Researchers are using AI to dig ever further into individual cells with the goal of developing insights more quickly into the workings of the human body’s major organs.
At the University of Illinois Chicago, scientists have introduced a new system that uses a machine learning algorithm and predictive analytics to predict which “transcription factors” are most likely to be active in individual cells.
As a University press release recently explained, transcription factors are proteins that bind to DNA and control what genes are turned "on" or "off" inside a cell. Understanding and manipulating these signals in a cell is crucial to the biomedical field because this method of manipulating signals within a cell has proven to be an effective way to discover new treatments and illnesses.
There are, however, hundreds of transcription factors inside a human cell, and it could take years of research, and lots of trial and error, to determine the most active factor.
"One of the challenges in the field is that the same genes may be turned ‘on’ in one group of cells but turned ‘off’ in a different group of cells within the same organ," Jalees Rehman, UIC professor in the department of medicine and the department of pharmacology and regenerative medicine at the College of Medicine, said in the release. "Being able to understand the activity of transcription factors in individual cells would allow researchers to study activity profiles in all the major cell types of major organs such as the heart, brain or lungs.
The system developed is named BITFAM, for Bayesian Inference Transcription Factor Activity Model, and it operates by “combining new gene expression profile data gathered from single cell RNA sequencing with existing biological data on transcription factor target genes.”
With this information, the system runs an array of computer-based simulations to find the best fit and predict the activity for every transcription factor in the cell. The system was tested on cells from tissue in the lung, heart, and brain by Rehman and fellow UIC researcher Yang Dai, UIC associate professor in the department of bioengineering at the College of Medicine and the College of Engineering.
"Our approach not only identifies meaningful transcription factor activities but also provides valuable insights into underlying transcription factor regulatory mechanisms," said Shang Gao, first author of the study and a doctoral student in the department of bioengineering. “By providing data like this for every transcription factor in the cell, the model can give researchers a good idea of which ones to look at first when exploring new drug targets to work on that type of cell.”
Rehman explained the application relevant to his lab is to use the new machine learning algorithm system to focus on factors that increase disease in certain cells.
“For example, we would like to understand if there is transcription factor activity that distinguished a healthy immune cell response from an unhealthy one, as in the case of conditions such as COVID-19, heart disease or Alzheimer's disease where there is often an imbalance between healthy and unhealthy immune responses,” he said.
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