, may not have a crystal ball to predict the future, but that’s precisely what he’s trying to accomplish in his computer science lab in the Virginia Commonwealth University School of Engineering.

“We’re working to understand the properties of biological networks and to make predictions based on those properties. The predictions in gene regulatory networks and protein interaction networks, for example, can play a significant role in healthcare in general,” he said. “This work allows us to look at complete networks and how they behave, which can impact all types of disease research and drug discovery.”

Ghosh and his students primarily focus on algorithm development, modeling, simulation, analysis and visualization of large-scale networks with an emphasis on complex biological networks.

Some ongoing projects involve protein aggregation simulation involved in Alzheimer’s disease, RNA interference pathway simulation, influenza lifecycle simulation, reverse engineering algorithms to infer genome-scale context-specific regulatory networks, and analysis and visualization schemes to study their time-varying properties.

“Our influenza research examines the virus’ life cycle,” Ghosh explains. “How does it interact with cells of healthy patients? We’re creating a computer-based model which tries to mimic what happens so that we can predict the ‘if, then’ scenarios. This knowledge can then applied in the treatment of influenza. We know the drugs, but the problem is control. How often to dose? And at what intervals?”

In his Alzheimer’s work, Ghosh is studying protein aggregation, which leads to plaques on the brain. “These plaques are responsible for killing some of the neurons in the brain. So we’re trying, based on our computer models, to determine how many of these protein molecules are being formed. Next, we can tell how many plaques are being formed. If we can stop the plaque formation using our predictability studies for use in drug discovery, we can help determine proper dosing in the future.”

The various processes of disease have been known in isolation, Ghosh explains. But by examining complete biological networks, researchers can begin to understand the properties behind diseases. Once the properties are known and computer science helps predict the outcome of possible scenarios, treatment becomes faster and more cost efficient.

“We’re working to understand the properties of biological networks and to make predictions based on those properties. The predictions in gene regulatory networks and protein interaction networks, for example, can play a significant role in healthcare in general.”

– Preetam Ghosh, Ph.D.