Details

Autor(en) / Beteiligte

• Singh, Apeksha Sudha

Titel

Analyzing Molecular and Cellular Dynamics to Interpret Changes in Immune States

Ort / Verlag

ProQuest Dissertations & Theses

Erscheinungsjahr

2024

Quelle

ProQuest Dissertations & Theses A&I

Beschreibungen/Notizen

• Mathematical models can be used to describe how a biological system changes over time and moreover the consequences of a perturbation arising in an abnormal state of that system. Technological advancements in experimentation and growing large-scale datasets drive the need for increased quantitative analysis of biological phenomena. However, a significant effort of data analysis of these datasets prioritizes recapitulating observations and forward predictions without focus on the underlying interpretability of these approaches. Mechanistic models aim to reproduce experimental observations while also providing understanding of the biological processes that generate these observations. In this dissertation, mathematical models were employed to understand how changes in cellular (Chapter 2) and population (Chapter 3) dynamics of hematopoietic cells emerge in different physiological and pathological states. In Chapter 2, we interrogated how macrophage polarization alters the stimulus-response activation dynamics of the transcription factor, NFκB. Statistical approaches were first applied to differentiate NFκB response dynamics across polarization states and define a landscape of macrophage functional states based on the timecourse data of this single analyte. Utilizing this data however, biochemical parameters of the NFκB signaling network, such as rates of receptor synthesis and proteasomal degradation, that define these different molecular network states could also be inferred by employing a mathematical model of this signaling system. In Chapter 3, we examined how inflammatory signals change the steady-state abundances of hematopoietic stem and progenitor cells (HSPC) in settings of myeloid bias. Beyond simply characterizing differences in abundances and gene expression from these data, employing a mathematical model of HSPC population dynamics allowed identification of likely dynamic rate perturbations. This model-aided analysis determined that differentiation bias of hematopoietic stem cells alone is an insufficient explanation for myeloid bias amongst multipotent progenitors and that proliferation of early myeloid-primed progenitors additionally contributes to myeloid bias with NFκB dysregulation, aging, and malignancy. Together these studies demonstrate the utility of mathematical modeling in the interpretation of biological datasets and additional insights could be gained through extension of these models and integration with other analyses approaches.