Algorithms of Computational Biology essay

The field of computational biology is a new field of biological manipulations which involves the use of computers and computer science for understanding and modelling of the structures and processes of life. It is an emerging field that allows for the use of computational methods for the representation and simulation of biological systems, as well as for the interpretation of experimental data on a large scale. This is one very important area of science that is vital in the discovery of more functions of biological systems. In essence, the beginnings of computational biology essentially date to the origins of computer science. Foremost British mathematician and logician Alan Turing, who Is often regarded as the father of modern computing; used early computer applications to implement a model of biological morphogenesis. At the same time, a computer called MANIAC, built at the foremost Los Alamos National Laboratory in New Mexico for weapons research, which was applied for such purposes as modelling hypothesized genetic codes.

As times rolled by, computers have been applied to deal with much more varied sets of analyses, namely those examining protein structure. These developments marked the rise of computational as a field. These studies originated from earlier studies centered on protein crystallography; where foremost scientists found computers as very indispensable tools for carrying out laborious Fourier analyses to determine the three-dimensional structure of proteins. From the 50s, taxonomists worked towards the incorporation of computers into their work. The use of machines to assist in the classification of organisms by clustering them based on similarities of sets of traits. These taxonomies have been useful particularly for phylogenetics which involves the study of evolutionary relationships. Also, in the 60s, when existing techniques were extended to the level of DNA sequences and amino acid sequences of proteins, combined with a burgeoning knowledge of cellular processes and protein structures, a whole new set of computational methods was developed in the support of molecular phylogenetics.

There are also some computational methods entailed the creation of increasingly sophisticated techniques for the comparison of strings of symbols that benefited from the formal study of algorithms and the study of dynamic programming in particular. Efficient algorithms always have been of primary concern in computational biology, and given the scale of data available and biology has also in turn provided examples that have driven much-advanced research in the field of computer sciences. Some examples also include graph algorithms for genome mapping and certain types of DNA and peptide sequencing methods, clustering algorithms for gene expression analysis and phylogenetic reconstruction, and pattern matching for various sequence research problems. From the 80s, computational biology drew on further developments in computer science which includes a number of aspects of artificial intelligence. Amongst all these developments were that of ontologies. This involves the representation of concepts and their relationship. This codifies the biological knowledge in computer-readable form and natural language processing, which provides a technological means for the mining of information from the text in the scientific literature. This subfield of science has also found uses in the aspect of biology; from the modelling of sequences for purposes of pattern recognition to the analysis of high dimensional data from large scale gene expression studies.

In the applications of computational biology, initially, it was focused on the study of the sequence and structure of biological molecules, which is often done in an evolutionary context. Over time, it has increased to the analysis of function. With functional prediction involving the assessment of sequence and structural similarity between an unknown and a known protein; also, the interaction of these protein molecules with other molecules. Such analysis may be extensive, hence computational biology has become closely related to systems biology. In the latter, we can see attempts to analyze the workings of large interacting networks of biological components, especially biological pathways. It is worthy of note that biochemical, regulatory, and genetic pathways are highly intertwined as well as they are dynamic. This, of course, calls for efficient computational tools for their modelling and analysis.

Nowadays, there are many modern and up to date technological platforms for the rapid, automated generation of biological data that have allowed for the extension from traditional hypothesis-driven experimentation to data-driven analysis, by which computational experiments can be performed on genome-wide based databases of unprecedented scale.

In distinguishing between the field of computational biology, which also involves the use of algorithms to improve its effectiveness; it is best to know efforts to distinguish it have always been at the forefront of discussions. It is important to distinguish between the field of computational biology and related fields such as bioinformatics, etc. and to a lesser extent from fields such as mathematical and theoretical biology. Bioinformatics and computational biology are often used interchangeably, even by perceived experts in the field. Although both fields are fundamentally computational approaches to biology, bioinformatics solely deals with data management and analysis using specific tools that are aids in biological representations and the interpretation of laboratory results. In plain words, computational biology is that which is a branch of biology aided by computation. It harps on using viable computer techniques in the formulation and solving of challenging problems, to the representation and examination of domain knowledge and ultimately to the generation and testing of scientific hypotheses. Computational biology is specifically associated with practical application, and journals and annual meetings in the field often encourage the presentation of biological analysis using real data along with theory.

In conclusion, it is worth noting that the contributions of computational biology have risen due to the aspects of theoretical biology derived from information theory, network theory, and non-linear dynamical systems, for example, have improved the advances in the mathematical study of complex networks which have increased the scientific world’s understanding of naturally occurring interactions among genes and gene products; while also providing insight into how characteristic network architectures may have arisen in the course of evolution and its robustness in the light of mutations; while making use of realistic algorithms to make improved and reasonable deductions.