Mathematics adds to understanding human disease
Published in the Proceedings of the Royal Society today, the modelling system was originally developed by engineers to study complex man-made systems. Now this approach is being applied to biological systems such as the cells of the human body. Mathematical models based on the underlying science are used in computer simulations to test ideas and to suggest new experiments.
Lead author, Professorial Fellow, Peter Gawthrop from the Systems Biology Laboratory is leading a revolution in a systems biology approach to understanding the flow of energy through the cells in our bodies.
He believes there is increasing scientific and medical interest in understanding how the human body generates, transports and uses energy in sickness and in health.
“Research in Systems Biology uses mathematical and computer modelling to investigate processes and pathways underlying complex human diseases,” says Professor Edmund Crampin, the Rowden White Chair in Systems Biology and Director of the Systems Biology Laboratory, and a collaborator on this paper.
“Understanding how a cell works, for example, requires combining information from many different domains – chemical, electrical, mechanical. Working out how these different aspects of cells interact is a challenge. Predicting what happens if the system is changed such as in disease, or through biotechnology is even harder,” he said.
The paper develops a framework for understanding cell function, which tracks the flow of energy through a cell’s network of biochemical reactions. This allows researchers to effectively combine different aspects of the cell within the same unifying mathematical description.
Work from the Systems Biology Laboratory is leading us closer to solving problems about disease.
“The work from this lab aims to find out what goes wrong in cells and what happens to cause cellular changes; the very fundamentals of biology,” Professor Gawthrop said.
“Ultimately we think that our approach will lead to the ability to more easily and reliably modify biological systems with predictable outcomes – so that we can better understand and then treat disease, and ultimately so that we can design new biological systems for biotechnological and biomedical applications.”