eChapters

Abstract
Proteins are comprised of domains, folds and motifs, which form its basic building blocks. Most efforts to understand protein evolution and its function have focused on domains as these can fold into a stable three-dimensional structure independent of the rest of the protein chain and generally perform a unique function conserved over the evolution. Genetic recombinant techniques allow reorganization of domains, called domain shuffling, resulting in different combinations of domains in different proteins. This along with swapping and insertion of domains results in complex architectures and are responsible for new protein functions in evolution. Protein domains are very useful in analyzing the mechanisms of protein folding and their stability and structural transformations in various conditions. This can be done reliably if a multi-domain target is considered only in terms of its constituent domains. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently.

Introduction

Natural evolution gifted the biological world a treasure of diverse bio-molecules that take part in various cellular activities. Proteins, one of the vital molecules of biological function are wisely folded polymers comprising 20 different aminoacids. Function of each protein is encoded in its aminoacid sequence. Aminoacid residues can indulge in bonded or non-bonded interactions depending on the environment. These inter residue interactions with in a polypeptide drive its folding in to a compact conformation from a linear molecule. Biochemical properties as well as the linear arrangement of the aminoacids determines the three dimensional structure of a protein. These determinants of defined structure directly or indirectly dictates functional fate of the folded protein also.

Abstract

Biological Pathways represent system level sophistication of organisms. An exclusive peek into the extent of sophistication of an organism may provide proof of its past evolutionary struggle and position in the developmental trend from pathway point of view. This knowledge may not align with the traditional universally acknowledged standard 16s rRNA trend. Analyzing the reasons behind such discrepancy can constitute an important research work. To map sophistication of one organism with respect to other organisms, phylogeny is required. Here, we are mapping the level of sophistication in terms of enzymes among a set of TCA cycles belonging to different species, arbitrarily chosen from the KEGG/PATHWAY database. For this purpose, we converted the metabolic pathway information into simplified enzyme-enzyme relational (E-E-R) graphs. Similarity scores obtained from inter-genus, intra-genus as well as inter-species comparison of these graphs are used for phylogenetic tree construction. These phylogenetic trees throw light on the trend of development of metabolic pathways among different species. But peculiarly, these trees have less similarity with the conventional evolutionary phylogenetic trees constructed from NCBI taxonomy data. In this paper, we have tried to find some justifications for this dissimilarity among phylogenetic trees, by considering their habitats.

Abstract
Combined advancement in computer sciences and hardware with uniformly significant developments in molecular biology and chemistry are providing toxicology with a powerful new tool box. This tool box of computational models promises to increase the efficiency and the effectiveness by which the hazards and risks of environmental chemicals are determined. Computational toxicology focuses on applying these tools across many scales, including vastly increasing the numbers of chemicals and the types of biological interactions that can be evaluated. Apart from that, knowledge of toxicity pathways gathered within the tool box will be directly applicable to the study of the biological responses across a range of dose levels, including those more likely to be representative of exposures to the human population.  Although inappropriate pharmacokinetic properties are a major cause of abrasion in the safety issues are recognized as today’s single largest cause of drug candidate failure. It is expected that the right balance of in vivo, in vitro and computational toxicology predictions applied as early as possible in the discovery process will help to reduce the number of safety issues.

Livestock have the potential to be transformative; by enhancing food and nutrition security and providing the income to pay for education and for other needs, livestock can transform lives of many (Jimmy Smith et al., 2013). The projected growth of population is 9.6 billion in the year 2050 from the current population of 7.2billion (Gerber et al, FAO, 2013). The world demand for milk and meat in 2050 is expected to expand 58 and 73% respectively from their demand levels in 2010 (FAO, 2011). Nevertheless, the growth animal agriculture is not on par with the demand. Today, in a world of limited resources, in the face of climate change and a rising world population, a major issue is global food security (Augustine et al 2013). The key inferences of the latest approved summary for policymakers by the IPCC (2013) on climate change are 1). Warming of climate system is unequivocal and will continue beyond 2100. Global surface temperature change for the end of the 21st century is expected to exceed 1.5°C relative to 1850 to 1900 for almost all RCP scenarios 2). It is very likely that frequency of heat waves and heavy precipitation events have increased 3).

Modern research on life science is progressing at the nanoscale not only with the objective to reduce time and cost but also to ease the handling and working conditions. Application of nanotechnology in various domains of science has led to the enormous innovations. This field facilitates better understanding as well as treatment of living system, activate biotechnological processes, synthesizing new drugs for targeted delivery and biocompatible materials for sustainable environment. Throughout the world nano science is getting wide propaganda. Its application to medicine is increasing rapidly in order to develop nano-drugs with prolonged availability and efficient delivery through rearrangement of molecules at the nanoscale using molecular interactions for example van der Waals forces, H bonds, electrostatic dipoles and various surface forces.

Conservation Agriculture (CA) is defined as a process of ‘sustainable intensification of agriculture’ for enhancing and sustaining production at low cost, without deteriorating soil health and flow of environmental services. The process of agricultural intensification increases food production and spares land for the other uses including eco-system services. In line with the vision of Norman Borlaug, agricultural intensification is also known as ‘The Borlaug Effect’. Frameworks such as ‘Conservation Agriculture’, ‘Ecological Intensification’ and ‘Ever Green Revolution’ share a common view of cropping systems as agro-ecosystems designed to make maximum use of fixed resources (land, light and temperature etc.) with optimum use of agri-inputs for attaining sustainable production levels (Cassman 1999)’. Conservation agriculture is also considered as an important component of the overall strategy for ensuring food security, poverty alleviation, health for all, rural development, enhancing productivity, improve environmental quality and preserve natural resources. With limited scope for further expansion of area under agriculture, production gains can be accomplished through intensification of agriculture, achieved by pursuing one or more strategies (with rationale use of natural resources as well as external production inputs) enumerated as under:

In India, being predominantly an agricultural country, livestock is emerging as a driving force in the growth of agricultural sector. Contribution of livestock to agriculture GDP has been rising steadily during the last three decades from 13.88% in 1980-81 to 27.28% in 2010-11 (BAHS 2012). About 70% population of the Country is engaged in agriculture and more than 50% of people below poverty line are associated with livestock production. India is blessed with a rich genetic resource of bovines, with an estimated number of 304.76 million (Livestock Census, 2007), which is 19% of the world. India continues to be the highest milk producing country in the world with an estimated production of 121.8 million tonnes during 2010-11 of which more than 53% is contributed by buffaloes (105.34 millions). Similarly, India ranks first in buffalo, second in cattle and goat, third in sheep, fourth in duck, fifth in chicken and sixth in camel population of the world. India is the third largest egg producer in the world (BAHS 2012). India posses a large treasure of domestic animal biodiversity with 37 breeds of cattle, 13 breeds of buffalo, 23 breeds of goat, 39 breeds of sheep, 6 breeds of horse/pony, 8 breeds of camel, 2 breeds of pig and 15 breeds of chicken registered under National Bureau of Animal Genetic Resources, Karnal.

Introduction

The branch of science that deals with resolving the evolutionary relationships among organisms is phylogenetics. Phylogeny is the description of biological relationships, usually expressed as a tree. A statement of phylogeny among objects assumes homology and depends on classification. Phylogeny states a topology of the relationships based on classification according to similarity of one or more sets of characters, or on a model of evolutionary processes. In many cases, phylogenetic relationships based on different characters are consistent, and support one another. If different characters induce inconsistent phylogenetic relationships, they all are dubious. Conversely, note that the same similarity data may be consistent with different possible topologies or trees.
 
Molecular approaches to phylogeny developed against a background of traditional taxonomy, based on a variety of morphological characters, embryology, and from fossils, information about the geological context (stratigraphy).

For most of the past 50 years world food production has outpaced rising demand. World population has doubled since World War II, whereas food production has tripled. In the developing world the calories available per person per day increased from an average of 1,925 calories in 1961 to 2,540 in 1992. World food production has expanded due to the Green Revolution—adoption of crop rotation, the mass production and use of petroleum-based fertilizers and chemical pesticides, expanded irrigation, and the introduction of genetically superior, disease-resistant cultivars. The trend may now be changing for the worse, however. Since about 1990 global grain production has risen only slightly and, despite slower rates of population growth, grain supplies per capita have fallen. In the worst case, Africa now produces nearly 30% less food per person than it did in 1967. As per FAO estimates, world food production would have to double to provide food security for the 8 billion people projected for 2025. By 2050, when world population is projected to be over 9 billion, the situation would be even more challenging.

Introduction

Agriculture is the mainstay of livelihood activity of about 70% of the state’s population. Madhya Pradesh is an agrarian state and the state’s economy is highly dependent on agriculture. Considering the sector’s sensitivity to climatic parameters it can be easily understood that the sector would be highly impacted by any change in these parameters.

Madhya Pradesh has a subtropical climate with three distinct seasons’ viz. winter from December to February, followed by summer season from March to May and rainy season extending from June to October. During winters the mean temperature remains around 10ºC and mean maximum temperature remains 25ºC and the minimum temperature goes down to 1°C in some regions. During summers, the mean minimum temperature is 22ºC and mean maximum is 38ºC. The maximum temperature during summer can go up to 48ºC, especially in May and June which are the hottest months.

Introduction
Recently, there is a great need to analyze and exploit the huge amount of biological data information generated through high throughput modern technologies like phenotyping, allelic variation, gene expression, chromatography, DArT, molecular marker etc. Principal component analysis (PCA) has stronghold on modern data analysis - A black box that is widely used but poorly understood. The goal of this article is to dispel the magic behind this black box and to focus on building a solid intuition for how and why principal component analysis works in huge numbers of high throughput biological data set. It is a versatile and easy-to-use multivariate mathematical statistical method (Jolliffe et al, 2003) developed to extract maximal information from large data matrices containing numerous columns and rows. It also makes possible the elucidation of the relationship between the columns and rows of any data matrix without being one of the dependent variable. So the PCA is a projection method representing the original data in smaller dimensions.

Data mining is becoming an increasingly important tool to transform the data into information. In recent years, data mining has been widely used in area of Bioinformatics for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics. Data mining is used to understand gene expression analysis, searching and understanding of protein mass spectroscopy data, 3D structural and functional analysis and mining of DNA and protein sequences for structural and functional motifs. For the first several hundred years of research in biology, the main bottleneck to scientific progress was data collection. Our new found data-richness, however, has shifted this bottleneck from collection to analysis. Biomedical literatures have been increased at the exponential rate. To find the useful and needed information from such a huge data set is a daunting task for users. Text mining is a powerful tool to solve this problem and it is used for biological knowledge discovery.

Introduction

The recent revolution in genomics and bioinformatics has caught the world by storm. The issues surrounding the human genome, including the analysis of genetic variation, access to genetic information and the privacy of the individual have fuelled public debate and extended way beyond the scientific and technical literature. During the past few years, bioinformatics, defined as the computational handling and processing of genetic information, has become one of the most highly visible fields of modern science. A flood of data means that many of the challenges in biology are now challenges in computing. Bioinformatics, the application of computational techniques to analyse the information associated with bio-molecules 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. In this chapter we provide an introduction and overview of the current state of the field.

1    Dental formulae and eruption of tooth

2    Ageing based on rings of the horn

3    General appearance of animal

4    Longevity of different animals

1.     Length and Size
2.     Volume
3.     Weight

1    Bovines

2    Canines

1    Disease diagnosis based on posture, gait appearance and clinical signs

1    Common diseases affecting livestock

2    Disease information

3    Diseases of skin

4    Diseases of muscuoskeletal system in canines

Artificial Insemination is a very important tool that caters to propagation of high-quality germplasm and breed improvement. It is also economical as it aids in breeding without the necessary to maintain the adult bucks cutting the input cost. However, the bucks maintained for production of semen and AI should be free of any diseases and in specifically venereal diseases. Venereal diseases are not so uncommon in livestock, and they cause a variety of symptoms including orchitis, abortions, still birth, vulvovaginitis etc., leading to infertility in breeding animals. Venereal diseases are infectious in nature and they are transmitted mainly through semen, feed-water, AI gun, artificial vagina, fomites etc. Due to venereal diseases, infertility, orchitis, epididymitis, vasculitis etc. occurs in bucks and leads to abortions and still birth in does (Ahmed et al, 2010; Beena et al, 2017), because they are directly linked with the economic output (Sharma et al, 2008; Azawi et al, 2010) and overall health of the animal affecting the productivity significantly (Menzies, 2011). To keep abortions and other venereal diseases at bay, it is important to raise disease free breeding bucks that deliver clean-semen for natural service or AI operation. Abortion rate between 2 to 5% indicates its endemic nature and abortion level exceeding 5% requires aggressive investigation (Menzies, 2011). Infectious agents that include viruses, bacteria, fungi, parasites etc.; as well as noninfectious cause can lead to infertility in goats (Moeller, 2001; Szeredi et al, 2006; Smith and Sherman, 2009). Many infectious causes are known to mankind that leads to trans-venereal diseases in goats including Brucella melitensis, Chlamydiosis, Campylobacter spp., Listeria monocytogenes, Toxoplasmosis, Coxiella burnetii etc. There are numerous viruses such as