What is Bioinformatics?

Bioinformatics is an interdisciplinary field that has become critical for managing data in modern science and technology, including medicine.1 Bioinformatics can be described as the science of storing large volumes of complex biological data, as well as analysis of this data for novel insights, which is then applied to various fields to answer big questions and challenges.2

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Core Concepts

The scientific field of bioinformatics includes an interrelationship of three separate areas encompassing: a scientific area, through the gathering of tools and techniques from various subjects such as molecular biology as the source of data that is analyzed; informatics or computer science, which provides the hardware for analysis, as well as the networks for sharing results; and mathematics, which is used in the algorithms for data analysis.3

The complementary combination of these fields provides a foundation for bioinformatics applications for areas such as molecular biology.3 This field involves biologists with knowledge of computer programming, or computer programmers, mathematicians and database managers that learn foundational biology.2

Bioinformatics also involves processing and storing biological data, as well as analyzing; this may include creating databases for storing experimental data, predicting how proteins fold, and modeling how chemical reactions within a cell interact with each other.2

Learn more about Structural Bioinformatics

Modern Techniques and Tools

Basic biological research requires deduction of the order of DNA sequences, with many significant applications in biotechnology. Modern DNA sequencing technologies has enabled complete sequencing of DNA sequences or genomes, such as the human genome.3

Following the first genome projects, powerful sequencing platforms were developed such as next-generation sequencing (NGS). NGS is a high-throughput method that enables large base-pair sequencing in DNA and RNA samples, which revolutionized the volume of samples that could be sequenced at one time, as well as having impact on studying gene expression profiles, epigenetic changes, mutations and molecular analysis for personalized medicine.3

Additionally, technologies used in bioinformatics includes sequence alignment as a basis for sequence analysis, which is a software that usually inserts gaps between nucleotides or amino acid residues within the sequences in order for as many similar sites to be aligned as a way to overcome challenges in short read sequences.4

Molecular modeling including biomolecular modeling is also a significant component of bioinformatics, with the RCSB Protein Data Bank being a worldwide repository for processing and distributing data on the 3D structure of macromolecules such as proteins and nucleic acids.5

Other technologies include systems biology, which is also an interdisciplinary field that combines biology, statistics, mathematics and computer science, and aims to address collective behaviors within biological systems that may escape traditional molecular approaches.6

Recent computational systems models have integrated diverse datasets that have used mathematical, machine learning and artificial intelligence (AI) methodologies to investigate the complexity of biological processes.6

Another innovative development includes the integration of single-cell data into computational models, with single-cell technology and single-cell RNA sequencing having significant impact on computational modeling, which provides novel insights into complexity and heterogeneity of biological processes within and across cells.6

This enables further comprehension of biological processes at a cellular level, heterogeneity within cells, and differentiation pathways, which can aid with mapping the developmental trajectory of cells and discovering new cell types and states.6

What is Bioinformatics?

Applications and Impact

Advancements and developments in technologies such as NGS and computing have furthered the use of mathematical and computational approaches for modeling purposes to simulate biological processes with complex datasets.6

Additionally, computational models enable scientists and researchers to simulate experiments, make predictions of the outcomes of biological processes, as well as produce novel hypotheses to test.6

Computational systems modeling within pharmacology is significant in drug discovery, which aids in pharmaceutical research by predicting interactions between drugs and their targets. This can streamline the drug development process as it decreases reliance of trial-and-error methodologies.6

These models can also aid with understanding the mechanism of action of drugs and diseases, as well as genetic disorders, which can help with developing novel gene therapy techniques. Applications in multi-omics including genomics and proteomics can aid with gaining a more comprehensive understanding of how diseases develop in order for unique and more effective therapies.6

Computational modeling is also critical for advancing personalized medicine, encompassing tailored treatments to individual genetic profiles, ensuring effective treatment and identifying any adverse effects that may occur.6

An example of a breakthrough in computational modeling includes creating whole-cell to multi-scale models that represents cells and tissues; this breakthrough started with a model of a unicellular organism called Mycoplasma genitalium.6

The progression of this research includes creating models that represent whole-cell processes for complex systems such as humans. Additionally, more complex models of complete systems including the human immune system, called ‘digital twins’ are also under construction, with the aim being of high quality for computational experiments that can predict real-life outcomes.6

Find out more about data integrity and compliance

Challenges and Future Directions

Bioinformatics, which has vast applications for many fields, also consists of many challenges including data integration, privacy issues, as well as requiring more advanced computational resources.6,7

Challenges in computational modeling includes the integration of heterogenous data types, including genomics, proteomics, transcriptomics, metabolomics and epigenetics, as well as when the data has been obtained from different studies or at different timepoints. This can be an obstacle that impacts data quality and completeness, as datasets may include missing values and biases.6

Additionally, more advanced computational resources may be required for processing large high-throughput data, which may need more robust infrastructures as well as efficient data processing algorithms.6

Privacy issues can also be a concern, with the sharing of genomic data for advancing personalized medicine, potentially leading to privacy breaches in novel technologies such as direct-to-consumer genetic testing applications.7

Emerging technological trends such as AI and machine learning within bioinformatics can be integrated into computational models to overcome challenges and shape the future of personalized medicine and biotechnology by enabling more comprehensive understanding of biological processes, leading to more targeted and efficient treatments.6

AI and machine learning driven processes can also aid in reducing the cost and time for analyzing and interpreting data from large datasets, in order to advance processes involved in bioinformatics.8

Conclusion

Bioinformatics plays a significant role in advancing scientific research, with integration of mathematics and computational modeling with various fields such as biological research and medicine, which provides innovative insights into disease association, new targets for drugs, and gene therapy.6

With continued innovation and research, as well as advanced computational resources and power, bioinformatics may have the potential to progress the simulation and analysis of complex biological models including multi-scale, multi-cellular, and whole organisms, which may be achieved in the future. From targeted therapies to digital twins, bioinformatics has the potential to revolutionalize healthcare.6

References

  1. Bayat A. Science, medicine, and the future: Bioinformatics. BMJ. 2002;324(7344):1018-1022. doi:https://doi.org/10.1136/bmj.324.7344.1018
  2. What is bioinformatics and how do we use it? Your Genome. Available from: www.yourgenome.org. https://www.yourgenome.org/theme/what-is-bioinformatics-and-how-do-we-use-it/#:~:text=Bioinformatics%20is%20the%20science%20of%20both%20storing%20lots
  3. Branco I, Choupina A. Bioinformatics: new tools and applications in life science and personalized medicine. Applied Microbiology and Biotechnology. 2021;105(3):937-951. doi:https://doi.org/10.1007/s00253-020-11056-2
  4. Chao J, Tang F, Xu L. Developments in Algorithms for Sequence Alignment: A Review. Biomolecules. 2022;12(4):546. doi:https://doi.org/10.3390/biom12040546
  5. Aminpour M, Montemagno C, Tuszynski JA. An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications. Molecules. 2019;24(9). doi:https://doi.org/10.3390/molecules24091693
  6. Puniya BL, Verma M, Damiani C, Bakr S, Dräger A. Perspectives on computational modeling of biological systems and the significance of the SysMod community. Bioinform Adv. 2024 Jun 26;4(1):vbae090. doi: https://doi.org/10.1093/bioadv/vbae090
  7. Bonomi L, Huang Y, Ohno-Machado L. Privacy Challenges and Research Opportunities for Genomic Data Sharing. Nature genetics. 2020;52(7):646-654. doi:https://doi.org/10.1038/s41588-020-0651-0
  8. Alber M, Buganza Tepole A, Cannon WR, et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digital Medicine. 2019;2(1):1-11. doi:https://doi.org/10.1038/s41746-019-0193-y

Further Reading

Article Revisions

  • Sep 17 2024 - The article on bioinformatics has been comprehensively updated for clarity, depth, and accessibility, now reflecting recent advancements and applications in the field. The introduction has been revised to better define bioinformatics, illustrating its interdisciplinary nature and role in managing complex biological data. Core concepts have been expanded, with added examples of bioinformatics' integration with molecular biology, informatics, and mathematics. New content on "Modern Techniques and Tools" covers advancements in DNA sequencing, molecular modeling, and systems biology, including applications of AI and machine learning to explore complex biological processes. The "Applications and Impact" section now highlights bioinformatics’ role in drug discovery, personalized medicine, and disease modeling, with examples like whole-cell models and "digital twins." Challenges in data integration, privacy, and computational resources are outlined, along with future directions where AI could address these issues. A new conclusion summarizes bioinformatics’ role in advancing science and healthcare. Updated references ensure accuracy, and improved formatting enhances readability, making the article more informative and accessible for readers.

Last Updated: Nov 4, 2024

Marzia Khan

Written by

Marzia Khan

Marzia Khan is a lover of scientific research and innovation. She immerses herself in literature and novel therapeutics which she does through her position on the Royal Free Ethical Review Board. Marzia has a MSc in Nanotechnology and Regenerative Medicine as well as a BSc in Biomedical Sciences. She is currently working in the NHS and is engaging in a scientific innovation program.

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