Biologyis undergoing a fundamental transformation from a primarily observational science to a data-driven
engineering discipline, catalyzed by the integration of high-throughput data and computational modeling. This paper explores
the emergence of Digital Biology, a field in which computer science and life sciences converge to address complex medical
challenges that were previously intractable. It examines how foundational technologies such as Generative AI, Digital Twins,
and CRISPR-Cas9 are shifting research methodologies from traditional trial-and-error approaches toward predictive and
precision-based engineering methods. Through a comprehensive literature survey and comparative analysis, the study analyzes
the impact of these technologies on drug discovery timelines and patient care outcomes, highlighting significant improvements
in efficiency, reproducibility, and accuracy. However, this digital transformation also introduces critical new challenges. As
biological systems become increasingly data-driven, they inherit the vulnerabilities associated with digital infrastructures and
interconnected networks. The paper investigates emerging cyberbiosecurity risks, including AI-assisted biological misuse and
irreversible genomic privacy threats, and concludes that the transformative benefits of digital biology must be supported by
strong security and ethical frameworks to ensure safe implementation.
[1] Wilke, C.O., Adami, C.: "The biology of digital organisms." Trends in Ecology & Evolution 17(11), 528–532 (2002).
[2] O’Neill, B.: "Digital evolution." PLoS Biology 1(1), e18 (2003).
[3] Witters, D., et al.: "Digital biology and chemistry." Lab on a Chip 14(17), 3225–3232 (2014)
[4] Jumper, J., et al.: "Highly accurate protein structure prediction with AlphaFold." Nature 596, 583–589 (2021).
[5] Elhefnawy, N. S.: "AlphaFold-Based Protein-Protein Interaction Prediction Methods Classification." In: Proc. 2025
5th Intl Conf. on Emerging Smart Technologies and Applications (eSmarTA), 2025.
[6] Kasoju, N., et al.: "Digital health: Trends, opportunities and challenges in medical devices sector." J Family Med
Prim Care 12(2), 123–129 (2023).
[7] Ho, A.V., et al.: "BioNeMo Framework: A Modular Library for AI Model Development in Drug Discovery." arXiv
preprint arXiv:2406.09573 (2024).
[8] Wu, J., et al.: "Towards generative digital twins in biomedical research." Comput Struct Biotechnol J 22, 123–135
(2024).
[9] Bobak, D., et al.: "Development of Online Anatomical Atlas for Biomedical Informatics." In: Proc. 2023 21st Intl
Conf. on Emerging eLearning Technologies (ICETA), pp. 68–75 (2023).
[10] Al-Sarem, M. M., et al.: "Deep Learning and Generative AI in Cancer Medical Imaging." IEEE Access 12, 1–15
(2024).
[11] Huang, J., Shi, Q., Xie, D., Duan, X.: "Visuomotor Policy Learning for Task Automation of Surgical Robot." IEEE
Trans Med Robot Bionics 6(1), 200-210 (2024).
[12] Murphy, S.V., Atala, A.: "3D bioprinting of tissues and organs." Nature Biotechnology 32(8), 773–785 (2014).
[13] McDermott, J. R., McBride, J. R., Taylor, N. J.: "Laboratory automation in life sciences: current trends and future
perspectives." SLAS Technology 26(3), 201–213 (2021).
[14] Gao, J., Hu, K., Zhang, F., Cui, X.: "Hexagonal Convolutional Neural Network for Spatial Transcriptomics
Classification." In: 2022 IEEE Intl Conf. on Bioinformatics and Biomedicine (BIBM) (2022).
[15] D'Acunto, M.: "Quantum Computation by Biological Systems." IEEE Trans Mol Biol Multi-Scale Commun 9(2),
257–262 (2023).
[16] Kavitha, R., et al.: "Emerging technologies in biological sciences research methodologies." Cuestiones de Fisioterapia
54(2), 1042–1050 (2025).
[17] Chaudhari, S., et al.: "Engineering End-to-End Remote Labs Using IoT-Based Retrofitting." In: Proc. IEEE Int. Conf.,
2025.
[18] Liu, T., et al.: "Cyberbiosecurity: Advancements in DNA-based information storage and security." Front
BioengBiotechnol 12 (2024).
[19] Sandbrink, E.: "Artificial intelligence and biological misuse: Differentiating risks of language models and biological
design tools." arXiv preprint arXiv:2307.10098 (2023).
[20] Ijaz, M.N., O’Neil, J.G.: "Cyber-Biosecurity Challenges in Next-Generation Sequencing: A Comprehensive Analysis
of Emerging Threat Vectors." In: Proc. IEEE Conference, 2024.
[21] Berger, K.M., Schneck, P.A.: "National and transnational security implications of asymmetric access to biological
data." Front BioengBiotechnol 7 (2019).
[22] Laith, A.L.E.: "Cyberbiosecurity in the age of biotechnology and AI." J Global Biosecurity 1(1), 1–10 (2019).
[23] Ney, P., et al.: "Computer Security, Privacy, and DNA Sequencing: Compromising Computers with Synthesized
DNA." USENIX Security Symposium (2017).
[24] Richardson, L.C., et al.: "Cyberbiosecurity: A New Discipline to Help Safeguard the Bioeconomy." Front. Bioeng.
Biotechnol. 7 (2019).
[25] Schabacker, D., et al.: "Assessing the Security of the DNA Synthesis Pipeline." Applied Biosafety 24(2), 70-76
(2019).
