Computational Molecular Biology 2024, Vol.14, No.3, 125-133 http://bioscipublisher.com/index.php/cmb 131 difficult to scale these models to larger systems or longer timescales (Marrink et al., 2019). Additionally, ensuring the physical consistency of conservation laws across composite models is a complex task that can constrain progress (Pastor-Escuredo and Álamo, 2020). The integration of novel in vivo measurements and advanced computational techniques, such as machine learning, holds promise for overcoming these challenges, but the field is still in the early stages of developing these integrated approaches (Hussan et al., 2022). 7 Future Directions in Biophysical Modeling 7.1 Advances in multiscale modeling Multiscale modeling has emerged as a powerful tool to integrate data across different scales and uncover mechanisms that explain the emergence of function in biological systems. This approach is particularly effective in biomechanics, where it can bridge the gap between molecular biophysics and macroscopic tissue mechanics. Integrative biomechanics, which uses multiscale models to address clinical problems at the tissue and organ levels, exemplifies the potential of this approach. However, challenges remain in developing better models and acquiring the necessary data to parameterize and validate these models. The integration of machine learning with multiscale modeling offers a promising avenue to overcome these challenges by efficiently combining large datasets from different sources and levels of resolution, thereby creating robust predictive models that incorporate the underlying physics (Marrink et al., 2019). 7.2 Role of artificial intelligence and machine learning Artificial intelligence (AI) and machine learning (ML) are revolutionizing the field of biophysical modeling by providing new methods to analyze and interpret large, complex datasets. Recent advances in deep learning (DL) and reinforcement learning (RL) have opened up novel opportunities for mining biological data, which were previously intractable due to their size and complexity. The combination of ML with multiscale modeling can naturally complement each other, creating robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. This integration can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision-making for the benefit of human health. 7.3 Potential for personalized medicine applications Personalized medicine stands to benefit significantly from advances in biophysical modeling. Image-based predictive modeling of heart mechanics, for example, uses state-of-the-art cardiac imaging technologies, modern computational infrastructure, and advanced mathematical modeling techniques to noninvasively analyze and predict in vivo cardiac mechanics. This approach can aid in clinical diagnosis and developing personalized treatment plans by integrating in vivo measurements of cardiac structure and function using sophisticated computational methods. The potential for personalized medicine applications extends beyond cardiology, as integrative biomechanics can be applied to various clinical problems, including genomic applications and the development of improved interventional procedures and protocols. Addressing the challenges in this field will require a coordinated effort from both the clinical-imaging and modeling communities to bridge the gap between basic science and clinical translation (Mardt and Noé, 2021). Acknowledgments We would like to thank the anonymous reviewer for their valuable opinions and suggestions, and also thank Ms. Zhang for her literature collection. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Aland S., Hatzikirou H., Lowengrub J., and Voigt A., 2015, A mechanistic collective cell model for epithelial colony growth and contact inhibition, Biophysical Journal, 109(7): 1347-1357. https://doi.org/10.1016/j.bpj.2015.08.003
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