Computational Molecular Biology 2024, Vol.14, No.3, 125-133 http://bioscipublisher.com/index.php/cmb 130 Figure 1 Simulations and experiments are complementary (Adopted from Bottaro and Lindorff-Larsen, 2018) Image capton: (A) Solving an inverse problem aims to describe causal factors that produce a set of observations. Molecular simulations, conversely, can be used to construct a set of microscopic molecular conformations that can be compared with experimental observations through the use of a forward model. (B) Computational approaches to studying biomolecules range from detailed quantum mechanical models to atomistic molecular mechanics to coarse-grained models, where several atoms are grouped together. The decreased computational complexity granted by progressive coarse-graining makes it possible to access longer time scales and greater length scales. (C) Experimental data can be combined with physical models to provide a thermodynamic and kinetic description of a system. As the model quality improves, it becomes possible to describe more complex phenomena with less experimental data. SANS, small-angle neutron scattering; EPR, electron paramagnetic resonance; FRET, fluorescence resonance energy transfer; DG, Gibb’s free energy (Adopted from Bottaro and Lindorff-Larsen, 2018) 6.3 Scalability and computational challenges Scalability and computational challenges are significant hurdles in modeling cellular mechanics. High-resolution models that capture detailed biophysical interactions require substantial computational resources, making it
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