A computer neural network model has been trained by a team of bioengineers to foresee how blood platelets respond to intricate conditions found at the time of a heart attack or stroke.
With the help of computerized robotic system, the researchers at the Institute for Medicine and Engineering, University of Pennsylvania exposed human blood platelets to hundreds of different combinations of biological stimuli like those experienced during a heart attack.
After the experiment, it was discovered that the complexity of integrating numerous signals could be built up from the responses to simpler conditions involving only two stimuli.
"With patient-specific computer models, it is now possible to predict how an individual's platelets would respond to thousands of 'in silico' heart-attack scenarios," Nature quoted Scott L. Diamond, professor of chemical and biomolecular engineering and the director of the Penn Center for Molecular Discovery, as saying.
"With this information we can identify patients at risk of thrombosis or improve upon current forms of anti-platelet therapies, he added"
The team developed its technique, called Pair wise Agonist Scanning, or PAS, to define platelet response to combinations of agonists, chemicals that bind in this case to platelet cells, initiating a cellular response.
The findings of the study were published in Nature Biotechnology.