Turning 1s and 0s Into Hope

Working alongside doctors and biologists armed with laser beams and microscopes, UNM researchers Shuang Luan and Terran Lane wield complex computer codes of 1s and 0s to fight disease. Luan and Lane, both assistant professors of Computer Science, study bioinformatics, the use of computers, software, and databases to solve biological questions.

Luan is interested in the interface between computer science and radiation oncology. "You need committed collaborators in medicine and the background in the theory of computer science to design algorithms to solve these problems," explains Luan. "We have both here at UNM. And our department makes interdisciplinary research a priority." Collaborating with researchers at UNM and across the country, Luan is working on three cancer-related bioinformatics projects.

New Dimensions in Cancer Treatment

Luan helped develop two software programs to improve linear accelerator-based (LINAC) cancer treatment. LINAC, the most common radiation device, uses a precisely positioned beam to irradiate cancerous tumors. Multileaf collimators, or moveable "leaves," placed under the beam, shield healthy body parts from the radiation. Accurately positioning the beam and the leaves is challenging when the tumor is located in a moving body part like the lung. Luan collaborated with researchers at University of Maryland, University of Notre Dame, and UNM Radiology to develop new four-dimensional treatment techniques that optimize the leaves and synchronize the angle of the beam with the body's movement. The team used computational geometry and powerful algorithms to create the program.

In a second LINAC project, Luan and researchers at the University of Maryland and University of Notre Dame pioneered another treatment optimization technique. Normally, LINAC machines use up to 13 individual beams of radiation positioned at different angles to treat a tumor. Using computational geometry, the team developed Single ARC Dose Painting Therapy, treatment planning software that moves the beams around the patient, continuously delivering radiation from all possible directions. Luan says results are excellent. "Treatment time can be as much as 80% shorter and we can deliver a higher dose of radiation to the tumor and less to the surrounding structures." The team has filed a provisional patent for the technique.

Gamma knife treatment uses focused beams of radiation to treat brain tumors. Again, using powerful algorithms, Luan and his research partner at University of California at San Francisco developed software that optimizes the beams, turning them into a relative brush that "paints" the contours and volume of the tumor with radiation. The dynamic process covers the tumor faster and more evenly. Ultimately, the software could be incorporated into a treatment planning program. A provisional patent has also been filed for the technique.

Improving cancer treatment is a long way from the theoretical work Luan had planned to pursue. His goals changed the day he saw how his work could heal. "I'll never forget the first day they used my algorithm to treat patients," says Luan. "I was nervous, but it worked. That's when I decided to make it the focus of my career."

Probing RNAi

Like Luan, Terran Lane is using his computer science expertise to conduct bioinformatic research. One focus of Lane's work is ribonucleic acid (RNA), which is closely associated with DNA. While DNA contains the code for cell growth, RNA acts as the "middle man," controlling the flow of genetic information between DNA and the proteins it ultimately produces. Many researchers believe understanding RNA will dramatically improve disease treatment and genomic research.

Recently, biology researchers discovered a cellular process, now called RNA interference (RNAi), that can be used to stop malfunctioning genes from producing bad proteins. Lane is conducting RNAi research with immunologists in the UNM Biology Department and the Center for Evolutionary and Theoretical Immunology. He uses algorithms to create models of chemical probes that connect with RNA and stop the process of protein production. "We're designing these RNAi probes to target specific RNA and disrupt them, while creating as few side effects as possible," explains Lane. The challenge is how to design the short interfering RNA probe (siRNA) so that the probe targets a specific malfunctioning gene or protein without disrupting beneficial ones. The powerful algorithms help the researchers evaluate all the possible variations in the structure of the probe.

It's early in the research, but Lane and his team have made one important find. "Some of the early work in the area assumed that everything was highly specific and you didn't have to worry about side effects," explains Lane. "But we predicted computationally that you're likely to have side effects unless you work to design probes that are free of side effects." The team already developed one algorithm that other RNAi researchers can use to design more effective drugs with fewer side effects. It's clear that with their bioinformatic research already yielding results, Lane and Luan are turning computer codes into hope.