An Undergraduate’s Guide to Entering Computational Biology

Student writer: Mahir Jethanandani
Student editors: Ana Lyons
23andMe editor: Thao Do

Upon the start of last semester, I had little idea about what computational biology consisted of. By definition, computational biology is an interdisciplinary field that develops and applies computational methods to analyse large collections of biological data, such as genetic sequences, cell populations or protein samples, to make new predictions or discover new biology. In other words, the computational study of biology, as the name would imply. Little did I know that the subject meant so much more.

My first introduction to computational biology came from a seminar course entitled Connect: Computational Biology. In the course, I explored core techniques such as how to use gene alignment tools and analyze research papers, learned about the range of career paths, and heard from speakers who were the best in the field. We visited sequencing laboratories in the local area and learned how difficult it is to sequence the entire human genome.

In this course, PhD students in Public Health discussed the advisor-student selection process, and the breadth of courses taken, from gene regulation to artificial intelligence. Verily Life Sciences and Amgen alumni spoke about past projects in computational biology, and the relationship between life sciences and quantitative sciences.

All in all, the most glaring question that surfaced at each session, however, was: How does anyone get into the field?

If you are fortunate enough to have a Computational Biology core curriculum offered at your school, take them! In most cases, the applicable classes will be spread throughout multiple departments in undergraduate and graduate programs.

The key classes to take include Introduction to Computational Biology (duh!), Quantitative Cell Laboratory, Computational Genomics, Biological Modeling & Simulation, Statistics and Programming, and Biotechnology in Computational Biology.

These core computational biology classes will help you learn about microarray technology and understand computational biology’s place in genetics.

If your college or university does not offer a designated computational biology major, it can be useful to look at other schools that offer the major in order to determine what classes would be relevant to the computational biology field. For example, Carnegie Mellon University offers a biology core curriculum that includes modern biology, biochemistry, genetics, and cell biology, intended for students interested in computational biology.

Carnegie Mellon University also offers a Computer Science and Mathematics/Statistics core curriculum for computational biology featuring classes including: Introduction to Computer Science, Data Structures, and Algorithms courses fill out the computer science portion. An Introduction to Machine Learning class (bonus points if focused on computational biology) is also helpful to take, and is typically offered by either Computer Science or Bioengineering departments. These classes are sometimes found in graduate and not undergraduate programs, and students may need to contact instructors to get approval to take the respective class.

The suggested classes in Computer Science and Mathematics/Statistics core curriculum will help students develop skills in the cross-section of quantitative sciences and computational biology, like memory and time-efficient DNA alignment (developing methods to reduce the time and resources required for DNA alignment).

According to Dr. Dhivya Arasappan at the University of Texas, Austin, a balanced course load in biology and the quantitative sciences will help you “communicate with biologists, computer scientists, and statisticians alike. A basic foundation in all of the areas (CS, biology, and statistics) is necessary.”

If you opt to pursue Mathematics/Statistics classes, plan on taking courses like: Linear Algebra and Differential Equations, Probability, and Statistical Methods classes. Taking a real and/or numerical analysis class can build your mathematical maturity, and prepare you for classes like Statistical Learning Theory or Machine Learning. Extracting “how to analyze large amounts of biological data (basic statistics, biostatistics, machine learning type courses)” is the main takeaway from these classes, says Dr. Arasappan.

Enough about classes—how else can you break into the field? The best way to familiarize yourself with the field before gaining any technical knowledge is to spend time among similarly-driven people who are passionate about computational biology. Authentic computational biology enthusiasts can be found populating student groups on campus. Most computational biology organizations are centered around education and community,  which allow for a great environment to network and learn about the work and experiences of other students with similar interests.

If coursework ever feels like theory without application, laboratory experience can serve as a bridge to get hands on experience related to the field. Speaking with biology or neuroscience faculty who serve in advisory positions for students  majoring or conducting research in their field can help to build a shortlist of professors and labs to approach for lab experience. Demonstrating genuine interest in research, and being engaged with student groups and campus events, can show faculty and potential research mentors that your are serious about learning more about computational biology. When the times and opportunity  comes to applying to different labs, the worst outcome that can occur is that you get rejected. Even then, do not get discouraged. Just because you get a rejection from one lab does not mean you will get passed over by another one. Persistence is the key.

While my summer at 23andMe was not focused in computational biology, but rather software engineering, computational biology has a home for anyone interested in the field. Even as an intern, your skills can be well-suited to aid the company in advancing computational biology and personal genetics. In general, “many labs are generating large biological datasets and are desperately looking for people with the skills to analyze them, so there is great demand for someone with a computational biology skill set in research”, said Dr. Arasappan.

Overall, computational biology is still a new field, and there is much flexibility in what one can choose in undergraduate studies. Studying the life sciences, with a dash of quantitative subjects, is the common and useful path to computational biology. Any students with a knack for mathematics or human biology should consider pursuing the field. Laboratories are real-world applications of textbook knowledge, and student organizations are educational communities for the field, at best.

Here’s to the field of computational biology!

  1. Degree Requirements | Computational Biology Department. Carnegie Mellon University. 2018.


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