Describing the Unknown: A Spotlight on Computational Research

Describing the Unknown: A Spotlight on Computational Research By Camilla Li

Imagine you have a cup of water. In a lab, you can measure things like the temperature of the water. Now imagine if you can go in and measure the motion of individual water molecules. That is the power of a simulation.

6:30 AM, Oxford, Georgia. The sun had yet to rise over the small, sleepy town, but in the seemingly far-removed world of Oxford College, a light flickered on in the office of Dr. Alfred Farris just as the last stragglers filed from the library. Dr. Farris is an assistant professor of physics whose research curiously straddles the disciplines of computer science, physics, and biochemistry. When asked why computers are relevant in modern scientific research, he had a simple scenario in mind: Imagine you have a cup of water. In a lab, you can measure things like the temperature of the water. Now imagine if you can go in and measure the motion of individual water molecules. That is the power of a simulation. Historically speaking, it wasn’t until very recently that programmable machines were made capable of carrying out these tasks. With the advent of the Information Age, however, it is little wonder that the application of computations and simulations has worked its way into scientific research, establishing itself as yet another pillar in modern science. The idea of research in the natural sciences traditionally conjures images of white-coated scientists bent over fancy lab equipment or Sheldon Cooper scribbling equations on a white board. Somewhere along the lines, computational research emerged as an intermediate between the well-established realms of experiment and theory: an invaluable Option 3. “The one thing that’s interesting that I think takes a while to figure out is that there’s a difference between a computation and a simulation,” Dr. Farris made sure to clarify. “A computation is a calculation with a defined answer, [whereas] in a simulation, you are running experiments in the computer.” One of his projects involves studying the biological process of protein folding using a coarsegrained model, or a model with simplifications, analyzed using statistical mechanics. A problem sometimes with coarse-grained models, especially when modeling complex processes like protein-folding, is determining how much detail is necessary to in

Dr. Alfred Farris, Oxford College Physics professor

Dr. Simbarashe Nkomo, Oxford College Chemistry professor

clude. Models were compared for sequences mapped from a small plant protein, Crambin. “The idea is, you have all your theory grounded in physics, [in this case] statistical mechanics, and you use computers to solve problems inspired by biochemistry,” he explained. “What drew me to this kind of research is [how] the methods that are applied are not only applicable to physical systems, but models in other disciplines, like financial systems. The methodology is very general. It’s very interdisciplinary. Everyone has large data sets they want to analyze, and so they need certain analysis tools.” And it certainly is very interdisciplinary. Right next door is the office of Dr. Simbarashe Nkomo, an assistant professor of chemistry whose research focus includes simulations in chemical kinetics and quantum calculations. “When I was doing undergraduate research, I was doing a math project,” Dr. Nkomo recalled. “I ended up using chemical equations for the math problem that I was working on, and I realized that there was this connection between mathematics and chemistry.” For Dr. Nkomo, all of his projects are directly tied to experimental work. The quantum calculations are done in collaboration with chemist Dr. Reza Saadein, who performs the synthesis in the lab. From there, calculations are done by Dr. Nkomo to try to understand why the reaction is going a certain way. In simulating chemical kinetics, mathematics are used as a tool to gain insight into reactions that may be harder to carry out. These simulations are used to make predictions before the experiment is performed in the lab, where experimental analysis involves the image processing of the intensity of observed color changes. For this project, networks

"You are creating a model to mimic reality, but when nobody really understands what that reality is, it can get difficult."

- Dr. Farris

“There are reactions for which people are still trying to come up with the mechanisms,”

- Dr. Nkomo

"Sometimes, you don't know what you're looking for, but this applies to all sciences"

- Dr. Farris

of three diffusively-coupled chemical oscillators are studied, with the hopes of eventually applying what is learned from these studies to bigger networks. “There are reactions for which people are still trying to come up with the mechanisms,” he explained. “I come up with models that can describe the mechanisms that have been proposed. If my model gives me a good representation of the things I see in my experiment, then I can now use the model to make predictions. I want to be in touch with what should happen experimentally, [because] when you are solving equations from a mathematical point of view, an [unviable] answer could seem acceptable without context for which system you are representing.” Dr. Farris also addressed the limitations of computational research. “Sometimes, you don’t know what you’re looking for, but this applies to all science. For example, nobody knows why proteins fold. There are some generic behaviors that are common in all proteins, and some that disobey the generic behaviors. When you start setting these things up in a simulation, you sometimes don’t know what the questions are and what the answers are, and are on your own when figuring out what is reasonable. You are creating a model to mimic reality, but when nobody really understands what that reality is, it can get difficult. “You’re somewhere in between theory and experiment. So the models involve theory, but the simulations themselves are basically individual experiments, and it’s not obvious if what you’re doing is correct, because you have nothing to test what you’re doing against. As computers become more powerful, [this kind of research] will keep taking off. But you will also need experiment and theory.”

Nevertheless, he assured me that in spite of the potential shortcomings, there is much to look forward to in conducting computational research. Of course, there are many mistakes that can be made with programming and model design, and from his experience, it takes time to eventually get to the point where you are able to look at code that you’ve written and say that the code is working correctly. When there is an error, and you can be confident that the code is working correctly, then the error you see must either be an error with the model design, or the results are correct and you just don’t understand what’s going on in terms of your interpretation of reality. “The fun is when you get something that’s unexpected. If your results are weird and you know your model’s realistic, then you have new physics. You have something new at work.” According to Dr. Nkomo, the field of computational chemistry has grown so prominent that there are computational chemists winning nobel prizes, and computational techniques are now applied in areas like drug design. The chemistry curriculum has also adapted to this changing research environment, by offering a course on machine learning in chemistry. I was fascinated by the methodologies and prospects of computational research, and wanted to see for myself, so Dr. Farris graciously agreed to show me some basic research techniques. I was to replicate a simple Monte Carlo simulation modeling the behavior of a ferromagnet. Barring some initial frustrations and mistakes made along the way, it was remarkable how much the simulations, though written in code and performed by the computer, lined up with my preconceived notions of what a scientific experiment should be. In my experience, the bottom-up approach of the simulation, starting microscopically to say something about the system on a macroscopic level, definitely contrasts with the top-down approach common with many experiments in the lab that I am familiar with. It was the same scientific thinking but from a different point of view—a different way of describing the unknown, if you will. “I think it works this time,” I said finally, showing him what I had been working on. “I think I fixed it.” I pressed run. The simulation came to life. And so we waited. “The fun is when you get something that’s unexpected. If your results are weird and you know your model’s realistic, then you have new physics. You have something new at work.”

Camilla Li is a sophomore tentatively majoring in Playwriting and Physics. In the previous year, she worked with faculty and a few other students to help organize an interdisciplinary project in the Atlanta Science Festival, combining her passions in the arts and natural sciences. She is currently a lab teaching assistant for an introductory physics course, and is looking to get involved in both creative projects and research that would bridge her interests in physics and the biological sciences. In her free time, she enjoys baking, painting, and singing musical theater showtunes.