As we've discussed in this space before, probably the most fundamental calculation that can be done is a quantum chemistry simulation that treats all electrons in a system explicitly. For many decades, this field has provided experimental validation, fodder for further experiments, and even data for expanded calculations. The obvious limitation here is that the chemical systems that can be studied are limited by the computing power available. Recent advances in computer hardware and algorithm development have helped expand problem sizes on all levels of rigor, but this is still a very real limitation.
Empirical, or semi-empirical, calculations represent a step down the detail scale from full quantum simulations, but a step up in terms of accessible system sizes. Examples of this type of study include molecular dynamics (MD) or Monte Carlo (MC) simulations of systems of molecules, sometimes in a solution with water. The basic simulation here allows the researcher to study several thousand atoms at a time, and is suitable for studying small-to-medium size organic molecules in some detail.
As we attempt to study the dynamics of larger biomolecules, it quickly becomes clear that standard MD or MC calculations can't be carried out for a sufficient length of time. To get at these problems, such as protein folding, special techniques have to be applied to the equations of motion to overcome the time constraints.
Clearly, as we move up the ladder toward more and more complex biological systems, it becomes necessary to introduce approximations to keep the simulations computationally affordable. As the problem shifts to DNA, tissue, and complete organisms, we pass out of the realm of chemical simulations and must bring in methods from other fields of scientific research. Over the next few weeks, we'll examine some projects that attempt to study different biological phenomena, working our way up from the simple to the very
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