Bi Assgt

Genomics is the study of an organism's entire genome. Investigation of single genes, their functions and roles is something very common in today's medical and biological research, and can not be said to be genomics but rather the most typical feature of molecular biology. Genomics can be said to have appeared in the 1980s, and took off in the 1990s with the initiation of genome projects for several species. A major branch of genomics is still concerned with sequencing the genomes of various organisms, although the knowledge of full genomes have created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics.

Proteomics is the large-scale study of protein, particularly their structures and functions. It is also the study of the full set of proteins in a cell type or tissue, and the changes during various conditions. This term was coined to make an analogy with genomics, and while it is often viewed as the "next step", proteomics is much more complicated than genomics. Most importantly, while the genome is a rather constant entity, the proteome differs from cell to cell and is constantly changing through its biochemical interactions with the genome and the environment. One organism has radically different protein expression in different parts of its body, in different stages of its life cycle and in different environmental conditions. The word is derived from PROTEins and by genOME, since proteins are expressed by the genome. The proteome refers to all the proteins produced by an organism, much like the genome is the entire set of genes. The human body may contain more than 2 million different proteins, each having different functions. Thus, proteomics is the study of the composition, structure, function, and interaction of the proteins directing the activities of each living cell. As the main components of the physiological pathways of the cells, proteins serve as vital functions in the body.

Comparative genomics is the study of relationships between the genomes of different species or strains. Comparative genomics is an attempt to take advantage of the information provided by the signatures of selection to understand the function and evolutionary processes that act on genomes. While it is still a young field, it holds great promise to yield insights into many aspects of the evolution of modern species. The sheer amount of information contained in modern genomes (several gigabytes in the case of humans) necessitates that the methods of comparative genomics are mostly

computational in nature. Gene finding is an important application of comparative genomics, as is discovery of new, non-coding functional elements of the genome. Comparative genomics exploits both similarities and differences in the proteins, RNA, and regulatory regions of different organisms to infer how selection has acted upon these elements. Those elements that are responsible for similarities between different species should be conserved through time (stabilizing selection), while those elements responsible for differences among species should be divergent (positive selection). Finally, those elements that are unimportant to the evolutionary success of the organism will be unconserved (selection is neutral). Identifying the mechanisms of eukaryotic genome evolution by comparative genomics is one of the important goals of the field. It is however often complicated by the multiplicity of events that have taken place throughout the history of individual lineages, leaving only distorted and superimposed traces in the genome of each living organism. For this reason comparative genomics studies of small model organisms (for example yeast) are of great importance to advance our understanding of general mechanisms of evolution. Having come a long way from its initial use of finding functional proteins, comparative genomics is now concentrating on finding regulatory regions and siRNA molecules. Recently, it has been discovered that distantly related species often share long conserved stretches of DNA that do not appear to code for any protein. It is unknown at this time what function such ultra-conserved regions serve.

The goal of comparative proteomics is to systematically compare global protein expression profiles, focusing on quantitative changes that occur as a function of disease, treatment or environment (Somiari et al., 2003).Such an approach may provide comprehensive insight into the dynamics of the proteome and reveal protein markers of disease. The increasing volume of data from high-throughput proteomics urgently needs to be made accessible in databases with standards for data storage, exchange and analysis. Comparative proteomics may be helpful to shorten the transfer between model and agronomic target species. Comparative proteomics has been used with the long-term goal to locate, detect, and characterize the differentially expressed proteins (DEPs) in human pituitary adenomas; to identify tumor-related and -specific biomarkers; and to clarify the basic molecular mechanisms of pituitary adenoma formation.

Drug design is the approach of finding drugs by design, based on their biological targets. Typically a drug target is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial pathogen.

Some approaches attempt to stop the functioning of the pathway in the diseased state by causing a key molecule to stop functioning. Drugs may be designed that bind to the active region and inhibit this key molecule. However these drugs would also have to be designed in such a way as not to affect any other important molecules that may be similar in appearance to the key molecules. Sequence homologies are often used to identify such risks. Other approaches may be to enhance the normal pathway by promoting specific molecules in the normal pathways that may have been affected in the diseased state. The structure of the drug molecule that can specifically interact with the biomolecules can be modeled using computational tools. These tools can allow a drug molecule to be constructed within the biomolecule using knowledge of its structure and the nature of its active site. Construction of the drug molecule can be made inside out or outside in depending on whether the core or the R-groups are chosen first. However many of these approaches are plagued by the practical problems of chemical synthesis. Newer approaches have also suggested the use of drug molecules that are large and proteinaceous in nature rather than as small molecules. There have also been suggestions to make these using mRNA. Gene silencing may also have therapeutical applications.

There are over 1,000 public and commercial biological databases. These biological databases usually contain genomics and proteomics data, but databases are also used in taxonomy. The data are nucleotide sequences of genes or amino acid sequences of proteins. Biological databases have become an important tool in assisting scientists to understand and explain a host of biological phenomena from the structure of biomolecules and their interaction, to the whole metabolism of organisms and to understanding the evolution of species. This knowledge helps facilitate the fight against diseases, assists in the development of medications and in discovering basic relationships amongst species in the history of life. The biological knowledge of databases is usually (locally) distributed amongst many different specialized databases. This makes it difficult to ensure the consistency of information, which sometimes leads to low data quality. By far the most important resource for biological databases is a special (yearly) issue of the journal "Nucleic Acids Research" (NAR). The Database Issue is freely available, and categorizes all the publicly available online databases related to computational biology (or bioinformatics).

The term "sequence analysis" in biology implies subjecting a DNA or peptide sequence to sequence alignment, sequence databases, repeated sequence searches, or other bioinformatics methods on a computer. Sequence analysis in molecular biology and bioinformatics is an automated, computerbased examination of characteristical fragments, e.g. of a DNA-strand. It basically includes five biologically relevant topics: 1. the comparison of sequences in order to find similar sequences (sequence alignment) 2. identification of gene-structures, reading frames, distributions of introns and exons and regulatory elements 3. prediction of protein structures 4. genome mapping 5. comparison of homologous sequences to construct a molecular phylogeny In chemistry, sequence analysis comprises techniques used to do determine the sequence of a polymer formed of several monomers. In molecular biology and genetics, the same process is called simply "sequencing."

In biology, phylogenetics (Greek: phylon = tribe, race and genetikos = relative to birth, from genesis = birth) is the study of evolutionary relatedness among various groups of organisms (e.g., species, populations). Also known as phylogenetic systematics, phylogenetics treats a species as a group of lineage-connected individuals over time. Phylogenetic taxonomy, which is an offshoot of, but not a logical consequence of, phylogenetic systematics, constitutes a means of classifying groups of organisms according to degree of evolutionary relatedness. Phylogeny (or phylogenesis) is the origin and evolution of a set of organisms, usually a set of species. A major task of systematics is to determine the ancestral relationships among known species (both living and extinct). The most commonly used methods to infer phylogenies include parsimony, maximum likelihood, and MCMC-based Bayesian inference. Distance-based methods construct trees based on overall similarity which is often assumed to approximate phylogenetic relationships. All methods depend upon an implicit or explicit mathematical model describing the evolution of characters observed in the species included, and are usually used for molecular phylogeny where the characters are aligned nucleotide or amino acid sequences.

Molecular dynamics (MD) is a form of computer simulation where atoms and molecules are allowed to interact for a period of time under known laws of physics. Because in general molecular systems consist of a large number of particles, it is impossible to find the properties of such complex systems analytically. MD simulation circumvents this problem by using numerical methods. It represents an interface between laboratory experiments and theory and can be understood as a virtual experiment. Even though we know matter consists of interacting particles in motion at least since Boltzmann in the 19th Century, many still think of molecules as rigid museum models. Richard Feynman said in 1963 that "everything that living things do can be understood in terms of the jiggling and wiggling of atoms." [1] One of MD's key contributions is creating awareness that molecules like proteins and DNA are machines in motion. [2] MD probes the relationship between molecular structure, movement and function. Molecular dynamics is a multidisciplinary field. Its laws and theories stem from mathematics, physics and chemistry. MD employs algorithms from computer science and information theory. It was originally conceived within theoretical physics in the 1950's, but is applied today mostly in materials science and biomolecules.

Subdiscipline of bioinformatics that focuses on the representation, storage, retrieval, analysis and display of structural information at the atomic and subcellular spatial scales. Structural Biological Analysis can provide the ultimate insight into the mechanism behind a biological function, understand how biological function follows structure. • Goals – Creation of methods for manipulating structural data – Application of these methods to solving problems in biology and discovery of new patterns.