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Computational Biology – A Gentle Introduction

The modern world is witnessing various multidisciplinary fields of biology. One of them is computational biology. With the revolution of life sciences over the years, computational biologists have now become leaders rather than mere service providers.

What is Computational Biology?

Computational biology is different from biological computing, which is a subfield of engineering science & computer science. It is actually similar to bioinformatics. However, there is a thin line of difference between the two. Computational biology refers to the field where computational approaches are used to address biological theoretical & experimental questions. Whereas, bioinformatics refers to understanding the complex biological data present.

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Computational biology is an interdisciplinary field involving applications of various foundations, such as biology, mathematics, physics & computer science. It uses computers for storage & processing of biological data. Various subfields of computational biology include computational anatomy & biomodelling and cancer computational biology.

Development of the Field

With the advent of brilliant researches by scientists, came in huge amounts of data. One such example is the ‘Genome Project’ with such a vast amount of DNA data collected. This huge volume of data is referred to as ‘Big Data’. And the analysis of big data requires computational biology.

The emergence of ‘big data’ was the reason for the development of data science and artificial intelligence. In the early 1950s, Alan Turing implemented a model of biological morphogenesis using early computers. Shortly afterward, computers started to be used in protein crystallography so as to determine protein structure.

Research Group Söding | Max Planck Institute for Biophysical Chemistry

This field has been playing a vital role in biology since the late 1990s. Computer biologists need to look at the biological system from a new perspective. The most important of which is to frame biomedical problems in the form of computational problems. And then begins the next major task, to solve the problem. Due to the changes over time, there was an invention of analytical algorithms for the facilitation of scientific breakthroughs.

Applications of Computational Biology

Computational biology involves the use of various algorithms, such as sequence matching and evolutionary trees. In today’s world, many software is in use to develop computational biology methods. Some of the reasons for using open source software for this purpose are reproducibility and increased quality along with faster development.

 

Australian Bioinformatics And Computational Biology Society

 

Role in Research

This field helps to determine the three-dimensional structure of proteins. It also describes biological tasks of sequences and gene expression changes leading to a particular disease. Now computational biology has become aligned with systems biology due to its involvement in functional prediction.

Other functions of this field include studies of molecular evolution and disease gene mapping. With emerging importance of computational biology for modern-day biologists, many universities, such as MIT, are providing courses for the same. These courses can help biologists getting into pharmaceutical companies.

For research, there is a need for researchers to collaborate with clinicians and experimentalists all over the biomedical world. For the unlocking of secrets hidden in biomedical data, the computational biologists should innovate both computational & biological fields.

 

Data Generation

In the past, the researchers would focus on the data which was public or would collect data from collaborators. As a result, the researcher becomes restricted in collecting data according to the hypothesis. Today collecting primary data is more preferable than secondary data for research purposes. To analyze the data, the researcher needs to have a thorough understanding of both algorithm innovation and biological questions.

With the development and advancement of data science, comes into picture ‘data science ethics’. Data science ethics refers to ethics ranging from data ownership to the ethical use of data. The owner of the data is the one who generated the data. Ethical use refers to use in a manner in which it does not harm anyone.

What is Data Science? – Dataquest

 

As biology has undergone an evolution as well as an advancement over the years, there is an exponential increase in the volume of data generation & processing. Due to this, computational biology has been playing a vital role in modern scientific research.

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