Exploration of Fundamental Principle of Robustness and Adaptability of Biological Functional Systems

为了揭示“复杂系统如何适应环境变化并在热波动世界中稳健地提供功能”的问题，在新的数据驱动方法的基础上，提出了两种新的方法：一种是从噪声以任意概率分布函数表示的噪声单分子时间序列中提取潜在物理量；另一种是量化多变量之间的高阶相关性和识别不可约信息理论模块结构。我们发现，单分子生物物理学(酶反应)最常用的数据分析导致了物理上的错误解释。

To unveil the question of "how can complex systems adapt to the environmental changes and provide functions robustly in a thermal fluctuating world" on the basis of novel data-driven approach, two novel methods are developed : The one can extract the underlying physical quantity from a noisy single molecule time series whose noise is represented in terms of any probability distribution function, and the other can quantify higher order correlation among multivariate variables and identify irreducible information theoretic modular structure. We showed that the most conventionally used data analysis for single molecule biophysics(enzymatic reactions) led us to a physically incorrect interpretation.