Computational Biology entails the application and development of data-analytical and theoretical methods, computational simulation techniques, and mathematical modeling to investigate biological, social and behavioral systems. It is one of the rapidly growing areas of biomedical research, but many students or researches might not find time to formally study and gain expertise in it.
Geoffrey Siwo, who is currently a research assistant professor at the prestigious University of Notre Dame, shares a few tips on how a biologist can keep up with computational biology.
The first and foremost advice is to – “Learn by solving a problem that matters to you”
Computational Biology includes a wide area of knowledge from data mining, statistics, nucleic acid and protein sequence analysis, machine learning and so on. If you are a full time biologist and short on time, one practical way is to start with a biological question which interests you the most and then starts thinking about how to solve it using computers instead of experiments. If this proves difficult, try to break down your problem into smaller manageable units.
An individual first has to learn how to use tools like Basic Local Alignment Search Tool (BLAST) which will enable him to find similar nucleic acid or amino acid sequences of interest, CLUSTALW to perform multiple sequence alignment and analyze the similarities among them and databases like PubMed to read peer-reviewed publications. Together, these tools will allow him to learn more about computational biology and bioinformatics.
“Share your ideas, interact with crowds, and don’t be afraid of being wrong! When you learn a new skill on your own, there’s a good chance you’ll make a mistake. But discussing your work with other biologists can help you avoid that”, says Siwo.
Many online resources provide a platform wherein users are allowed to ask questions about their subject of computational interest. For example, Biostars, Seqanswers and Bioconductor are forums where beginners and experienced professional can ask and answer questions.
Alternatively, one can contribute to an open-source programming project in your field or participate in one of the open innovation or people-sourcing challenges wherein participants are invited to use some biological data to make predictions which are then validated using already available datasets. Such challenges are effective because they provide a means of testing your skills using real-world data.
Internships in computational biology labs will enable you to acquire the skills you need and also bring your biological background to being a computational biologist. You can also participate in hands-on workshops and hack-a-thons, which can help develop different set of skills.
Computational Biology is an essential part of numerous facets of biological research. A hands-on approach, starting with a biological question of interest and learning skills that will help in resolving it can play a vital role in adapting to the rapid changes.