Guest post by Teresa Przytycka, PhD, Senior Investigator, Computational and Systems Biology section of the Computational Biology Branch at the National Library of Medicine’s National Center for Biotechnology Information, National Institutes of Health
If you pose the question – “What is the difference between computational biology and bioinformatics?”– you will get many contradictory answers. Terminology aside, most researchers would agree that the space between traditional biology and traditional computer science is wide enough to accommodate many different models of collaborations between these two groups of researchers.
Bioinformatics analysis, which involves the analysis of biological data such as DNA, RNA, and protein sequences, has become a standard step required after many types of now routine experiments. For example, after performing an experiment measuring gene expression in different conditions, bioinformatics analysis is likely to be used to compare gene expression between these conditions. In this setting, while experimental and computational components are necessary for the success of the project, only limited interaction between the experimentalist (the user of the tool) and the computational expert (the producer of the tool) is required.
However, given the richness of biomedical data and the complexity of the relations between various bimolecular entities, such as genes and proteins, researchers can be challenged to ask questions that cannot be answered through traditional means. In such cases, the user-producer model of collaboration is increasingly replaced by a different model of collaboration where biologists and computer scientists work side-by-side to both formulate and answer questions. Such collaborations across disciplines can introduce new perspectives and approaches to spur innovation and open the door to addressing new challenging questions.
Recognizing the need to foster interdisciplinary science, NIH formed an interdisciplinary committee to explore the development of a systems biology center at NIH in 2008. The driving idea behind such a center was to create a space where people of different backgrounds can mix, exchange ideas, and through these exchanges come to solutions to open biomedical questions enabled by interdisciplinary approaches. While this effort did not result in the establishment of a physical center, per se, it did give rise to a network of interdisciplinary collaborations. Interestingly, two of the collaborations NLM started at the time still continue today: the collaboration with the Center for Cancer Research at the National Cancer Institute focusing on conformational dynamics of DNA structure, and the collaboration with the Laboratory of Cellular and Developmental Biology’s Developmental Genomics Section at the National Institute of Diabetes and Digestive and Kidney Diseases studying various aspects of gene regulation.
What made these collaborations successful and long-lasting?
Perhaps most importantly, the process of bringing experimental and computational groups together calls for a willingness to learn each other’s languages, thought processes, and cultures — or the proverbial walking in someone else’s shoes.
Over the years, we learned to think together, work together, and publish many papers together. For example, in one of our joint projects, our experimental partners collected data that helped the computational group construct the gene regulatory network for a fly that can later be utilized by other researchers studying this model organism. In addition to the joy of advancing discovery together, these collaborations opened doors to foster synergies among young computational and experimental researchers from the collaborating groups. These interactions have enriched their NIH experience in important ways, giving them skills that they are likely to find very useful in the future — whether they go on to build their own research groups or follow other career choices.
I am confident that NLM’s support for interdisciplinary research through scientific collaborations will continue to spur innovation and discovery. It will also help to train a generation of researchers who can seamlessly work with people of difference scientific backgrounds.
What do you value in your collaborations?
Teresa M. Przytycka, PhD, leads the Algorithmic Methods in Computational and Systems Biology section at the National Center for Biotechnology Information. Dr. Przytycka is particularly interested in the dynamical properties of biological systems, including spatial, temporal and contextual variations, and exploring how such variations impact gene expression, the functioning of biological pathways, and the phenotype of the organism.
I’m curious about the question you start with here. I’ve always thought of them as being the same thing since NLM seems to think so as they are synonyms in MeSH and the term is treed both under Biology and Informatics. What’s your answer? Is there a difference between computational biology and bioinformatics or not?
Hi Melissa,
Many people use these terms interchangeably, but intuitively computational biology focuses more on biology and uses computational models to express and solve biological questions. In turn bioinformatics is more data centric and helps to process, store, and analyze biological data. For example, if a researcher said he/she did “Bioinformatics analysis” it means that (s)he analyzed properties of the data. But (s)he would build “computational model” to answer a specific biological question. So the distinction is subtle. See for example http://cbd.cmu.edu/about-us/what-is-computational-biology.html or https://diamondage.com/2017/07/05/bioinformatics-vs-computational-biology/
Teresa
“Computational biology” begins with an adjective and ends with the name of a fundamental discipline (noun). Alas, “Bioinformatics” expresses only the latter. Enliven it adjectivally and you have, for example, “Evolutionary Bioinformatics.” As expressed in my textbook of that name, this implies a blending of information science with evolutionary biology. The adjective “computational” implies a higher order of mathematics, such as Noah’s famous square root of 4 rule for loading an ark.