Mathematical modelling of the body's biochemical systems


Monday, 07 December, 2015

Researchers from the University of Melbourne have developed an energy-based mathematical modelling technique to build models of the complex biochemical systems within the human body.

The modelling system was originally developed by engineers to study complex manmade systems. Now this approach is being applied by the Melbourne scientists to biological systems, such as the cells of the human body, with the mathematical models based on the underlying science then used in computer simulations to test ideas and to suggest new experiments. Their work has been published in the journal Proceedings of the Royal Society A.

“Research in systems biology uses mathematical and computer modelling to investigate processes and pathways underlying complex human diseases,” said Professor Edmund Crampin, the director of the university’s Systems Biology Laboratory and a collaborator on the study.

“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.”

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.

“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,” said Professorial Fellow Peter Gawthrop, the lead author on the paper.

“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.”

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