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Cellular death is a key concept in the study of biology. However, its definition varies depending on the specific situation and does not have a universal mathematical interpretation. Researchers from the University of Tokyo have introduced a new mathematical framework for defining death, focusing on whether a cell that is possibly dead can revert to a specific “representative state of living,” which are the conditions we can reliably identify as “alive.” This research could prove beneficial for biologists and future medical studies.
While it’s a topic we often avoid, death ultimately awaits everyone—animals, plants, and even cells. Although we can usually distinguish between living and dead, it may surprise you that there isn’t a commonly accepted mathematical definition for cellular death. Understanding cellular death is crucial because it plays a vital role in numerous biological activities and can greatly affect health outcomes, making it essential to clarify what cellular death truly means in scientific investigations.
“My long-term aim in science is to mathematically comprehend the fundamental differences between life and nonlife; specifically, why the transition from nonlife to life is so challenging, while reverting back to nonlife seems easier,” explained Assistant Professor Yusuke Himeoka from the Universal Biology Institute. “In this project, we sought to create a mathematical definition and a computational approach to measure the life-death divide. We achieved this by utilizing a significant aspect of biological reaction systems, particularly enzymatic processes inside cells.”
Himeoka and his research team developed a mathematical definition of cell death that hinges on how cellular conditions, including metabolism, can be influenced by adjusting enzyme activities. They categorize dead states as those from which cells cannot revert to an observable “living” condition, regardless of any biochemical manipulations. This led them to create a computational method for measuring the life-death boundary, which they refer to as “stoichiometric rays.” This method is rooted in the study of enzymatic responses and the second law of thermodynamics, which suggests that systems tend to evolve from organized to disorganized states. Researchers can apply these techniques to gain deeper insights into controlling, and potentially reversing, cellular death in laboratory settings.
“However, this computational method cannot be applied to autonomous systems—those systems that generate control mechanisms, like proteins. Autonomy is a defining characteristic of living organisms. I aspire to adapt our method to be relevant for these systems as well,” Himeoka noted. “We often assume that death is irreversible, but this belief is not straightforward and may not always hold true. I am convinced that if we can gain greater control over death, it could fundamentally transform our understanding of life and have significant societal implications. Therefore, comprehending death is critical both in scientific exploration and social contexts.”
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