HARNESSING LARGE LANGUAGE MODELS FOR ADVANCING MATHEMATICAL BIOLOGY: A NEW PARADIGM IN COMPUTATIONAL SCIENCE
Main Article Content
Keywords
Mathematical biology, large language models (LLMs), com- putational biology, interdisciplinary research, hypothesis generation, data integration, machine learning, scientific innovation
Abstract
Mathematical biology is a dynamic interdisciplinary field that employs mathematical models and computational techniques to investigate and resolve complex biological phenomena. Recent advancements in compu- tational science, particularly the development of large language models (LLMs), have unveiled transformative opportunities to accelerate research and innovation in this domain. These sophisticated machine learning tools excel in tasks such as data analysis, natural language processing, and hypothesis generation, making them invaluable for addressing press- ing biological questions. This paper delves into the potential of LLMs to revolutionize mathematical biology by examining their diverse applica- tions, inherent advantages, and associated challenges. From automating literature reviews to facilitating multi-modal data integration and educa- tional advancements, LLMs demonstrate their versatility and capacity to enhance traditional methodologies. Furthermore, we propose a compre- hensive framework that integrates LLMs with established computational tools and experimental workflows, aiming to foster interdisciplinary col- laboration and propel the field toward groundbreaking discoveries. By addressing limitations such as interpretability, data dependency, and bi- ases, this integration can unlock new scientific frontiers and reshape the future of mathematical biology.
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[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2019.
[4] W. S. C. Gurney and R. M. Nisbet. Ecological Dynamics. Oxford University Press, 1998.
[5] J. Hofbauer and K. Sigmund. Evolutionary Games and Population Dynam- ics. Cambridge University Press, 1998.
[6] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
[7] Alfred J. Lotka. Elements of Physical Biology. Williams & Wilkins Com- pany, 1925.
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[9] James D. Murray. Mathematical Biology, volume 17 of Interdisciplinary Applied Mathematics. Springer, 3rd edition, 2002.
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[16] P. Turchin. Complex Population Dynamics: A Theoretical/Empirical Syn- thesis. Princeton University Press, 2003.
[17] Vito Volterra. Fluctuations in the abundance of a species considered math- ematically. Nature, 118:558–560, 1926.
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Published
Feb 13, 2025
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How to Cite
Dipans Verma, Dr. Sunil Dhaneshwar, Dr.Sandeep Kulkarni, & Dr. Bharti V Nathwani. (2025). HARNESSING LARGE LANGUAGE MODELS FOR ADVANCING MATHEMATICAL BIOLOGY: A NEW PARADIGM IN COMPUTATIONAL SCIENCE. Journal of Population Therapeutics and Clinical Pharmacology, 32(1), 885-893. https://doi.org/10.53555/dhwvb414
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Author Biographies
Dipans Verma
PhD scholar, University:Amity University Maharashtra, Mumbai,
Dr. Sunil Dhaneshwar
Professor, University name: Amity University Maharashtra, Mumbai.
Dr.Sandeep Kulkarni
Asst professor, Ajeenjya DY Patil University. Pune, Maharashtra
Dr. Bharti V Nathwani
Associate Professor, University name: Amity University Maharashtra,
How to Cite
Dipans Verma, Dr. Sunil Dhaneshwar, Dr.Sandeep Kulkarni, & Dr. Bharti V Nathwani. (2025). HARNESSING LARGE LANGUAGE MODELS FOR ADVANCING MATHEMATICAL BIOLOGY: A NEW PARADIGM IN COMPUTATIONAL SCIENCE. Journal of Population Therapeutics and Clinical Pharmacology, 32(1), 885-893. https://doi.org/10.53555/dhwvb414