About Computational Biology

Computational biology is a subject that deals with the basic fundamental of algorithmic and machine learning functions of computational biology combining with theory practice. This course is designed for the students of biology, statics, premed and agronomy departments. Other than these students, computer science, physic, maths and chemistry majors find this course beneficial. Computational biology is designed welfare of computational biologists, experimental biologists, and biostatisticians to understand the fundamental concepts of analyzing biological datasets, building models, and testing procedures using computer science paradigms. This course does not depends on any graduated courses. Through a combination of foundational examples and current researches, this course aims to demystify computer science, molecular biology, and some of the ways they intersect.

The study of this course will provide us with knowledge about the properties of DNA, RNA, proteins, and the relationships among the molecules. This engineering study will help us convert a biological question into a computational problem that can be solved using computers. This course's study will help us read and understand solutions to computational problems, which will be formalized as a series of tasks (an algorithm) that will teach us about. The studies for solving computational problems and implementing the algorithms by writing computer programs in Python, which can be run and easily understood by others.

What is Computational Biology?

This field of computational biology taught us how to analyze and identify protein binding patterns, compare sequences, and discover a variation within genomes. It will formulate our sequence to analyze the problem, implement a solution. The study of this course will provide us with knowledge about the properties of DNA, RNA, and proteins, the relationships among molecules. This engineering study will helps help convert a biological question into a computational problem that can be solved using computers. Study The study is course will help us to read and understand solutions to computational problems, which will be formalized as a series of tasks (an algorithm) that will teach us about general approaches for solving computational problems and also the implementation of the algorithms by writing computer programs in Python, which can be run and easily understood by others. Genomes, networks and their evolution are the important division having attractive topics like Biological sequence analysis, hidden Markov models, gene finding, comparative genomics, RNA structure, sequence alignment, hashing,  Gene expression, clustering/classification, EM / Gibbs sampling, motifs, Bayesian networks, microRNAs, regulatory genomics, epigenomics, Gene/species trees, phylogenetic, coalescent, personal genomics, population genomics, human ancestry, recent selection, disease mapping were covered in this field. Through this study, we can easily learn about principles and gene expression genomics and proteomics that aid precision medicine in modern plant and animal breeding technology.

Eligibility and Career in Computational Biology

Aspiring students should have a certificate of 10+2  from a recognized board in a relevant area of physics, chemistry and biology with the aggregated marks of a minimum of 60 % are needed to enter into the bachelor degree.  And then, the undergraduate program is a 4-year long term process with 8 semesters. This is needed for higher studies in. this following field. Basic knowledge about computer languages like C, C++, and Python is also required, giving better scope in this field. Computational biology is a growing field in today’s world. It grows not only in academia but also in industries. Major players of this digital world like Google, Microsoft, life technologies, L.Martin, Roche and Merck were invested heavily in computational biology. Engineers in this field can work in academic, commercial domains and both government and private sectors. Also, Postings like data scientists or data analytics, research scientists or data scientists are usually associated with one having this educational background. From this, it's clear that computational biology grows at the academics level and industrially. This field has growing rates at impressive levels.