Understanding the Difference Between Bioinformatics and Computational Biology

Bioinformatics and computational biology are two rapidly growing interdisciplinary fields that apply computational techniques to better understand biology. Though the names sound similar, there are some key differences between these two areas of study.

Bioinformatics

Bioinformatics is focused primarily on the development and application of computational methods to analyze and interpret large biological data sets. For example, bioinformaticians may develop algorithms for sequence alignment to compare DNA or protein sequences across organisms.

Other common bioinformatics tools include gene expression analysis platforms, network analysis tools to study biological networks, and machine learning algorithms to detect patterns in biological data.

The field draws largely from computer science, statistics, mathematics, and molecular biology. Those working in bioinformatics typically have backgrounds in one or more of these disciplines.

Example research applications include studying the genetic basis of disease, comparative genomics analyses, and design of new therapeutics.

Computational Biology

Computational biology takes a broader view and involves modelling and simulation of complex biological processes. For instance, computational biologists may create mathematical models depicting how cells communicate with each other via signals or how protein shapes change over time.

They perform in silico experiments with these models to generate predictions that can be tested experimentally.

Given the focus on modelling, computational biology relies heavily on mathematics, physics, chemistry, and computer science disciplines.

Researchers in this field often have specializations in one of these quantitative sciences. Potential applications are vast and include topics like computational neuroscience, pharmacokinetic modelling, or simulating the mechanics of embryonic development.

Key Differences between the fields

Though there is significant overlap between bioinformatics and computational biology, a few key differences help distinguish the two:

Bioinformatics focuses more on data-driven approaches, whereas computational biology emphasizes modelling and simulation.

Bioinformatics analyses specific biological data types (DNA, RNA, etc), while computational biology models behave more abstractly and generally.

Bioinformatics is applied to genetics, genomics, and molecular-level analyses more frequently, while computational biology is better suited for systems biology topics.

Bioinformatics has more direct applications to studying diseases, drug design, etc. Computational biology helps understand fundamentals like protein folding, gene regulation, signaling pathways.

Modern software platforms are starting to converge both tools to provide integrated environments for not just analyzing but also modeling biological phenomena.

There are certainly many shared aims - both fields leverage computational tools to elucidate complex biology and require cross-disciplinary teams spanning computer science, engineering, physics, statistics, and more.

But keeping the distinctions in mind helps clarify the specific problems best suited for either bioinformatics or computational biology.

Though they have divergent approaches, bioinformatics and computational biology methods play complementary roles in the chase to solve biological complexity.

As software platforms continue improving researcher accessibility to data sets and modelling tools, we can expect an acceleration of biological discovery powered by insights from both data-driven bioinformatics and simulation-focused computational biology approaches.