Traditional methods in biology have proven insufficient for understanding and accurately predicting complex biological systems. Why? The great majority of biologists are trained to study life from the bottom up, as the result of unguided evolutionary processes. It turns out there are better ways to observe, question, hypothesize, experiment, and analyze a complex system. On a new episode of ID the Future, I welcome biochemist and metabolic nutritionist Dr. Emily Reeves to the podcast to discuss her co-authored paper on how biologists can apply principles from systems engineering to biology to better approach the study of complex living systems.
As Dr. Reeves explains, the need for a new methodology in biology is motivated by two key observations. First, biological systems appear to be designed. Zoom into any complex system in biology, such as the bacterial flagellar motor, the light harvesting complex of Photosystem I, or ATP synthase, and you’ll find exquisite nanotechnology that is better engineered than its human-engineered counterpart. Second, biological systems have already been demonstrated to have much in common with human engineered systems. Biological systems are hierarchical, integrated, modular, optimized, and robust. These are all characteristics of top-down designed systems. “Therefore,” explains Dr. Reeves, “the tools that engineers use to makes these systems can be adapted to better understand biology.”
In addition to explaining how the new methodology operates, Dr. Reeves shows how it can be applied to various systems and phenomena to produce fruitful scientific research. As a case study, she describes how to use the methodology to better understand the commonly studied process of glycolysis. She also highlights the implications of this approach for understanding phenomena such as the Warburg effect, a proposal that seeks to explain the metabolic requirements of cell proliferation in many types of cancer. Dr. Reeves notes that a systems engineering approach to the Warburg effect suggests a different reason, one that has not yet been widely studied or reported in the scientific literature. Download the podcast or listen to it here.
