Scientists from the University of Michigan proposed using principles from control and observability theory to analyze biological systems in a new way. This method will allow for more accurate and cheaper identification of biomarkers—the key indicators of diseases.
The use of mathematical models from control and observability theory may change the approach to finding disease biomarkers. This is the conclusion reached by University of Michigan scientists, whose work was published in the Proceedings of the National Academy of Sciences, as reported by Phys.org.
The researchers proposed applying fundamental engineering principles, developed back in the 1960s, to biology to analyze complex systems in the body. According to Professor Indika Rajapakse, control theory describes how a system can be guided to a desired state by altering input parameters—similar to how a cell changes functions under the influence of external factors.
The second concept—observability—helps determine how many indicators need to be tracked to fully understand the state of a biological system. This allows scientists to identify the minimum number of biomarkers that provide a complete picture of the processes within a cell.
“Our goal is to find the minimal set of variables that, by observing them, we can infer the whole system,” explains Rajapakse. “This will allow us to focus only on the data that provides the highest informativeness, reducing the cost and complexity of research.”
In their experiments, the team applied a new method, Dynamic Data Selection (DDS), to analyze time-series transcriptomic data related to the cell cycle, pesticide action, and cell reprogramming. The scientists proved that even a reduced set of indicators can accurately reflect the system’s behavior.
The authors are confident that this engineering approach to biology will open new possibilities for early diagnosis, optimization of biomedical experiments, and the development of more effective treatment methods.
“If we can track a key biomarker and see its atypical change, this can signal the onset of disease. In the future, this will allow intervention at the earliest stages and prevent the development of illnesses,” noted Rajapakse.
