eChapters

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.

2.1. INTRODUCTION

Pollination is the process of mating in plants, that is, the transfer of pollen from anther to stigma. In flowers, pollen delivered to the stigma through a wide range of mechanisms that ensure an appropriate balance in the genetic makeup of the species. Most plant species have evolved ways to ensure an appropriate degree of interchange of genetic material between individuals in the population, and cross-pollination is the normal type of pollination. Plants benefit most by being pollinated by other individuals because this broadens the genetic characteristics of individual plants. As a result, they are more adaptable to necessary changes. Self- and often-pollinated crops evolved from cross pollinated crops.

Pollination followed by fertilization is important in agriculture, particularly for the crops whose economic part is either fruit or seed, such as, rice, wheat, fruit vegetables etc. For other crops, these events are also important for production of seed for cultivation of next generation, for examples, green vegetables (amaranth, palak, cabbage, cauliflower etc).

The study of pollination brings together many disciplines, such as botany, plant breeding, horticulture, entomology, and ecology. The pollination process as an interaction between flower and vector was first addressed in the 18th century by Christian Konrad Sprengel. It is important in horticulture and agriculture, because fruiting is dependent on fertilization, which is the end result of pollination.

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].

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.

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.

If the 1990s were the decade of genomics, the first ten years of the new century are set to become the decade of proteomics. For the first time, the technologies of proteomics make it possible to generate quantitative protein expression data on a scale and sensitivity comparable to that achieved at the genetic level. This advance has major implications for our understanding of cellular organisation in health and disease and for pharmaceutical and agricultural biotechnology. Indeed, proteomics is already yielding important findings, across a wide range of applications. Most proteomics research, however, is directed towards the more proximal goal of investigating protein expression and function under specified physiological conditions.

The rapid evolution of proteomics has continued during the past years with a series of innovations in the core technologies of two-dimensional electrophoresis and mass spectrometry, and a diversity of productive research programmes. Well annotated proteomics databases are now emerging in a number of fields to provide a platform for systematic research, with particularly promising progress in clinical applications such as cardiology and oncology. Large-scale quantitative research, comparable in power and sensitivity to that achieved for gene expression, is thus becoming a reality at the protein level.

4.1. INTRODUCTION

Rice (Oryza sativa) is the major food crop in the world. Nearly 40% of the world population consumes rice as the major staple food. Most of the people, who depend on rice as primary food, live in the less developed countries.  Archeological evidence on rice in India dates back to 1500-1000 B.C. Since the dawn of civilization, rice has served humans as a life-giving cereal in the humid regions of Asia and, to a lesser extent, in West Africa. Introduction of rice into Europe and the America has led to its increased use in human diets. There are 42 rice producing countries throughout the world but China and India are major rice production centers. Rice is grown in wide range of agro-climatic conditions ranging from mountainous (Jammu) lands to low land delta areas (Sundarban), spanning an area from 53° latitude north to 35° south of the world but about 90% of the crop is grown and consumed in Asia. Rice provides fully 60% of the food intake in Southeast Asia and about 35% in East Asia and South Asia. The highest level of per capita rice consumption (130-180 kg per year, 55-80% of total caloric source) takes place in Bangladesh, Cambodia, Indonesia, Laos, Myanmar (Burma), Thailand, and Vietnam (Kenneth and Kriemhild, 2000). In many cultures of the world rice is the central part of people’s life and culture. Rice is an excellent food and is an excellent source of carbohydrates and energy.

The importance of Plant Pathology has been realized over the years owing to number of crop failures in different parts of the world. Plant disease control is indispensable for qualitative production and to compete in global agricultural trade. There is great need of sensitization of our research in various aspects which shall guide the generation to come for managing plant diseases not only for enhancing crop production but also in facilitating trade by conserving the biodiversity and environment.

Research Trend in Plant Pathology

Like in other sciences, there has been a change in the trend of research in Plant Pathology with passage of time. Up to the late 1960s most of the interest in plant pathology was concentrated in crop diseases mostly caused by fungi. Physiology of microorganisms and physiology of parasitism became increasingly popular fields of interest in 1960s.

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.

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.

16.1. INTRODUCTION

Millets are a large part of the basic diet for farm households in the world's poorest people in the poorest countries. The production of millets on average for the 5 years have been given in Table 16.1. Top consuming countries are India, Nigeria, Niger and China. However, just because a country is a top global consumer does not mean that millet is a significant source of calories for them. India and China are rank as the 1st and 4th consumers of millet, but due to their population size they rank 11th and 38th in per-capita consumption, accordingly.

Millet particularly the finger millet) is an excellent dietary source of calcium, iron, manganese, and methionine-  an amino acid lacking in the diets of hundred of millions of the poor who live on starchy foods such as cassava, plantain, polished rice, and maize meal. Millet use is diverse, including in cereals (including porridge and kasha), soups, breads and stuffings, fermented beverages, and baby food.

Soil is formed by disintegration and decomposition of rocks, minerals and organic matter, Soil is a habitant for plant growth and serves as a storehouse of water and food nutrients for plants, Soil is not same everywhere and varies morphologically, physically, chemically and mineralogically from one point to another both in horizontal and vertical directions. Development of soil informatics is necessary to classify the soils, to assess the productivity of soils for various crop productions and to use the soil for various purposes such as land capability classification, soil irrigability classification, soil rating and soil mapping.

Morphologically soil may appear red, yellow, black, brown or ashy in colour. Soil may vary from sandy to clayey in texture. Depending upon the nutrient reserve capacity, the soil may be fertile or infertile. On the basis of climate, a soil may be classified as laterite soil. Red earth and red loam soil, podzol soil or desert soil.

Different kinds of soil contain minerals of various types and amounts and so exhibit distinct physio co-chemical properties. Although various methods have been used in the past to classify the soils, the most modern and scientific methods is based on dominants pedogenic processes acting on a place to from a soil. The new method of soil classification has two important characteristics-Firstly soil classification is based on certain diagnostic parameters which could be estimated quantatively, and secondly depending on diagnostic parameters, a scientific method is being followed to classify a soil from higher to lower levels such as order, suborder, great group, subgroup, family and series.

11.1. INTRODUCTION

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop and is the dietary staple of more than 500 million people in over 90 countries, primarily in the developing world. It is grown on 47 m ha in 104 countries in Africa, Asia, Oceania, and the Americas. Among those, USA, Nigeria, India, Mexico, Sudan, China, and Argentina are the major sorghum producers globally. Sorghum grain is mostly used directly for food (55%), and is consumed in the form of porridges (thick or thin) and flat breads; however sorghum is also an important feed grain (33%), especially in Australia and the Americas. Stover (crop residue after grain harvest) is an important fodder source to both milch and draft animals in mixed crop-livestock systems. Sorghum is also a good source of green fodder due to its quick growth and high yield and quality of biomass. Of late, sorghum with sugar-rich juicy stalks (called sweet sorghum) is emerging as an important biofuel crop. Thus sorghum is a unique crop with multiple uses as food, feed, fodder, fuel and fiber. Sorghum is generally grown in rainy season (spring) but in India it is grown in both rainy and postrainy seasons. The postrainy season sorghum grain is prized for its food value (Reddy et al., 2009).

12.1. INTRODUCTION

Sorghum [Sorghum bicolor (L.) Moench] is an important food/feed/fodder crop in more than 90 countries across the world and it is one of the cheapest sources of energy and micronutrients (Reddy et al., 2009; Ashok Kumar et al., 2011). Considerable efforts have been made to improve sorghum productivity through genetic enhancement and crop production and protection research all over the globe. Sorghum being an often cross-pollinated crop with cross pollination up to 2-15% depending on genotype and environment (House 1985), initially, pure-lines were the target products although phenomenon of heterosis was demonstrated in sorghum as early as in 1927 (Conner and Karper, 1927). Predominant self-pollinating nature of sorghum with tiny florets with single seed per emasculation and pollination prevented commercial exploitation of heterosis for commercial sorghum production. However, the discovery of a stable and heritable cytoplasmic-nuclear male-sterility (CMS) mechanism (Stephens and Holland, 1954), enabling large-scale, economic hybrid seed production gave a fillip to the exploitation of heterosis since early 1960s. The first commercial sorghum hybrid CSH 1 was released in 1964 in India. Using CMS mechanism, large number of commercial hybrids have been developed and released/marketed for commercial cultivation in all sorghum growing regions with strong Indian National Agricultural Research Systems (NARS).

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.

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.

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.

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.

Advancement of both biological sciences and computer technology gave birth today’s one of the hottest area known as bioinformatics. Gradually bioinformatics analysis has become an essential component to the continued improvement and development of any kind of biological research work. It has various applications in medicine, biotechnology, agriculture etc. Some of the applications of bioinformatics related to biological information analysis are as follows [1]
    
·    Information related to bio-molecules can be mapped (e.g.), the sequences can be parsed to find sites where so-called “restriction enzymes” will cut them.
    
·    Sequences can be compared, usually by aligning corresponding segments and looking for matching and mismatching letters in their sequences. Genes or proteins that are sufficiently similar are likely to be related and are therefore said to be “homologous” to each other.
    
·    If a homologous exists then a newly discovered protein may be modeled - that is 3D structure of the gene product can be predicted without doing laboratory experiments.
    
·    Bioinformatics is used to attempt to predict the function of actual gene products.

The advances in science and technology are deeply intertwined. Researchers can use the semiconductor manufacturing techniques that underlie miniaturization to build radios and exceptionally small mechanical structures that sense fields and forces in the physical world. These inexpensive, low-power communication devices can be deployed throughout a physical space, providing dense sensing close to physical phenomena, processing and communicating this information, and coordinating actions with other nodes. These multifunction devices are battery operated and are connected wirelessly with sensing, processing and communicating capabilities and are known as the wireless sensors. If we use these sensors with biological issues or Biotechnology, then we can find the new concepts biosensors. If we really implement these concepts in real life we can get rid of many diseases like heart attack, blood sugar level, gene reaction and other changes that happens in our body much before its shows to outside.