Bioinformatics vs. Computational Biology

Bioinformatics refers to the study of large sets of biodata, biological statistics, and results of scientific studies. Some examples of bioinformatics studies include the analysis and integration of genetic and genomic data, cheminformatic comparisons of proteins to help improve personalized medicine, and the prediction of protein function from data sequence and structural information.

Computational biology, by contrast, is concerned with solutions to issues that have been raised by studies in bioinformatics. Both disciplines are generally considered facets of the rapidly-expanding fields of data science and biotechnology. Computational biology is useful in scientific research, including the examination of how proteins interact with each other through the simulation of protein folding, motion, and interaction.

Bioinformatics and computational biology are two fields that have arisen from the growth of bioenterprise around the globe. BIO, the Biotechnology Innovation Organization, predicts that advancements in biotechnology, bioinformatics, and computational biology will significantly assist the world as it faces the 21st century’s impending resource challenges.

The respective fields of bioinformatics and computational biology are often integrated with laboratories, research centers, or colleges. As both fields rely on the availability and accuracy of datasets, they usually help one another reach their respective project goals. While computational biology emphasizes the development of theoretical methods, computational simulations, and mathematical modeling, bioinformatics emphasizes informatics and statistics.

Though the two fields are interrelated, bioinformatics and computational biology differ in the kinds of needs they address. While both fields pursue greater utilization of our collective biological understanding, bioinformatics tends to concern itself with the gathering and collation of biodata, and computational biology with the practical application of this biodata.

Check out the differences between the related fields of bioinformatics and computational biology for reference and clarification.

Similarities, Differences, and Overlap: Bioinformatics vs. Computational Biology

Bioinformatics Computational Biology

Bioinformatics is the process by which biological problems posed by the assessment or study of biodata are interpreted and analyzed. Bioinformatics professionals develop algorithms, programs, code, and analytic models to record and store data related to biology. This includes the study of the human genome, biochemical proteins, pharmacological ingredients, metabolic pathway readings, and much more. These sets of data form the basis of what is often seen as the next step in the process: computational biology.

Computational Biology is concerned with solutions to issues that have been raised by studies in bioinformatics. In many cases, the phrases “bioinformatics” and “computational biology” are used interchangeably, particularly in job descriptions or position titles. This is due, in part, to the fact that the two fields have been around for only a few short decades. Computational biology has been used to build highly-detailed models of the human brain, map the human genome, and assist in modeling biological systems. Computational biology involves researching, developing, and implementing algorithms or tools that address biological questions, concerns, or challenges that have been raised by bioinformatic analyses.

Applications Bioinformatics is a rich specialization in which the myriad uses of data are explored, from pharmacology to antibiotics, from green technologies to climate change studies. These, and many more, are included in a running list of bioinformatics applications in the fields of:
  • Microbial genome applications
  • Molecular medicine
  • Personalized medicine
  • Antibiotic resistance
  • Preventative medicine
  • Drug development
  • Gene therapy
  • Evolutionary studies
  • Biotechnology
  • Waste cleanup
  • Crop improvement
  • Insect resistance
  • Alternative energy sources
  • Artificial intelligence
  • Zoology
  • Animal behavioral studies
  • Climate change studies
  • Forensic analysis
  • Machine learning
  • Bio-weapon creation
  • Improvement of nutritional quality
  • Development of drought-resistant varieties
  • Veterinary science
Computational Biology is used, for example, to aid in a virologist’s work on selecting the proper vaccination strain for influenza. Whether analyzing molecular medicine compounds or calculating metabolic pathway proteomics, computational biology allows professionals to build the toolkit to fit the task. Other applications include, but are not limited to:
  • Stochastic models
  • Systems biology
  • Machine learning
  • Molecular medicine
  • Preventative medicine
  • Artificial intelligence
  • Metabolic pathway studies
  • Cellular biology
  • Data mining
  • Biochemical studies
  • Radiotherapy
  • Deep learning
  • Veterinary studies
  • Neural networks
  • Oncology
  • Animal physiology
  • Text mining
  • Advanced mathematics
  • Genomic trends
  • Genetic analysis

Most careers available in bioinformatics can be found in computer information science, pharmaceuticals, biotechnology, medical technology, computational biology, proteomics, and medical informatics. They often develop algorithms, build databases, and present data, studies, and research to other bioinformatics professionals. The databases they build are typically used for processing and analyzing things like genomic information or genetic trends.

The majority of the computational biology careers one might find on BioSpace, Science Careers, or Indeed involve data mining, data extraction, content curation, research, data analytics, bioinformatics theory, data management, programming, technical writing and documentation, and project management.

Subfields The field of bioinformatics encompasses a wide array of sub-disciplines that are underpinned by a scientific ethic grounded in the biological sciences. Closely related to computational biology, bioinformatics continues to grow in scope and utility. Examples of a few of the many fields informed by the work of bioinformatics include:
  • Computational biology: the application of solutions to problems in bioinformatics
  • Genetics: the study of heredity and the variation of inherited characteristics
  • Genomics: the branch of molecular biology concerned with the structure, function, evolution, and mapping of genomes
  • Proteomics: the study of proteins and their functions
  • Metagenomics: the study of genetic matter from environmental sources and samples
  • Transcriptomics: the study of the complete RNA transcriptome
  • Phylogenetics: the study of the relationships in groups of animals and humans
  • Metabolomics: the study of the biochemistry of metabolism and metabolites
  • Systems biology: the mathematical modeling and analysis of large sets of biodata
  • Structural analysis: an assessment that determines the effects of physical loads on physical structures
  • Molecular modeling: the modeling of molecular structures by way of computational chemistry
  • Pathway analysis: a software assessment that identifies related proteins in metabolic pathways
As an emerging discipline, computational biology is gaining momentum in the medical and tech worlds as an excellent means of quantifying huge sets of data. Computational biology is an area in which there is a great deal of overlap with bioinformatics. In fact, the two fields have developed in tandem with one another and often find use in systems where data and data models of biological data are necessary. Some other occupational offshoots include:
  • Bioinformatics: the process by which biological problems are interpreted and analyzed
  • Computational bioengineering: various data-based methods used to study biological or ecological systems
  • Computational biomechanics: various data-based methods used to study the effect of forces on biological structures
  • Computational bioimaging: the visualization of real-time biological processes
  • Mathematical biology: the study of biological systems using mathematical models
  • Theoretical biology: related to the emerging field of mathematical biology
Tools & Software
Featured Academic Programs

Northeastern University, online MSc: Northeastern University offers an online master’s in bioinformatics degree that focuses on subjects such as bioinformatics programming, bioinformatics computational methods, and ethics in biological research. The program also offers a graduate certificate in data science.

Rochester Institute of Technology, on-campus MSc: The Rochester Institute of Technology offers an on-campus master’s in bioinformatics degree that focuses on ethics in bioinformatics, bioinformatics algorithms, database management for the sciences, computational statistics, and data science methods, and molecular modeling and proteomics.

Rochester Institute of Technology, on-campus BSc: The school also offers an innovative bachelor of science program in bioinformatics and computational biology. This program includes courses such as bioinformatics programming, ethical issues in biology and medicine, fundamental bioinformatics analysis, biochemistry, introduction to database and data modeling, and introduction to bioinformatics.

Coursera, bioinformatics courses: This comprehensive collection of courses from leading universities in bioinformatics offers introductory, intermediate, and advanced-level studies.

Additionally, there are several graduate certificates available, including those from:

Columbia University in the City of New York, online MSc: Columbia University offers an online computer science master’s degree in computational biology that focuses on subjects such as computational genomics, database systems, advanced machine learning, deep learning for computer vision, and natural language processing, and statistical modeling and data analysis.

The University of Idaho, on-campus MSc: The University of Idaho offers an on-campus master’s degree in computational biology that focuses on subjects such as laboratory experience in the biological sciences, laboratory experience in the computational sciences, laboratory experience in mathematics or statistics, evolutionary biology for non-life scientists, and computational biology.

Carnegie Mellon University, on-campus BSc: Renowned research university Carnegie Mellon offers an esteemed undergraduate program in computational biology. This program includes courses such as algorithms and advanced data structures, algorithm design and analysis, great ideas in theoretical computer science, principles of imperative computation, cell biology, biochemistry, and modern biology.

Coursera, computational biology courses: As perhaps the industry leader in open education models, Coursera’s comprehensive collection of courses from leading universities in computational biology offers introductory, intermediate, and advanced-level studies.

Additionally, there are several related graduate certificates available, including those from:

The Bottom Line: The Fields of Bioinformatics & Computational Biology

As industries poised to forever change the way the world does ethical, responsible, sensible business, bioinformatics and computational biology use the last century of research in biology to reforge the discipline for the needs of the coming century.

Taking cues from the world’s organisms to build a healthier and cleaner future and making use of the staggering number of applications in the modern tech landscape, one can rest assured that as science’s collective knowledge grows (and as the very definition of biology evolves), the usefulness of biotechnologies will become unquestionable.

The Bureau of Labor Statistics reports an uptick in the percentage of bioinformatics and computational biology positions in the wider economy, such as in the occupations of computer and information research scientists, bioinformatics technicians, and biomedical engineers.

Kenneth Parker
Kenneth Parker Writer

Kenneth Parker is a feature writer, poet, and musician living in the Pacific Northwest. His writing on remote work, education, and technology has been published by,, and other websites. His poetry, short fiction, and album reviews have appeared in Scifaikuest, Nanoism, and No Clean Singing. His background includes time spent as an associate editor, proofreader, private grammar instructor, freelance content editor, medical claims agent, and SEO consultant. He is a graduate of the University of Oregon, where he studied literature and worked as a composition tutor.