Monte Carlo Overview

Monte Carlo - Overview

This Monte Carlo simulation
of a 71 base pair DNA molecule demonstrates phosphate-phosphate
repulsion. The strongest
repulsion is in the reddest
areas. It is believed that
this interaction accounts for
30% of the energy required
to bend DNA into the folded
form found in a cell nucleus.

When we build a model--a representation of some system or phenomena that we're interested in--we must find data to test the model in a simulation. At times, though, the data is not available or is too difficult or expensive to acquire. If the data is expected to be random, the Monte Carlo method is a well-established technique for generating data for the model. A simple explanation of how and why the Monte Carlo method works is here.

One of the most useful application of Monte Carlo simulation is for "random walks." This finds use in many fields, from analyzing price movement in the stock market to modelling the movement of molecules in a solution. The Monte Carlo method provides the random data for these kinds of applications. A prime example of a random walk is Brownian motion.