to MATLAB with I/O package. Fig.7.1 shows block diagram of the system. Table.7.1 shows the component list required to develop the system.

6.1 Introduction

This chapter describes the development of data logger of agricultural field parameter monitoring system with LabVIEW. The system comprises of two sections- Fields device and data logger. Field device comprises of Arduino, smoke detector (for sensing hazardous gases), soil moisture sensor, temperature sensor, humidity sensor, water level sensor, battery, display unit, 2.4 GHz RF modem. Data logger comprises of Arduino, display unit, 2.4 GHz RF modem, power supply, PC with LabVIEW. The system is designed to monitor the environment and agriculture field parameters. The field device is designed to collect all physical parameters and communicate it to the receiver end with LabVIEW. For establishing communication between two sections 2.4GHz RF modem is used, which can be replaced with any other modem also.Fig.6.1 shows block diagram of field device and Fig.6.2shows block diagram of data logger.Table.6.1 & Table.6.2 shows the component list required to develop the field device and data logger.

5.1 Introduction

The chapter describes the development of local web server on the laptop, to control the actuator from the server. Web server is used as remote control for the system. The system is designed to control water pump with respect to the soil sensor values. It comprises of NodeMCU, soil sensor, LCD, two relay boards for pump in and out the water from field.

4.1 Introduction

The objective of this chapter is to design a mobile app for agricultural field automation with Blynk app. Mobile app is designed to control the water management in field as per requirement. The complete system comprises of two sections sensor node and mobile app. Sensor node comprises of Node MCU, Power supply, LCD, Relay board, soil sensor and Ultrasonic sensor. The system is designed to establish control and communication with specific agricultural field to take sensory data from soil and ultrasonic level sensor and control the PUMP IN motor and PUMP OUT motor with the help of mobile app.Fig.4.1 shows a block diagram for a system to control specific agricultural field with mobile app and IoT modem.

3.1 Introduction

Internet of Things (IoT) allows computational devices and sensory support to connect with each other and access services on the Internet. The IoT idea was introduced to connect devices through the Internet and facilitate access to information for users. The potential application of IoT includes agriculture. The aim of this chapter is to explain the role of NodeMCU in the agriculture field. NodeMCU is IoT module which can be used to present the IoT concept as a basis for monitoring and control. The systems used in farm production processes. NodeMCU play a key role, with a focus on their realization by available microcontroller platforms and appropriate sensors. IoT based system provides opportunity to users to monitor and control the process remotely.

2.1 Introduction

Automation of agricultural field with IoT and microcontroller is new era of technology. Arduino based solution to agricultural field is open source platform. It can be used for developing data loggers and new sensors environment. The rate of solutions with Arduino and sensors is high as it is open source platform. The use of Arduino in agriculture can accelerate the growth of automation. Arduino can be interfaced with sensors like:

• Temperature
• Humidity
• Soil Moisture
• Water Pressure
• Water Flow and Water Metering
• Rain Gauge

To understand IoT “Internet of Things” it is important to understand internet first. Internet is connecting a device to other device anywhere in the world. When two devices are connected with the Internet, they can send and receive all kinds of information such as text, graphics, voice, and video. The high-speed, fiber-optic cables are used, through which the bulk of the Internet data travels. The Internet has revolutionized the communication world. In today’s scenario the internet is an important part for people around the world. The Internet is a global network of billions of electronic devices. The World Wide Web (www) is most widely used part of the Internet.

The “Internet of Things is a potentially integrated part of the ‘Future Internet’. IoT can be defined as a dynamic global network with self - configuring capabilities based on communication protocols where physical and virtual ‘Things’ interact with each other.

The Internet of Things (IoT) is inter-connecting the networks of smart devices. Smart devices may be embedded with vehicles, buildings, or other electronics object with software, sensors, actuators, and network connectivity. Network connectivity enables smart devices to collect and exchange data. The Internet of Things (IoT), also referred to as the Internet of Everything (IoE) which comprises of the web-enabled devices. IoT has its applications in smart city, smart surveillance, automated transportation, smarter energy management systems, water distribution, urban security and environmental monitoring. Agriculture is another new area of IoT application.

Due to advancement in technology the processing power and storage capacity has been increased, in the recent years. At the same time technology is making the devices pervasive, wearable and mobile. Smart devices are fitted with sensors, actuators which enable devices to sense, compute and act intelligently. They are able to exchange information with environment.

Introduction

Since the onset of human civilization molluscs have a tremendous impact on Indian tradition and economy. In India, molluscs have been in great demand as ornaments, currency, as popular panacea for illnesses and as mascot for ward off evil spirits in olden days. Now, molluscs have assumedgreater significance in our industrial, technological and aesthetic aspects of life, in addition to edible utility. Several species of gastropods and bivalves are traditionally fished for food and shell from time immemorial. About 80,000 - 1, 00,000 species of molluscs are recorded from the world over and a total of 3271 species are recorded from India. They are represented in 220 families and 591 general and the spectrum comprise of 1900 gastropods,1100 bivalves,210 cephalopods,41 polyplacophores and 20 scaphopods (edible and ornamental) and 14 species of cephalopods are exploited on commercial basis in India(Appukkuttan et al.,1989). It is difficult to categories shells satisfactorily according to their uses as many are collected for several purposes. The main categories are shown in the Table-1.

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). 

SYSTEMS BIOLOGY

The ultimate goal of any biological experiment is to understand biological systems in sufficient detail to enable accurate, quantitative predictions about the behavior of the systems, including predictions of the effects of modifications of the systems. To precisely simulate the behavior of the system using a computer model is one of the ways to achieve the above goal and in this venture we are nowhere close to the goal. Systems biology will help us to get closer to this goal and it is expected that biologists to adopt to take advantage of the power of systems biology approach. Systems biology is an emergent field that aims at system-level understanding of biological systems. Systems Biology is the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system. For example, theenzymes and metabolites in a metabolic pathway.With the progress of genome sequence projects and range of other molecular biology projects that accumulate in-depth knowledge of molecular nature of biological system, we are now at the stage to seriously look into possibility of system-level understanding of solidly grounded on molecular-level understanding.Unlike molecular biology which focus on molecules, such as sequence of nucleic acids and proteins, systems biology focus on systems that are composed of molecular components. Although systems are composed of matters, the essence of system lies in dynamics and it cannot be described merely by enumerating components of the system.

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.

Abstract

A Mathematical Method for Artificial Neural Networks offers an original, broad and integrated approach that explains each tool in a manner that is independent of specific ANN systems. Artificial Neural Networks are abstract mathematical models of brain structure and functions. They are nothing but a mathematical model that relies on varying the strength of connections between the internal processing elements to interpret data. They can be applied to many pattern classification problems. In this paper, it has been used to predict the level of amino acid in protein sequences. Amino acids are the building blocks of proteins. It is any molecule that contains both amine and carboxylic acid functional groups. Here, known protein sequences of ingredients are taken as input patterns. The network is trained using back propagation algorithm with sigmoid function as activation function by generating random weights. It is tested again with unknown protein sequence. Error is calculated by the difference between actual network output and desired output. If the training is successful the patterns are correctly classified. The network is trained until the error is minimum. The amino acid level is thus predicted in a particular protein sequence.

Abstract

The species of liver flukes of the genus Fasciola (Platyhelminthes: Digenea: Fasciolidae)are obligate parasitic trematodes residing in the large biliary ducts of the herbivorous mammalian hosts causing fascioliasis worldwide. While Fasciola hepatica has a cosmopolitan distribution, the other major species, i.e., F. gigantica is reportedly prevalent in the tropical and subtropical regions of Africa and Asia. To determine the phylogenic location of Fasciola sp. of Assam (India) origin based on rDNA molecular data,ribosomal ITS regions were sequenced (Accession numbers- EF027103 and EF027104) and compared with other species of trematodes in the family Fasciolidae that includes, Fasciolopsis buski and Fascioloides magna besides the species of Fasciola. NCBI databases were used for sequence homology analysis using BLAST; multiple sequence alignments were performed using ClustalW program.The phylogenetic trees constructed based upon the ITS (I & II) sequences by multiple tree building methods in MEGA revealed a close relationship with isolates of F. gigantica from China, Indonesia , Japan, Egypt and Zambia, the isolate from China with significant bootstrap values being the closest. Using the novel approach of molecular morphometrics that is based on ITS2 secondary structure homologies, phylogenetic relationships 

Data Mining is the process of automatic discovery of novel and understandable models and patterns from large amounts of data. Bioinformatics is the science of storing, analyzing and utilizing information from biological data such as sequences, molecules, gene expressions, and pathways. Development of novel data mining methods will play a fundamental role in understanding these rapidly expanding sources of biological data. Data mining techniques are an automated means of reducing the complexity of data in large bioinformatics databases and of discovering meaningful and useful patterns and relationships in data. Bioinformatics, the application of computational techniques to analyze the information associated with biomolecules on a large-scale, has now firmly established itself as a discipline in molecular biology, and encompasses a wide range of subject areas from structural biology, genomics to gene expression studies. It provides an introduction and overview of the current state of the field. The main principles that underpin bioinformatics analyses look at the Data Mining in Bioinformatics and databases that are commonly used, and finally examine some of the studies that are being conducted, particularly with reference to transcription regulatory systems.

The dream of having genomes completely sequenced is now a reality. The complete sequence of many genomes including the human one is known. However, the understanding of probably half a million human proteins encoded by less than 30’000 genes is still a long way away and the hard work to unravel the complexity of biological systems is yet to come. A new fundamental concept called proteome (PROTEin complement to a genOME) has recently emerged that should drastically help to unravel biochemical and physiological mechanisms of complex multivariate diseases at the functional molecular level. Proteomics involves the systematic study of proteins in order to provide a comprehensive view of the structure, function and regulation of biological systems. Coupled with advances in bioinformatics, this approach to comprehensively describing biological systems will undoubtedly have a major impact on our understanding of the phenotypes of both normal and diseased cells. Initially, proteomics focused on the generation of protein maps using two-dimensional polyacrylamide gel electrophoresis.

Keeping pace with latest technologies Central Institute of Freshwater Aquaculture (CIFA) has attempted to develop the following three databases on the following aspects for quick retrieval of information resources. The brief description of the databases is presented below for easy comprehension.

CD-ROM Database on Socio-Economic Aspects of Carp Culture in Kolleru Lake (Pictorial)

In the era of 21st century world is progressing fast towards the electronic media of communication and the real revolution has been brought about in the information technology. It has revolutionized the collection, storage and retrieval of information very speedily and accurately. One of the IT tools like CD-ROM (Compact Disc Read Only Memory) plays an important role in storing the huge data and can be easily carried to remote places. Since it is lightweight, pocket sized and can hold about 660 megabytes of data which is equivalent to 3 lakh pages. CD-ROM is a high capacity optical disc. It is 12 cm diameter, made of durable plastic with a reflective metal coating and lacquered surface. Considering all these advantages an attempt has been made to develop a database on socio-economic aspects of carp culture in Kolleru Lake which is immensely helpful to all scientific and farming community (Roy et al., 2004).

ABSTRACT

Gene expression profiling holds tremendous promise for dissecting the regulatory mechanisms and transcriptional networks that underlie biological processes. Microarray expression analysis has become one of the most widely used functional genomics tools. Typical approaches to gene identification and functional characterization have and continue to involve protein characterization, peptide sequence determination, and identification of the corresponding DNA sequence. More recently, expressed sequence tags (ESTs), microarrays, large-scale gene expression (transcriptome) profiling, and associated informatics technologies are rapidly becoming commonplace in the sciences. These ‘genomic’ approaches typically take advantage of technologies for characterizing large numbers of nucleic acid sequences, bioinformatics, and the expanding collection of nucleic acid sequence data.

INTRODUCTION
 
Environment and ecological set up is maintained by the climatic conditions, presence and compositions of gases present in them, humidity level, temperature and the pollution. These conditions are responsible for the emerging of diseases in the environment. The diseases in plants, animals and human beings are due to the factors that affect the ecology. This also depends on the organism whether they are susceptible or resistant to the specific disease. This means the genetic level of the organism is an important factor.
Plant pathology or phytopathology is the study of diseases in plants. The disease evolution may be due to the climatic condition, humidity level, temperature and the genetic character of the plant and the pathogen. The parasitic adaptation of microorganisms to be plant pathogens includes acquisition of the following three abilities: 1) ability to enter into a plant, (2) ability to overcome the resistance of the host and (3) ability to evoke disease.

ANTIBIOTICS

Antibiotic is a drug used to treat infections caused by bacteria and other microorganisms. Technically an antibiotic is a substance produced by a microorganism that selectively inhibits the growth of another. Penicillin was the first antibiotic discovered by Alexander Fleming in 1928, a significant breakthrough for medical science. Antibiotics had saved thousands of wounded soldiers in the I and II world wars. Also, use of it made surgery safer and increased the average life expectancy of human beings. Antibiotics are among the most frequently prescribed medications in modern medicine. Some antibiotics are ‘bactericidal’, meaning that they work by killing bacteria. Each type of antibiotic affects different bacteria in a different way. Some antibiotics can be used to treat a wide range of infections and are known as ‘broad-spectrum’ antibiotics. Others are only effective against a few types of bacteria and are called ‘narrow-spectrum’ antibiotics.

Algae refer to an assemblage of polyphyletic organism that conducts oxygen evolving photosynthesis other than land plants. They are distributed widely throughout India in the sea, in fresh water and in moist situations on land. Algae also form an important component of biodiversity of Andaman and Nicobar Group of Islands. Database for Algae resources of Andaman and Nicobar Islands are created with an aim of providing an information pool to the researchers working in fields related to fresh water and marine water algae in Andaman and Nicobar Group of Islands. About 313 fresh water algal species belonging to 15 families and 57 marine algal species belonging to 18 families has included in this database among which 40 species has been found to be first time reported in India. Algae have a wide application in a variety of Industries such as Food, Fodder, and Fertilizer etc. They are also important sources of Protein, Iodine, Vitamins, Minerals and Substances of Antibiotic Nature.

The role of bioinformatics in genomics and molecular biology research can be likened to the role of intelligence gathering in battlefield. Intelligence is clearly very important in leading to a victory in the battlefield. Computational algorithms are the heart of bioinformatics as they enable predictions, understanding and interpretation of the data generated from laboratory experimental research carried by biologist. This article discusses one such approach called Decision tree induction and its application for generating rules from a dataset extracted from biological databases.

Decision Tree (DT) is one of the most popular choices for learning from feature based examples. They are especially attractive in data mining environment for several reasons. First due to their intuitive representation, the resulting classification model is easy to assimilate by humans. Second, DT does not require any parameter setting from the user and thus are especially suited for exploratory knowledge discovery. Third DT can be constructed relatively fast and accuracy of DT is comparable or superior to other classification models.

The coral reef ecosystem is one of the most diverse ecosystems, which is housing for an amazing diversity of organisms and estimation of species richness ranges from 650 000 to over 9 million. For this reason, coral reefs often are referred to as the “rainforests of the ocean”1.Prokaryotes represent overriding life form on the planet and are almost certainly the most diverse component of coral reef communities2. Corals have been known to harbour many different species of bacteria.

The rise in the number of drug-resistant pathogens and the limited success of strategies in providing new agents indicate an uncertain forecast for future antimicrobial therapy. So it is critical that new groups of microbes from unexplored habitats be pursued as sources of novel antibiotics. Marine actinomycetes serve as a logical target given the historical significance of their terrestrial counterparts in antibiotic production. Isolation strategies directed towards new marine-derived actinomycetes have been lacking, but it is clear that such procedures will increase the understanding of marine bacterial diversity and yield many novel marine natural products. Though recent literatures report the isolation of novel class of actinomycetes from marine sediments and sponges, reports on actinomycetes associated with corals are very scanty. Therefore in this study we attempted to characterize the actiomycetes diversity associated with the coral Acropora digitifera from the Gulf of Mannar, Southern coast of India. The antibacterial potential of the isolated actinomycetes was also investigated.

A fundamental problem facing researchers today is how to make effective use of available wealth of online background domain knowledge to improve their understanding of complex biological system. Effective use of such online background domain knowledge plays a crucial role in all stages of integrated biological studies.

In the past years, research in molecular biology and molecular medicine has accumulated enormous amounts of data. This includes genomic sequences gathered by the Human Genome Project, gene expression data from micro array experiments, protein identification and quantification data from proteomics experiments, and SNP data from high-throughput SNP arrays. However, the understanding of the biological processes underlying these data lags behind. There is a strong interest in employing methods of knowledge discovery and data mining to generate models of biological systems. Mining biological databases put forward challenges which knowledge discovery and data mining have to address in the future. Important background knowledge in bioinformatics is often contained in documents, such as scientific publications or database annotations. Analyzing data from biological databases often requires the consideration of data from multiple relations rather than from one single table. Recently, approaches (such as propositionalization algorithms) are being studied that utilize multi-relational data and yet meet the efficiency requirements of large-scale data mining problems.

Neisseria meningitidis, also simply known as meningococcus, is a gram-negative bacterium best known for its role in meningitis. It only infects humans; there is no animal reservoir. It is the only form of bacterial meningitis known to cause epidemics. The carbohydrate capsule of N. meningitidis determines its virulence and is targeted by the immune system. Approximately 12 strains of N. meningitidis exist and are characterized by the polysaccharide expressed on its capsule: A, B, C, 29-E, H, I, K, L, W-135, X, Y and Z. Inhibition of Pencillin Binding proteins (PBPs) leads to irregularities in cell wall structure such as elongation, lesions, loss of selective permeability and eventual cell death and lysis. 3-D structure of PBP was studied using Bioinformatics tool. Using Homology modelling, converted protein sequence of penicillin binding protein, extracted from NCBI server, in FASTA format and performed PDB-BLAST to search a desired template. Selection of template was based on lower E-value, bit score and identity. Using MSA with the help of CLUSTAL W software conserved region was found between the target and template sequence, 1K25. The 3-D structure of penicillin binding protein was found with the help of Modeller 9v2 and molecular dynamics was performed using GROMACS software. The docking was performed (Autodock 3.0 software) using 3 ligands viz. Cefuroxime, Chloramphenicol, Cefalotoxime with standard Bioinformatics tools.

Among several respiratory diseases affecting man, bronchial asthma is the most common disabling syndrome, nearly 7 to 10 per cent of the world population suffer from bronchial asthma, which is a chronic illness involving the respiratory system in which the airway occasionally constricts, becomes inflamed, and is lined with excessive amounts of mucus, often in response to one or more triggers. These episodes may be triggered by such things as exposure to an environmental stimulant (or allergen), cold air, warm air, moist air, exercise or exertion, or emotional stress. In children, the most common triggers are viral illnesses such as those that cause the common cold. A variety of exogenous determinants of asthma were also been, some of which are air pollution, tobacco smoking, diet, occupation and respiratory infection, these pollutants may serve as inciters or triggers of asthmatic reaction in airways that are already hyper responsive, they may exert direct toxic influence on the respiratory epithelium or may augment or modify immune responses to inhaled antigens. During an asthma episode, inflamed airways react to environmental triggers such as smoke, dust, or pollen. The airways narrow and produce excess mucus, making it difficult to breathe. In essence, asthma is the result of an immune response in the bronchial airways. As according to the World Health Organization over 80% of the people in developing countries depend upon traditional medicine for their primary health care. Medicinal plants are considered as source of various alkaloids and other chemical substances essential for mankind.

Biodiversity conservation and sustainable use has, for many people, become associated mainly with issues related to plants and wild animals. Although much less discussed, the loss of farm animal genetic resources may well be much more serious than in crops because the gene pool is smaller and very few wild relatives remain. The fact that 32% of livestock breeds worldwide are at risk of becoming extinct and that the rate of extinction continues to accelerate (FAO, 2000) is thus a serious cause for concern. Animals of different characteristics and hence outputs suit differing local community needs.

The union territory of A & N Islands constitutes the farthest and the remotest part of Indian union. The islands are situated in the Bay of Bengal surrounded by sea. The neighboring countries are Burma in north east and Singapore, Malaysia and Indonesia in south east. The Union territory of A & N Islands is situated between 60 and 140 north latitude and 920 and 940 east longitude forming a broken row of continuous islands from. North to South direction. They can be divided into two groups namely Andaman and Nicobar groups. The dreaded 100 channel which is about 145 Kms wide and 400 fathoms deep, separates the two groups

Bioinformatics can be defined as “the collection, archiving, organization and interpretation of biological data” (Orengo et al., 2003). By extension, structural bioinformatics deals with the collection, archiving, organization and interpretation of 3-D structural data of bio macromolecules. Structural bioinformatics emerged with the first crystal structures of haemoglobin and myoglobin. These structures were analyzed and contributed significantly to our understanding of the principles of life at a molecular level. A repository of all publicly accessible protein crystal structures, the Protein Data Bank (PDB) was established by Walter Hamilton et al., at Brookhaven National Laboratory in 1971. Until the late 1980s, structural biologists often knew all the available structures of bio macromolecules and much of the analysis, such as structure comparison and hydrogen-bonding analysis was done manually without much computer intervention. As the number of available structures increased, so did the need for software tools for structure analysis. Structural bioinformatics encompasses a wide variety of applications, including structure visualization, validation, classification, structure-based function prediction, identification of targets for structural genomics projects and prediction of the structures of individual bio macromolecules and complexes as well the binding modes of small molecules inside proteins of pharmaceutical or biotechnological interestStructural and theoretical analyses of proteins are central to the understanding of complex molecular mechanisms and are fundamental to the drug discovery process.

Bioinformatics can be defined as the application of computer technology for the management and analysis of biological data using computer to gather, store, analyze and merge the biological data. It consists of the organization of many kinds of large-scale biological data, particularly that based on DNA and protein sequence, into web-accessible databases and the methods required analyzing these data. Bioinformatics is a fast growing field within the biological sciences that was developed because of the need to handle large amounts of genetic and biochemical data. The goal of bioinformatics is to uncover the wealth of biological information hidden in the mass of data and to obtain a clearer insight into the fundamental biology of organism. This knowledge can have profound impact on various fields such as human health, agriculture, environment, energy and biotechnology. Bioinformatics involves use of various biological databases, which are archives of consistent data that are stored in a uniform and efficient manner. Biological databases are large collections of data that are relatively different to maintain outside the centers and institutions that produce them. These data are traditionally accessed and analysed using browser-based World Wide Web (www) interfaces. There are two types of databases i.e. Primary database and Secondary database. Primary databases contain information and annotations of DNA and Protein sequences, DNA and Protein structure and DNA and Protein expression profiles catalogued for the end users.

A genome is an organism’s complete genetic signature and the collection of information that an organism can pass on to its offspring. Genomics, the study of the structure, function, and evolution of entire genomes is principally dealing with genetic information which helps us to understand how organisms and ecosystems work, how theymalfunction, or how they might be melded to human needs. The model organisms used in genomics have, in general, small genomes and short generation times. To meaningfully analyze the gene-to-phenotype relationships, a large amount of sequencing information is necessary. Progress in DNA sequencing technologies leads to accumulate Information about structures of genomes (Mohan Kumar, 2007).

HISTORY OF INSECT GENOMICS

Commencing from the publication of the entire nucleotide sequence of the genome of Haemophilus influenzae in 1995, a complete genome sequence of many organisms, including lower and higher eukaryotes, have been reported. The cost-effective way to get sufficient sequence information to address the questions is to sequence a high-quality reference genome and then map sequences from other genomes onto the reference sequence. Insects constitute the vast majority of known species with their importance including biodiversity, agricultural, and human health concerns. Hence, a study of Insect genomics is imperative to elucidate information to other organisms.

ANALYSIS OF CODON USAGE VARIATION

Multivariate analysis methods such as Correspondence Analysis and Principle Component Analysis have often been used to identify major trends of variation in synonymous codon usage among inter or intra specific genes. Genetic codes are degenerate meaning most of amino acids can be encoded by more than one codon (triplet of nucleotides); such codons are synonymous and usually differ by one nucleotide in the third position. The alternative synonymous codons are not used with similar frequency and their usage varies among different genes. Hence there is the presence of Relative Synonymous Codon Usage values. 

SEQUENCE DATA

The gene sequences downloaded from Genbank of National Center for Biotechnology Information that is the nucleotide sequence repository known worldwide, serves as the secondary data used for analysis. Relative synonymous codon usage value of each gene is extracted separately using the Codon Usage software of Sequence Manipulation Suite. These are the raw data which needs to be normalized to be used before application of various multivariate analysis.

In the context of molecular biology, a consensus sequence is the minimum nucleotide sequence that shows the characteristics common to most members of the family among a set of related genetic sequences.It serves in representing the results of a multiple sequence alignment, where related sequences are compared to each other, and similar functional sequence motifs are found. It also shows conserved/variable residues. It is found several times in the genome and is thought to play the same role in its different locations. For example, many transcription factors recognize particular consensus sequences in the promoters of the genes they regulate. In the same way restriction enzymes usually have palindromic consensus sequences, usually corresponding to the site where they cut the DNA. Transposons act in much the same manner in their identification of target sequences for transposition. Splice sites, i.e. those sequences immediately surrounding the exon-intron boundaries, can also be considered as consensus sequences. Defining a consensus DNA binding site by examining consensus sequences is a key work carried out by biologists (Harley et al., 1994). Rareness or anomaly detection followed by in depth analyses in such sequences may reveal significant information regarding the structural, functional and biochemical mechanisms of the genes which are of much importance to biologists.

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.

Mitochondria are key energy generators in most eukaryotic cells. In recent years a number of mitochondrial genomes have been completely sequenced, contributing to the knowledge of molecular features related to function and evolution of this peculiar genome (Boore, 1999). The pathways of mitochondrial DNA evolution are obscured by their extraordinary size. Gene content, gene rearrangement and mode of expression are the characteristics of mitochondrial genomes. The mitochondrial genomes from eukaryotes reveal large size differences between animal, fungal and plant species. The average size of animal mtDNA is ~ 16 kb (Boore, 1999), whereas plant mtDNAs range from 200 to 2500 kb (Palmer, 1990). The size of fungal mtDNA’s range from 19 kb to 176 kb (Hudspeth, 1995).The mtDNA genome has a limited number of protein coding genes such as NADH ubiquinone oxidoreductase (ND2 to ND5), cytochrome c oxidase (COX), succinate dehydrogenease (SDH), ATP synthase (ATP6), cytochrome (CYTB), etc. Among non-coding genes only rRNA and tRNA genes are found.Ribosomal RNA (rRNA) is a component of the ribosomes, the protein synthetic factories in the cell. rRNA molecules are extremely abundant. They make up at least 80% of the RNA molecules found in a typical eukaryotic cell. Synthesis of the three nucleolar rRNA molecules is unusual as they are made on one primary transcript that is chopped up into three mature rRNA molecules.

The development of bio-informatics started with the working of computers and accumulation of data on genes and proteins. There are 100,000 proteins in the human body or 1 million. In the last decade the number of protein sequences gathered in databases increased tremendously, but the key to understand the function of a protein is not its sequence but its structure. Experimental determination of 3 dimensional native folded structure of a protein usually can be done via crystallography techniques. But such experiments are time consuming. Currently there are hundreds of different fully automated approaches to protein 3D structure prediction.

All proteins are polymers of Amino acids. The polymers also known as polypeptides consist of a sequence of 20 different L-m-amino acids also referred to as residues. For chains under 40 residues the term peptide is frequently used instead of protein. To be able to perform their biological function, proteins fold into one that is driven by a number of non covalent interactions such as hydrogen bonding, ionic interaction, Vander Waal’s forces and hydrophobic packing. In order to understand the functions of proteins at a molecular level, it is often necessary to determine the three dimensional structure of proteins. The modern Information Technology techniques like Artificial Intelligence have opened new pathways to researchers. Once ANN has been trained it is capable of producing accurate output for the unknown inputs. Many researchers have tried to analyze the large sequence sized gene to determine the protein, predict the secondary structure of the protein and model its three dimensional (3D) view [11].

Today, when the collection of biological data has been increasing at explosive rate, the processing of these data is needed. It would be extremely valuable if the specialists who know what the data mean were also able to imagine ways to collect, process and exploit these data. Biological and agricultural scientists should note that concepts from computer science, discrete mathematics and statistics are being used increasingly to study and describe biological systems. The specialists, who know the subjects of mathematics, statistics and computer programming, are needed for solving the computational problems in biology. Statistical data mining approaches seem ideally suited for bioinformatics, since it is data rich, but lacks a comprehensive theory of life organization at molecular level, or in the words of I.E. Alcamo “ Keeping up with the directions and application of DNA is a never ending job.” Starting with possible definitions of statistical data mining and bioinformatics, this chapter will give a general side view of both the fields on the statistical data mining part and its technologies and discuss how statistical data mining may be used in bioinformatics.

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.

This statement signifies that the casualty of people is not due to the earthquake but due to the damage of buildings by earthquake forces. In developed countries, the casualty and loss of property is very minimal in comparison to the death toll and losses in India in the event of earthquakes of same magnitude. It has been experienced in the past that on the event of earthquakes of Richter magnitude scale of 6 or more, the death toll in India is in terms of thousands where as in Western Countries, it is multiple of tens. The less casualty is due to less damage of buildings and other infrastructures which in turn is due to the design and construction of Buildings and other structures as per the earthquake resistant theory. This paper discusses the issues related to the earthquake hazards in India and the concept of earthquake resistant structures.

Influenza a Pandemics

Drug designing is the approach of finding drugs by design based on their biological targets. The substance used for medical purpose, either alone or in mixture that changes the state or function of cells, organs or organism as a whole is known as a drug. So typically a drug target is a key molecule involved in particular metabolic or signaling pathway that is specific to disease condition or pathology, or to the infectivity or survival of a microbial pathogen. Drugs may be designed that bind to the active region and inhibit the key molecule that causes disease. However these drugs would also have to be designed in such a way that they should not affect any other important molecules similar to the key molecule. These can also be used to enhance the normal pathway of metabolism by promoting specific molecules that may have been suppressed in disease state.

In recent years, we have witnessed an explosion of biological information and the bioinformatics data arising from the rapid research and unprecedented progress in molecular biology. The novel genomic technologies have revolutionized the drug designing, discovery and development processes in government as well as private pharmaceutical industries through the use of these technologies. Various databases are doubling in size every year and fortunately we have the complete genome sequences of more than 100 organisms. At present, the pharmaceutical industries have clasped genomics as a source of drug targets. They also distinguish that the field of bioinformatics is crucial for validating these potential drug targets and for determining the ones that are most suitable for entering the drug development pipeline. Due to our increased understanding of molecular biology, there has been a change in the way that medicines are being developed. Once upon a time, new synthetic organic molecules were tested in animals or in whole organ preparations. Now this approach has been replaced with a molecular target in which in-vitro screening of compounds against purified, recombinant proteins or genetically modified cell lines is carried out with a high throughput as a consequence of better and ever improving knowledge of the molecular basis of disease.

The genetic code uses 64 codons to represent the 20 standard amino acids and the translation termination signal. Each codon is recognised by a subset of a cell’s transfer ribonucleotide acid molecules (tRNAs) and with the exception of a few codons that have been reassigned in some lineages (Osawa and Jukes 1989; Osawa et al., 1990) the genetic code is remarkably conserved, although it is still in a state of evolution (Osawa et al.,1992). The genetic code uses 64 codons to represent the 20 standard amino acids and the translation termination signal. Each codon is recognised by a subset of a cell’s transfer ribonucleotide acid molecules (tRNAs) and with the exception of a few codons that have been reassigned in some lineages (Osawa and Jukes 1989; Osawa et al., 1990) the genetic code is remarkably conserved, although it is still in a state of evolution (Osawa et al., 1992).

Insights into the three-dimensional (3D) structure of a protein are of great assistance when we plan experiments to understand protein function and in assistance of the drug design process.

Homology modeling
    
·    Homology modeling consists of the extrapolation of the structure for a new (target) sequence from the known 3D-structure (X-ray crystallographic or NMR) of related family members (templates).
    
·    Homology modeling is for those targets that have homologous proteins with known structure (>30% identity)
    
·    Homology modeling is for easy targets.
    
·    Homology modeling treats the template in an alignment as a sequence and only sequence homology is used for prediction.
    
·    When the sequence identity between the target and template sequence is low (<25%), homology modeling may not produce a significance result.

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.

Objectives
    
·    Creation and use of large databases of Biological information to augment traditional laboratory based biology.
    
·    Analysis and interpretation of genomic and structural data using computational techniques from Mathematics, Statistics and Computer Science.
    
·    Explain the process and mechanism of evolution by comparing the Genomes of different species.
    
·    Development of new methods and technologies for understanding and diagnosis of genetic diseases and design of drugs for their treatment.

Motif

1.    A nucleotide/amino acid sequence pattern having biological significance.

2.    Occurs repeatedly, either in same molecule or in many molecules. Found in upstream intergenic region.

3.    Functionally important regions of genome (gene) can be recognized by searching such patterns.

4.    Used for locating binding sites, regulatory signals, to control gene expression.

5.    Identifying potential drug target sites.

Sequence Alignment

Sequence Alignment is the procedure of comparing two (Pair-wise Alignment) or more (Multiple Sequence Alignment) sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences.

Significance: Discovering functional, structural and evolutionary information in biological sequences.

Biological sequences like the nucleotide and amino acid sequences can be analyzed by means of the sequence alignment. The objective behind this is to obtain knowledge regarding the similarity between the query and the target sequence to decipher the structure, function, homology of the query sequence.

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).