Collaborations between Duke-NUS Medical School (Duke-NUS), the University of Bristol, Monash University and RIKEN have led to the surface of an algorithm that can predict the factors necessary for human cell conversion. Cell types are not constant, and may be reprogrammed or transformed into another cell type through the addition of a unique set of cellular factors.
Collaborations between Duke-NUS Medical School (Duke-NUS), the University of Bristol, Monash University, and RIKEN, led to the surface of an algorithm that can predict the factors necessary for human cell conversion.
Cell types are not constant and may be reprogrammed or transformed into another cell type through the addition of a unique set of cellular factors.
However, the two-step conversion carries the risk of cancerous mutations, leading to unpredictable behaviour. Moreover, determining the unique set of cellular factors needed for each cell conversion is a long and costly process involving much trial and error.
Therefore, after five years of research, Duke-NUS Research Fellow Dr. Owen Rackham developed a computational data-driven algorithm named Mogrify.
Making use of a database containing gene expressions from an estimated 300 different human cell and tissue types, this innovative method is able to predict the optimal set of cellular factors required for any given cell conversion.
When experimented with, the algorithm passed with flying colours, accurately forecasting the set of cellular factors essential for previously documented cell conversions. Moreover, it also successfully predicted the outcome of two human cell conversions which have not been carried out in the past.
“Mogrify acts like a ‘world atlas’ for the cell and allows us to map out new territories in cell conversions in humans,” explained Dr Rackham, who is from the Systems Genetics of Complex Disease Laboratory (SGCDL) in the Centre for Computational Biology at Duke-NUS.
One of the first clinical applications the team aims to achieve would be to reprogrammed patients’ defective cells into functioning healthy cells, without the immediate iPS step.
“Mogrify leverages big data and systems biology, and can be expanded into clinical applications. Its robustness and accuracy will continue to improve as more data are input into the framework,” Associate Professor Enrico Petretto, co-author of the study and head of SGCDL added.
In time to come, the team at Duke-NUS plans to apply Mogrify to translational medicine. Collaborative efforts between research groups within Duke-NUS have also been established to use the scientific method to develop treatments for specific diseases, such as cancer.
Image from Nature Genetics & Rackham et al
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