HARNESSING LARGE LANGUAGE MODELS FOR ADVANCING MATHEMATICAL BIOLOGY: A NEW PARADIGM IN COMPUTATIONAL SCIENCE

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.