Introduction
Modern biological research is working with an increasing amount of experimental data that complicate analysis. However, computer technologies have been actively used in science for faster and more accurate research in recent years. In particular, the field of computational biology uses the principles of mathematical analysis, statistics, and computer modeling to test hypotheses and forecasting experimental results. Computer models created by scientists allow exploring larger data sets and determine biological patterns, on the basis of which new scientific theories are created.
Computational Biology
Computational biology is an essential area of ​​modern biologic research, which can significantly improve their accuracy and speed. This discipline is broad and, in particular, is engaged in the development and use of “models of biological systems constructed from experimental measurements” (Murphy, n.d, para. 1). This area includes the creation of algorithms, computer simulations, mathematical models based on statistical biological data (Costanzo, 2021). Computational biology is especially widely utilized by scientists when sequencing and analyzing genes, which requires processing a large amount of information (Yanai & Chmielnicki, 2017). Often this term is associated with another area of ​​biological research called bioinformatics. However, computational biology, although part of this direction, differs from it.
Bioinformatics vs. Computational Biology
Overall, the difference is that bioinformatics involves big data research and uses the latest technology. This area aims to explore how scientists can make the most efficient use of all available technological and computing capabilities (Costanzo, 2021). Computational biology, in this case, acts as one of the tools that are used to simplify and reduce the cost of collecting and analyzing laboratory data (Costanzo, 2021). In general, the two areas intersect closely since “bioinformatics systems typically are needed to provide data to computational biology” (Murphy, n.d, para. 2). Thus, bioinformatics is a multidisciplinary field that uses the complex capabilities of modern science. At the same time, computational biology is utilized to solve particular problems and is part of this field.
The main function of computational biology is dealing with certain limited datasets. In particular, this area is used as a support or replacement for laboratory research, which increases the accuracy and reduces the complexity of the analysis. It is most efficient, for example, when “conducting population genetics and protein analysis or understanding specific pathways within a larger genome” (Costanzo, 2021, para. 7). Thus, computational biology is used to answer more general questions, while bioinformatics allows scientists to explore more narrow areas. Bioinformatics is more suitable for analyzed large data sets, as well as for the study of multidisciplinary research problems.
The Role in Biological Research
Currently, no innovative biological research is complete without the use of computational biology methods. For example, scientists in the field laid the foundation for RNA sequencing by creating a data analysis framework as well as a gene expression matrix (Yanai & Chmielnicki, 2017). Additionally, they have played a leading role in single-cell RNA research, which has resulted in a 5-fold increase in scientific publications on this topic over the past five years (Yanai & Chmielnicki, 2017, p. 1). It is noteworthy that most of the research in this area is led by scientists specializing in computational biology. Within the framework of applied use, computational biology is largely utilized for testing hypotheses (Yanai & Chmielnicki, 2017). However, the focus of this area is on collaboration with other fields that collect and use data.
The most important task of computational biology is, nevertheless, to represent biomedical issues as computational ones. Often this is due to the revision of the current biological system; the integration of new data to create a complete model is necessary (Murphy, n.d). The goal, in this case, may not be understanding the complex model but taking into account the largest amount of available experimental data. In this context, computational biology also performs the function of predicting experimental results based on pre-existing models (Murphy, n.d). Another problem that computational biologists solve is the refinement or creation of the most efficient and appropriate methods for data analysis (Murphy, n.d). Thus, this area forms the basis of modern biological research, as well as the development of tools for its functioning.
Computational Biology and Deep Learning
Currently, the size of the datasets with which computational biology needs to interact is growing steadily. Therefore, this area is actively utilizing deep learning principles to develop more efficient methods of modeling (Jones et al., 2017). Deep learning is a sub-section of machine learning that focuses on creating artificial neural networks that mimic the activity of the human brain (Tang et al., 2019). For computer biologists, using these technologies means more accurate modeling. Moreover, deep learning allows machines to learn, including on the raw input data, which significantly accelerates the analysis process and also provides great opportunities for forecasting (Jones et al., 2017). In particular, advances are used in medical diagnostics, image data analysis, and genetics research.
Conclusion
Computational biology is a relatively new field that has become the backbone of modern biological research. This direction uses mathematical analysis, statistics, computer modeling to test hypotheses and forecast the results of experiments. Although it is similar to bioinformatics, it works with more limited data sets and is used to solve specific problems. In recent years, computational biology has been actively used in genetics and has laid the foundation for RNA and DNA sequencing methods. It is often used as an auxiliary tool or laboratory research tool. He is currently actively adopting deep learning to process larger data sets and create more sophisticated models.
References
Costanzo, S. (2021).Computational biology vs. bioinformatics: What’s the difference? Northeastern University. Web.
Jones, W., Alasoo, K., Fishman, D., & Parts, L. (2017). Computational biology: Deep learning.Emerging Topics in Life Sciences, 1(3), 257-274. Web.
Murphy, R. F. (n.d). What is computational biology? Carnegie Mellon University. Web.
Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019). Recent advances of deep learning in bioinformatics and computational biology.Frontiers in Genetics, 10, 1-10. Web.
Yanai, I., & Chmielnicki, E. (2017). Computational biologists: Moving to the driver’s seat.Genome Biology, 18, 1-3. Web.