Using maths to predict the behaviour of cells could lead to new diabetes treatments
State-of-the-art microscopic research into how cells work could lead to new treatments for diabetes.
Using new ‘super-resolution’ microscopy techniques, a team of researchers at Heriot-Watt University in Edinburgh mapped the positions of hundreds of thousands of molecules within cells for a study recently published in Current Biology.
The cells the researchers looked at were on a nanoscale, which is roughly as small to humans as Jupiter is large to us.
The research team collected molecular results, known as ‘big-data’ due to the large amount of information generated, which provided a bulk of information to allow for detailed mathematical analysis.
Professor Rory Duncan, Head of the Institute for Biological Chemistry, Biophysics and Bioengineering at Heriot-Watt University worked with Professor Gabriel Lord from the University’s School of Mathematical and Computer Sciences on the study to challenge a long-held theory about the way cells behave on the nanoscale.
The researchers believe their findings could revolutionise future research into diabetes and neurological (nervous system) treatments.
Previous research had led biologists to believe that cells that discharge hormones, like insulin, or neurotransmitters, like serotonin, package their cargo in vesicles (small bubble-like fluid-filled sacs within the cells) with controlled movement, following the same paths to similar places in the cells, similar to a train on a railroad.
These specific tracks could not be viewed even by the world’s most powerful microscopes, however biologists were convinced they were there due to what they had seen in the movement of cells and behaviour of the vesicles. Clusters of nano-sized molecules inside cells were believed to overlap with the vesicles but this theory has now been challenged using the latest mathematical modelling technology.
Professor Lord said: “One of Heriot-Watt’s strengths is cross-disciplinary working. Together with our colleagues in the biology department, we approached how these cells behave in a radically new way, asking what if the vesicles do not follow paths to special molecular depots inside cells, but instead avoid the cluster of molecules, like a boulder does when following a valley between mountains?
“Working with the biology team and combining microscopy information from diverse experiments into a ‘mathematical model,’ we were able to run multiple experiments on a computer that aren’t possible in the real world.”
Professor Rory Duncan added: “In contrast to what biology theorised, vesicles actually avoid areas in the cell previously thought to attract them, following ‘valleys’ in between groups of the specific molecules known to drive secretion. The net result to the observer is the same – vesicles re-use similar routes and move to nearly identical places in the cell, but the mechanism is the opposite of previous thinking, and the physical tracks do not exist.
“Our new approach allows us to run experiments on the computer and our resulting model predicts how the vesicles and molecules behave in cells, particularly if they are disrupted or mutated, as happens in disease states.
“This predictive ability is powerful because it tells biologists which molecules to target in future studies and lays the way for larger and more thorough modelling of complex biological processes.
“These findings have wide-reaching possibilities for studying cellular dynamics. For example, when something goes wrong with the transport of neurotransmitters in these vesicles, it leads to a variety of neurological disorders. We don’t yet know what goes wrong but now we are starting to understand how cells behave at a molecular level, science may be able to make breakthroughs for conditions like epilepsy or diabetes.
“The integration of cutting-edge microscopy, with cell biology and mathematical modelling, could be applied to many other problems in biomedicine and will accelerate discovery in the years to come.”
Read the report on this study in Current Biology
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