2026-09-20T07:40:15Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/421822026-02-16T02:46:55Zcom_1773_4888col_1773_4891

Qian, Hong Wang, Yue 2018-07-31T21:08:56Z 2018-07-31T21:08:56Z 2018-07-31 2018 Wang_washington_0250E_18616.pdf http://hdl.handle.net/1773/42182 Thesis (Ph.D.)--University of Washington, 2018 With the development of experimental apparatus and data processing softwares, one now has easy access to cancer related data on a single cell, its genome and/or molecular compositions. At this level of description, stochasticity is a significant component of the dynamics. Statistics also emerge naturally from stochastic data. In the first part, we study the cancer cell growth data with statistics, and build stochastic models to show that there exists multiple phenotypes in seemingly homogeneous cells. In the second part, we use branching processes to explain the phenomenon that the proportions of different phenotypes of cancer cells will always converge. In the third part, we consider how to quantify the causal effect from a random variable to a response variable. We prove that in special cases quantifying causal effect is impossible. In the fourth part, we consider the lifting of stochastic processes, and prove the convergence of related thermodynamic quantities, so as to explain the origin of entropy production. application/pdf en_US CC BY-NC-ND Cancer biology Causal inference Entropy productions Population dynamics Stochastic processes Applied mathematics Biology Statistics Applied mathematics Some Problems in Stochastic Dynamics and Statistical Analysis of Single-Cell Biology of Cancer Thesis Open Access open.access