eChapters

1.     Equipments

1.     General Precautions
    
2.     General Requirements for Biotechnology Laboratory

1.     Concentration

1.     Length and Size
2.     Volume
3.     Weight

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.