Kannan Meera, Bridgewater Gabrielle, Zhang Ming, Blinov Michael L
Center for Cell Analysis and Modeling, UConn Health, Farmington, CT, 06030, USA.
Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM, 87544, USA.
NPJ Syst Biol Appl. 2025 Feb 4;11(1):15. doi: 10.1038/s41540-025-00496-z.
Predictive mathematical modeling is an essential part of systems biology and is interconnected with information management. Systems biology information is often stored in specialized formats to facilitate data storage and analysis. These formats are not designed for easy human readability and thus require specialized software to visualize and interpret results. Therefore, comprehending modeling and underlying networks and pathways is contingent on mastering systems biology tools, which is particularly challenging for users with no or little background in data science or system biology. To address this challenge, we investigated the usage of public Artificial Intelligence (AI) tools in exploring systems biology resources in mathematical modeling. We tested public AI's understanding of mathematics in models, related systems biology data, and the complexity of model structures. Our approach can enhance the accessibility of systems biology for non-system biologists and help them understand systems biology without a deep learning curve.
预测性数学建模是系统生物学的重要组成部分，并且与信息管理相互关联。系统生物学信息通常以专门的格式存储，以方便数据存储和分析。这些格式并非为便于人类阅读而设计，因此需要专门的软件来可视化和解释结果。因此，理解建模以及基础网络和通路取决于掌握系统生物学工具，这对于没有或几乎没有数据科学或系统生物学背景的用户来说尤其具有挑战性。为应对这一挑战，我们研究了公共人工智能（AI）工具在探索数学建模中的系统生物学资源方面的应用。我们测试了公共AI对模型中的数学、相关系统生物学数据以及模型结构复杂性的理解。我们的方法可以提高非系统生物学家对系统生物学的可及性，并帮助他们无需经历深度学习曲线就能理解系统生物学。
