Computational biology: a brief introduction and its applications

In current biological sciences, generating huge amounts of data from molecular tools have become the main source of research. Computational biology arises from the application of mathematical and computational tools to provide help in the development of software and algorithms that facilitate the understanding of these data (10). It involves interdisciplinary sciences mainly mathematics and computation for the study of genes and proteins through the development of models and simulations (1,15). This field leads to the understanding of complex biological systems supported by other areas like genetics, medicine, biochemistry, etc. (12). Currently, some of the biological data produced by different research projects around the world are analyzed mainly with bioinformatic tools.

The beginning of bioinformatics began in 1960 with Dayhoff, Eck, and Ledley who had worked on the development of the first protein structure and sequence Atlas using their knowledge in mathematics, informatics, and biology (5). In the last decades, biological data volumes have grown quickly mainly due to the availability of new techniques of genome sequencing (14). This has led to researchers to understand and apply these tools through different computational languages and software (7). In that sense, bioinformatics tools help to solve complex biological problems by quickly organizing the data to ensure proper access to the information in databases and the development of computational tools for data analysis (13). In the next section, we show the importance of using bioinformatic tools to improve the comprehension of the new paradigm of biological sciences.

Nowadays, humanity faces a variety of health problems, such as limited availability of drugs, high costs, and secondary effects over treatments; therefore, the need for developing new molecules. Hence, there is a need to develop new molecules like peptides, proteins, and antibodies (2,3). For this purpose, we can use the DNA and protein sequencing technologies on balance with computational models and bioinformatics tools to predict potential molecules with structure and activity that could be applied in biological and pharmaceutical research (8). The development of new drugs are based on molecular docking and molecular dynamic tools by reducing time and operational costs through the identification of bioactive and useful candidate molecules without the use of reactive or sophisticated equipment (11). One example of this is the development of vaccines assisted by computational modeling for the discovery or design of molecules with desired immunological properties. The simulation of the interactions between potential molecules against a particular pathogen can reduce experimental times before laboratory experimentation (16).

Enzymes are widely used in different kinds of industries like food, agriculture, chemistry, pharmacy, etc. by their nature of catalyzing chemical reactions. An in silico bioprospection is being used to find enzymes of industrial interest quickly (9). For this, the use of bioinformatics tools are crucial to analyze some properties like structure, union sites, conformational changes & behavioral dynamics in a minimum time to know if a potential enzyme is useful or not in a particular industry field (4).

Finally, another example is the concept of Pharmacogenomics which refers to the discovery of drug variations on individuals using the omics sciences. Here the data obtained is intended to be used in new drug development or even redirecting current treatments to other pathologies. The influence of this area and its significance in preclinical discovery has led to interesting conclusions and applicability such as drug-gene alerts, this current implementation notifies physicians when pharmacogenomics information is available for determinate target gene-drug (17). These new approaches can help in complex and emerging infectious diseases where information is up to date and time is limited, for example during this pandemic researchers could find potential effects of drugs that can target SARS-CoV-2 through the use of transcriptomic data of patients, demonstrating the viability of these methods to find alternative treatments (6).

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We are grateful for the writing and edition of this article to Alex Aguirre, Francisco Sigcho, Jean Piere Ramos, Cesar Navarrete , Sebastian Bermudez and Arantxa Ortega