Hayashi Kumiko, Takamatsu Nobumichi, Takaramoto Shunki
The Institute for Solid State Physics, The University of Tokyo, Kashiwano-Ha 5-1-5, Kashiwa, Chiba 277-8581 Japan.
Biophys Rev. 2024 Oct 2;16(5):571-579. doi: 10.1007/s12551-024-01239-w. eCollection 2024 Oct.
Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fréchet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems. Traditionally, biological research focuses on average values from repeated experiments. However, EVA offers insights into molecular mechanisms by examining extreme data points. We introduce EVA's concepts with simulations and review its use in studying motor protein movements within cells, highlighting the importance of in vivo analysis due to the complex intracellular environment. We suggest EVA as a tool for extracting motor proteins' physical properties in vivo and discuss its potential in other biological systems. While there have been only a few applications of EVA to biological systems, it holds promise for uncovering hidden properties in extreme data, promoting its broader application in life sciences.
极值分析（EVA）是一种统计方法，用于研究数据集极值的特性，这对于工程、气象学、金融学、保险业和环境科学等领域至关重要。EVA使用弗雷歇、威布尔或冈贝尔等分布对极端事件进行建模，有助于风险预测和管理。本综述探讨了EVA在纳米级生物系统中的应用。传统上，生物学研究侧重于重复实验的平均值。然而，EVA通过检查极端数据点来深入了解分子机制。我们通过模拟介绍EVA的概念，并回顾其在研究细胞内运动蛋白运动中的应用，强调由于细胞内环境复杂，体内分析的重要性。我们建议将EVA作为一种在体内提取运动蛋白物理特性的工具，并讨论其在其他生物系统中的潜力。虽然EVA在生物系统中的应用仅有少数几例，但它有望揭示极端数据中的隐藏特性，促进其在生命科学中的更广泛应用。
