解讀體外與活細胞蛋白之單分子運動軌跡的生物學相關過程

Abstract

一般細胞的生理系統結構與其功能表現大多屬於多尺度物理系統的範疇；包括小至氫鍵，大至多蛋白組成的生化系統。生物系統能夠可靠地執行生化功能是維持穩定生長與分化的先決條件，近期研究指出執行生化功能的動態過程中存在隨機過程微擾其能量圖景（energy landscape）的現象。系統動態發展深受周圍環境隨機變化的影響，生物系統在隨機雜訊干擾下如何展現可靠生化功能？ 為了系統性研究此議題，本論文將待研究的相關生物課題由小至大分成三個層次：（1）蛋白上氫鍵網絡如何影響蛋白動態、（2）蛋白形態改變（conformational changes）如何影響蛋白功能以及（3）蛋白間交互作用的重要性。本研究利用單分子顯微鏡記錄單分子結構與運動狀態，萃取受到環境影響的動態行為，提出理論模型深入解析生物系統運作的基本原理。本論文將介紹建構單分子螢光顯微鏡的方法與如何分析單分子量測數據的數學分析技術，並將單分子量測技術與理論模型整合於不同尺度的生物學相關議題，期能提供新視野，有助於深入了解系統動態的基本機制。

The composition of a biological system contains a wide scale of components, ranging from hydrogen bonds to complex protein-protein interaction networks. A biological system can robustly perform biochemical functions to accomplish stable growth and differentiation. Recent research has indicated that the energy landscapes of local transitions in a biological system confront with several random perturbations, which is referred to as "intrinsic noise". The complex interactions between a biological system and its surroundings influence the system's dynamics. However, how a biological system robustly performs biochemical functions under such random perturbations remains unclear. To address this question, in this thesis study, the complex interactions of a biological system were divided into three levels of research: (1) the influence of hydrogen-bonding networks on protein dynamics; (2) the influence of conformational changes on the function of a protein; and (3) the effect of protein-protein interaction on cellular signal transduction. To investigate the influence of intrinsic noise on the dynamics of a biological system, we employed single-molecule microscopy to record protein dynamics in a biological system. We developed a theoretical model for deciphering the underlying mechanism that generates the dynamical processes recorded on the trajectories of a reporting protein. In summary, this thesis study aims to offer some useful guidelines for the implementation of single-molecule microscopy methodology accompanying with mathematical tools needed for analyzing datasets of single-molecule trajectories. The integration of single-molecule experimental techniques and theoretical models entails unravelling the hidden dynamics of a biological system and providing an in-depth mechanistic understanding of biological processes under varying environmental perturbations.