Computational Molecular Biology 2024, Vol.14, No.3, 125-133 http://bioscipublisher.com/index.php/cmb 129 accuracy. These computational approaches enable researchers to explore the intricate details of cellular components and their interactions, facilitating a deeper understanding of cellular mechanics. 5.3 Integration of experimental and computational approaches The integration of experimental and computational approaches is pivotal for advancing our understanding of cellular mechanics. By combining experimental data with computational models, researchers can achieve a more comprehensive and accurate representation of cellular processes. For example, the integration of biophysical experiments with biomolecular simulations has led to significant advancements in understanding the function of biomolecules at an atomic level (Figure 1) (Bottaro and Lindorff-Larsen, 2018). This synergistic approach allows for the refinement of simulations based on experimental observations and vice versa, leading to more robust and predictive models. Furthermore, the use of computational biophysics to study macromolecular machines acting on genes exemplifies the power of combining structural and biophysical experiments with advanced computational methods to uncover the mechanisms underlying fundamental biological processes. This integrated approach is essential for bridging the gap between experimental observations and theoretical models, ultimately enhancing our ability to decipher the complexities of cellular mechanics. In summary, the combination of experimental methods and computational tools is crucial for validating and advancing biophysical models in cellular mechanics. The integration of these approaches provides a more holistic understanding of cellular processes, paving the way for future discoveries and innovations in the field (Mardt and Noé, 2021). Bottaro and Lindorff-Larsen (2018) found that integrating molecular simulations with experimental data allows for a more comprehensive understanding of biomolecular processes by leveraging both forward and inverse modeling approaches. Forward modeling uses molecular simulations to predict system behaviors, which are then compared against experimental results. Meanwhile, solving inverse problems helps elucidate the underlying factors leading to observed phenomena. The combination of quantum mechanical models, molecular mechanics, and coarse-grained simulations enables the study of biomolecules at various spatial and temporal resolutions, with progressively reduced computational complexity. As these computational methods become more sophisticated, they offer insights into thermodynamics and kinetics that require fewer experimental inputs. This synergy between simulation and experimentation is particularly valuable for probing complex biological systems and understanding their dynamic behaviors on a deeper level, significantly enhancing the precision of biomolecular modeling and reducing experimental limitations. 6 Challenges in Modeling Cellular Mechanics 6.1 Complexity of cellular processes Modeling cellular mechanics is inherently complex due to the multifaceted nature of cellular processes. Cells are composed of various components, including the cytoskeleton, cell membrane, and nucleus, each contributing to the overall mechanical behavior. The interactions between these components and their response to external stimuli add layers of complexity. For instance, the cytoskeleton's elasticity, membrane tension, and cell-substrate adhesion are crucial for cellular functions such as migration and differentiation, but these interactions are challenging to model accurately. Additionally, the dynamic nature of cellular processes, such as mechanotransduction and cell signaling, further complicates the development of comprehensive models (Stirnemann, 2022). 6.2 Limitations of current models Despite significant advancements, current models of cellular mechanics face several limitations. One major challenge is the accurate representation of the heterogeneous and dynamic nature of cell membranes. While molecular dynamics simulations have provided insights into membrane organization, they often fall short in capturing the full complexity of multicomponent systems. Moreover, many models struggle to integrate the mechanical properties of subcellular components with the overall cellular mechanics, leading to incomplete or oversimplified representations. Another limitation is the difficulty in directly probing nuclear mechanics, which plays a crucial role in cellular behavior but remains challenging to measure and model (Stirnemann, 2022).
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