Computational Biology — An Overview
Computational Biology is the study of biology using computational techniques. It is an interdisciplinary field with computer science, applied maths and biology at its center. It has foundations in biochemistry, biophysics, statistics, biophysics, molecular biology, genetics, genomics, ecology, evolution, anatomy, neuroscience, and visualization.
This field came into limelight with the Human Genome Project. The world’s largest collaborative biological project was launched in 1990 and officially completed in 2003. The high throughput experimental techniques developed during this project lead to generation of huge amounts of data and new fields like mathematical biology/biomathematics, bioinformatics/computational biology came into prominence.
One of the commercial outcomes of this project was setting up of BROAD, a collaborative institute of MIT and Harvard, which aims to understand and improve human health through genomics. This is just one success story of application of computational biology, there are many upcoming startups in pharma industry, healthcare and agriculture who apply computation techniques to accelerate their growth.
Pharmaceutical industry has been known for time consuming, extremely costly and high risk drug discovery process. And even after all this, the chances of success are very less. Computational pharmacology is a field which employs computational techniques in understanding of drug action, drug side effects, identification of drug targets and drug design.
Companies are doing mathematical modeling and data analysis to identify mechanisms, drug actions and side effects at an early stage in the drug discovery process in the hope of increasing chances of success. Software startups are out there for identifying drug binding sites and design.
Experimental techniques like CRISPR are already gaining attention and biotech companies are developing computational techniques to employ these in real world applications.
Computational Neuroscience is another developing field where technologies are used to understand brain and behaviour. Computational algorithms, informatics tools, imaging softwares play have a huge application in this area.
Data storage and security are also upcoming fields because of all this data generation. Traditionally biologists have been storing information in an unstructured way for personal usage. Now data generation is increasing at an enormous rate for different kinds of data, genomic, proteomic, metabolomic, environmental etc. Intelligent analysis softwares, structural databases are becoming requirements for efficient use of this data. Medical data contains delicate personal information about the patients, data from clinical trials and research stages of drug discovery process is highly sensitive and confidential. Therefore data security is another upcoming field in this area.
Agriculture is seeing growth in plant genomics, precision farming, food tracking and effective supply chains because of big data. Lots of startups are coming up and competing with traditional big agricultural players because of the technology and information being made available in this field. It is seen as a positive trend for all the stakeholders- farmers, environment, industry and consumers.
Historically, doctors have been treating patients with their experience and knowledge. Now with big data it will be possible to tap into the treatment records of millions of patients to generate data driven hypothesis. Images from hospitals are being analysed and processed to understand different stages of diseases and find ways of early detection and diagnosis. With this new approach, not only the physician but also the network of researchers and scientist can come into play, treating the patients in a an entirely novel way.
These are some of the fields where computational biology is creating buzz in the market. From these trends, it can be seen that biology — which has been away from mathematics and computer science for so long — has lots of new potential and is being tapped in areas which were not possible to reach earlier.