Dr. Peter Swain

Modeling the complexity of cells: When computer science meets biology

Researchers use computational biology – a field of biology that combines physics, computer science, applied mathematics, and statistics – to solve problems in biology by creating models of the signalling networks that exist inside cells. Signalling networks are systems of proteins that interact to relay information from a cell’s external environment to the inside of the cell, a process crucial for a cell to survive. Researchers use models of these networks to help them understand such complex processes. However, creating models is challenging and the models generated thus far can only accommodate a few of the many biological processes that occur.

Dr. Peter Swain, an assistant professor in the department of physiology at McGill University, creates computer models that predict a cell’s response to many external signals. However, a danger in modelling complex systems is that the model can start to become as complex as the system we wish to understand. “How do you analyze the complexity of signalling networks inside a cell?”, Swain asks. To do this, Swain looks at key groups of proteins that interact within signalling networks to perform particular information processing tasks. This approach allows his group to build models of signalling networks that are increasingly complex.

Swain is interested in signalling networks because they activate gene expression. “When we build our model, we know the starting point and the end point from experimental data on bacteria”, he says. In fact, it is possible to determine experimentally maximal and minimal responses to stimuli, but much harder to determine intermediate responses. To circumvent that problem, Swain and his colleagues use probability theory to predict optimum response curves to particular biological stimuli. They then show with their model that signalling networks can activate gene expression in this optimum way. To validate their approach, they compare computer-generated data to the data generated from bacteria.

The network models that Swain and his colleagues build can be used to predict cellular responses to stimuli. Because key interactions found in bacteria occur in other systems, the network models generated from bacteria cells can provide an initial framework to decipher the normal functioning of more complex signalling networks such as those found in mammalian cells. “If we understand how the signalling networks work when cells function correctly, then we will understand what happens when they don’t!”.

Peter Swain, member of the Centre for Nonlinear Dynamics at McGill University, holds a Canada Research Chair in Systems Biology. He is also the team leader of the Mathematics of Information Technology and Complex Systems (MITACS) network, a Network of Centres of Excellence (NCE).

For more information, please visit : www.cnd.mcgill.ca/~swain

Written by: WARM-SPARK writer Sophie-Anne Lamour PhD Student, Dept. Pharmacology

For further information, please contact Dr. Peter Swain using the Email contact form or by phone at 514 398-4360