Computational Molecular Biology 2024, Vol.14, No.3, 125-133 http://bioscipublisher.com/index.php/cmb 128 with disease states. Additionally, a model integrating biomechanics and biochemistry has been used to study cell migration in the context of wound healing, providing a quantitative understanding of spatiotemporal waves and their role in collective cell migration (Gou et al., 2020). 4.2 Understanding cell-cell and cell-matrix interactions Biophysical models have been instrumental in elucidating the interactions between cells and their surrounding matrix. For instance, a review of various experimental approaches has summarized the techniques developed to characterize forces at the cellular and subcellular levels, emphasizing the importance of mechanical regulation in cell-matrix interactions4. Moreover, a holistic model for cell motility in 3D environments has been proposed, focusing on the mechanical cues from the extracellular matrix and their impact on cell migration and invasion. This model considers the bi-directional interactions between the cell and its microenvironment, including the cytoskeleton and nucleus10 (Mierke, 2020). Mierke (2020) found that electrospun fibrous gel matrix models offer significant advancements over traditional extracellular matrix models by allowing independent control of matrix properties. In contrast to the interconnected variation in traditional models-where alterations in concentration also affect pore size and elasticity—electrospun models enable more precise tunability. By using photocross-linking techniques, elasticity and porosity can be adjusted independently, leading to more customizable environments for cellular interaction. Moreover, matrix degradation can be selectively controlled by employing a mixture of degradable and non-degradable fibers, adding another layer of flexibility to the system. This innovation in matrix modeling provides a more adaptable framework for tissue engineering and biomedical applications, where independent control over matrix properties is crucial for replicating complex biological environments. These advances enhance the ability to mimic in vivo conditions more accurately, promoting better research outcomes in cellular and tissue dynamics. 4.3 Insights into cellular force generation The generation of forces within cells is a critical aspect of cellular mechanics. Recent advances in biophysical models have provided insights into how cells generate and respond to mechanical forces. For example, a contraction-reaction-diffusion model has been developed to integrate biomechanics and biochemistry in cell migration, showing how cytoskeleton contraction generates distributed forces for mechanosensing and signaling (Marzban et al., 2019). Additionally, a high-resolution computational mechanics cell model has been used to study the forces exerted by cells during tissue regeneration, providing a quantitative understanding of the impact of cell-biomechanical effects on tissue organization6. These models highlight the complex interplay between mechanical forces and cellular behavior, offering new perspectives on cellular force generation and its implications for health and disease (Liedekerke et al., 2019). 5 Experimental Validation and Computational Techniques 5.1 Experimental methods for model validation Experimental methods play a crucial role in validating biophysical models in cellular mechanics. Techniques such as micropipette aspiration have been extensively used to study the mechanical properties of cells. This method allows for the precise measurement of cellular responses to mechanical stress, providing valuable data for validating computational models (Gravett et al., 2021). Additionally, advanced imaging techniques have been employed to observe the dynamics of cytoskeletal molecular motors, offering insights into their mechanical operations within cells. These experimental approaches are essential for ensuring the accuracy and reliability of computational models in cellular biomechanics (Guo et al., 2023). 5.2 Computational tools and simulations Computational tools and simulations have become indispensable in the study of cellular mechanics. Molecular dynamics (MD) simulations (Sinha et al., 2023), for instance, have been widely used to investigate the structure and function of biomembranes, providing atomic-level details that are often challenging to obtain experimentally. Similarly, deep Markov state modeling has emerged as a powerful technique for analyzing the long-timescale behavior of complex systems, such as proteins, by incorporating experimental data restraints to improve model
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