Application of computer and statistics in biology
There is a vast and broad application of both computer technology and statistics in biology. Few of the important fields of biology are as mentioned below, briefly explaining the application of both computer technology and statistics.
Genomics is an attempt to analyze or compare the complete genetic compliment of an organism. Modern high-density experimental studies on various genomes produce huge amounts of data. Interpretation of this data into a biologically meaningful knowledge is nowadays a major challenge. This challenge is answerable only by the implementation of computer technology and statistics. It requires a development of robust analytical methods which are applicable and useful. Various statistics and algorithms are also used for the comparison of sequenced genome. Principles of Genomics synthesize the state-of-the-art statistical methodologies applied to genome study.
Comparative Genomics is a collection of robust protocols formolecular biologists beginning to use comparative genomic analysis tools in a variety of areas. It involves Comparison of human genetics with model organisms such as mice, fruit fly, E. coli. Computational approaches to genome comparison have recently become a common research topic in computer science. A public collection of case studies and demonstrations is growing, ranging from whole genome comparisons to gene expression analysis. This has increased the introduction of different ideas, including concepts from systems and control, information theory, strings analysis and data mining. It is anticipated that computational approaches will become and remain a standard topic for research and teaching, while multiple courses will begin training students to be fluent in both topics.
The term "proteome" refers to the entire complement of proteins, including the modifications made to a particular set of proteins, produced by an organism or a cellular system. This will vary with time and distinct requirements, such as stresses, that a cell or organism undergoes. The term"proteomics" is a large-scale comprehensive study of a specific proteome, including information on protein abundances, their variations and modifications, along with their interacting partners and networks, in order to understand cellular processes. “Clinical proteomics” is a sub-discipline of proteomics that involves the application of proteomic technologies on clinical specimens such as blood.
It refers to the analysis of macromolecular structure particularly proteins; the main goal of structural genomics is the extension of idea of genomics, to obtain accurate three dimensional structural models for known proteins. Structural genomics proceeds through increasing levels of analytic resolution, starting with the assignment of genes and markers to individual chromosomes, then the mapping of these genes and markers within a chromosome, and finally the preparation of a physical map culminating in sequencing. Structural genomics takes advantage of completed genome sequences in several ways in order to determine protein structures. The gene sequence of the target protein can also be compared to a known sequence and structural information can then be inferred from the known protein’s structure. Structural genomics can be used to predict novel protein folds based on other structural data. Structural genomics can also take modeling-based approach that relies on homology between the unknown protein and a solved protein structure.
Drug design is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of small molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modelling techniques. This type of modelling is often referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design.
Functional genomics refers to the development and application of global experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics. It is characterized by high-throughput or large-scale experimental methodologies combined with statistical or computational analysis of the results (Hieter and Boguski 1997).
It is an application of genomic approaches and technologies to the identification of drug targets. In other words knowing whether a patient carries any of the genetic variations which can help to prescribe and individualize drug therapy, decrease the chance for adverse drug events, and increase the effectiveness of drugs. Pharmacogenomics combines traditional pharmaceutical sciences such as biochemistry with an understanding of common DNA variations in the human genome.