Jerome P. Nilmeier, a

biophysicist working in computational biology, is willing to bet his new

research will provide a breakthrough in the use of the Monte

Carlo probability code in biological simulations.

Working with Gavin E.

Crooks at Lawrence Berkeley National Laboratory, David D. L. Minh at Argonne

National Laboratory, and John D. Chodera, from the University

of California, Berkeley,

Nilmeier has coauthored a paper that introduces a new class of Monte Carlo moves based on nonequilibrium dynamics. The

paper appears in the *Proceedings of the National Academy
of Sciences.*

The Monte

Carlo technique is one of the most widely used methods to model a

system and determine the odds for a variety of different outcomes. The

technique was first developed by scientists working on the Manhattan Project

who needed to figure out how far neutrons might pass through a variety of

different types of shielding materials.

The Monte

Carlo technique harnesses the power of computers to figure out the

probable outcomes of equations that have hundreds or thousands of variables. It

is a short cut that, instead of giving a definitive answer, gives a probable

answer. Random numbers are put into the equation, the outcome is tested, the

probability for the different outcomes is determined and then a decision can be

made about what is the most likely outcome.

To help explain the

impact of the new Monte Carlo technique,

Nilmeier uses the example of a chemical compound composed to two identical or

similar subunits, what’s known as a dimer.

“The dimer model

that we studied is a good proxy for a reactive system where molecules are

allowed to collide and form new molecules and also to dissociate into free

atoms,” Nilmeier says. “Using the new technique, we can bias our

simulation to sample the collision event more frequently and obtain better

statistics. In our paper, we report an order of magnitude improvement, but we

can easily imagine systems that would have several orders of magnitude in

improvement.”

One of the important

keys to the Monte Carlo technique is how it

goes about making guesses. Different types of systems have different types of

variables with different types of relationships. What works well for measuring

how far a neutron will pass through different type of radiation shielding

doesn’t work well at all when it’s applied to a biological system where there

are millions of molecules all moving very short distances very rapidly in many

different directions. What Nilmeier and his colleagues figured out is a new

method to apply the Monte Carlo technique to

these types of problems.

“My colleagues and

I were pointed to this new method in 2008. We were all at the annual Berkeley

Mini Stat Mech Meeting when we noticed new theorems that could be applied to

biology,” says Nilmeier. “Following the meeting, we started to

work.”

Nilmeier currently works

on a team led by Carol E. Zhou of the Global Security Computing Applications

Division and Felice Lightstone, the group leader of the biosciences and

biotechnology division.