Emerging Computational Model to Accelerate Therapeutic Applications
Release Date: 18-Nov-2020
Now-a-days, cellular therapy is considered as a very promising and powerful treatment strategy against different types of disease but one of the major challenges that is found in the cellular therapy is keeping the cells alive in the lab conditions. But a group of researchers have focused towards developing an algorithm that could predict the lab conditions required by molecules. The computational approach developed by the researchers is called as EpiMogrify. In the current time, computational therapy is considered as one of the most rapidly blooming market as it delivers several time-consuming methods.
The model developed by the researchers is able to identify important molecules that are needed by the cells such as astrocytes, and heart cells. Researchers involved in the respective study are paving the road of cell therapy and regenerative medicines so that their expertise in the computational models could provide benefit to the other researchers and ultimately lead to the development of therapies or treatments that are beneficial.
For the successful accomplishment of the study, researchers added all the important information into their model about genes tagged with epigenetic markers whose presence indicates that a gene is important for cell identity. Further model leads to the detection of the genes that are important and coding for the necessary proteins. In addition to it, the computational model also is helpful in determining all the data that is important with respect to binding of the protein to the receptors and their respective activities.
Overall, the introduction of computational approach is witnessed to be optimizing lab conditions for generating high-end therapeutic applications from the cells. The researchers involved in the respective study have already filed a patent for their developed approach. The researchers aim to provide all the data available and generated through the model to other researchers so that global pharmaceutical industry could be boosted.
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