Computational Biology

Advances in biology and chemistry combined with improved understanding of diseases and its management, have led to multiple measurement technologies resulting in vast amounts of data. It is increasingly difficult to infer a conclusion and take a decision when looking at vast amounts of complex data ^[1]. The inherent complexity of biological systems, the error-prone and incomplete nature of data collection mechanisms, and the large size of biological datasets motivate novel algorithms with significant memory and computational requirements ^[2]. These challenges have led to the development of solutions using techniques from multidisciplinary fields including engineering, computer science, applied mathematics, and statistics, leading to the identification of a field known as Computational Biology. It has been predicted that biology is set to become a highly quantitative science. In the 21st century, biology will become the most computer-intensive science ^[1].

Typically computational biology refers to algorithms and statistical techniques used to solve problems in genomics, a field involved with understanding the genetic make up of cells and organisms. The types of problems that are being studied include, indentifying the sequence of molecules in a gene of protein to prediction of what genes are triggered under certain biological conditions. Additionally, aspects of computational biology seek to develop computer models that apply physical, chemical, and biological principles that mirror the behavior of biologic molecules and processes. By performing virtual experiments and analyses “in silico,” computational biology offers the promise of testing biological hypotheses through modeling and simulation improving the efficiency of scientific discovery ^{[3, 4]}. Systems biology, Image processing, Biosignal processing, modeling and simulation, computational genomics, computational biochemistry, and machine learning are just few of the fields that are used to solve challenges faced in biology that fall under Computational Biology. For example, new devices have been developed to measure physical and chemical parameters of molecular systems with ever increasing throughput and sensitivity, and numerical techniques have been devised, which allow the analysis of large data sets in the frameworks of computational system models ^[5].