Computational biology, which is also known as bioinformatics, is the combined application of math, statistics and computer science to solve biology-based problems.
Bioinformatics covers a wide range of biology topics, such as genetics, evolution, biochemistry, biophysics, and cell biology. Computational biology leverages quantitative tools such as machine learning, statistical physics, algorithm design and frequency statistics.
A Brief Introduction
Computational biologists are tasked with the development and application of data-analytical tools, theoretical methods, mathematical modeling and software simulation techniques to explore biological systems. Computation is now an essential part of biological research projects. For example, protein data banks, genomic databases and brain MRI images contain massive amounts of raw data that can be translated into insightful information about all aspects of biology.
Computational biologists use their math and computational skills to translate biological processes into computational models. Computational biologists actually work in very different interdisciplinary sub-fields, such as systems biology, population genetics, molecular networks and medical, functional, comparative, agricultural genomics. Students must obtain a broad, interdisciplinary knowledge of the basic principles of math, biology and computational science.
Unique Problem Solving
Many fields of the life sciences, such as biology and chemistry, now rely on quantitative prediction and interpretation to address complex questions that can only be answered using advanced statistical, mathematical and computational tools. The amount of data available for biologists now requires substantive quantitative approaches to make accurate analyses and interpretations. The power of computers means that researchers can explore sophisticated and highly complex problems.
Computational biologists usually have a graduate-level degree and choose to either focus on biology or math and computers. They take classes in probability models, inferential statistics and quantitative genetics and genomics. Three of the most common programming languages studied include C++, Python and MATLAB. Most programs require classes in statistical theories, data mining and machine learning. Other common classes include differential equations, dynamical systems and bioinformatics programming.
Oncology research and development departments employ computational biologists as key members of multidisciplinary teams who work to discover and develop new cancer treatment medicines. They are responsible for providing computational support to their fellow researchers by mining public and private genomic data and investigating potential treatment pathways. They strive to identify predictive biomarkers through understanding cellular mechanisms in clinical settings.
These computational biologists will be expected to have a master’s or Ph.D. in bioinformatics, statistics or related field. They must have experience with the analysis and processing of genomic data as well as knowledge of industry standard pathway tools and network analysis databases used in the field of cancer research, such as GSEA and DAVID. They will also need the ability to effectively work in a multidisciplinary team setting and effectively communicate in a clear and concise manner. Experience with scripting, machine learning and drug discovery and development is preferred.
Readers should note that the terms computational biology and bioinformatics are often used interchangeably, but the first usually connotes the development of algorithms and mathematical models, while bioinformatics is commonly associated with the development of software and visualization tools.