Quantitative Approaches to Cancer and Cellular Differentiation
MetadataShow full item record
CitationFerlic, Jeremy. 2019. Quantitative Approaches to Cancer and Cellular Differentiation. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
AbstractIn the past decade, advances in computational power have expanded the potential to analyze vastly complex systems. While this explosion of research has made great contributions in the fields of biology and human health, there remains a need to develop frameworks and tools to provide greater access for general scientists to these new methods. In this work, we describe a framework and two software packages to help further investigate cancer and cellular differentiation.
In the first chapter, we evaluate the effects of implementing screening for the precancerous state monoclonal gammopathy of undetermined significance (MGUS) in the progression to multiple myeloma (MM). Advances in medicine have discovered therapeutic and lifestyle interventions that potentially reduce the risk of progression from MGUS to MM. It remains an open question how best to implement a screening strategy and how to evaluate the effects of the new policy. We model the United States population containing high- and low- risk subgroups and compare screening regimens using MGUS and MM incidence and mortality measures.
Next, in the second chapter we present a computational tool, DIFFpop, an R package to simulate the movement of cells through differentiation hierarchies. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios.
Finally, in the third chapter, we develop a new method and tool to estimate rate parameters for and simulate continuous-time Markov branching processes. The software includes methods to simulate branching processes according to time-dependent rates as well as random distributions of offspring. ESTIpop uses the Gillespie Stochastic Simulation Algorithm with adaptive thinning. Parameter estimation is based on an extension of the Central Limit Theorem applied to multitype branching processes with ancestors of various types. The software is flexible and can be applied to any user-defined multitype branching processes.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42013057
- FAS Theses and Dissertations