An international team of researchers has developed advanced computer models, or “digital twins,” of diseases that can identify dynamic genome- and cellulome-wide, disease-associated changes in cells across time. Developed with the goal of improving diagnosis and treatment, the research, published in Genome Medicine, underlines the complexity of disease and the necessity of using the right treatment at the right time. The scientists, headed by Mikael Benson, PhD, at Linköping University, and Karolinska Institutet, reported on the development of one model to identify the most important disease protein in hay fever.
In their published paper, titled, “A dynamic single cell‑based framework for digital twins to prioritize disease genes and drug targets,” the investigators concluded, “We propose that our framework allows organization and prioritization of UR [upstream regulator] genes for biomarker and drug discovery. This may have far-reaching clinical implications, including identification of biomarkers for personalized treatment, new drug candidates, and time-dependent personalized prescriptions of drug combinations.”
Continue reading… “Digital Twin Multi Network Models Could Aid Personalized Therapy, Biomarker, and Drug Discovery”
