A systems investigation of the neural regulation of cardiovascular function

Hypertension, a disease that affects one in three American adults, is characterized by sustained elevated blood pressure, a condition that often leads to heart disease and stroke. Physiologically, such sustained elevated blood pressure appears to implicate a blood pressure control system that has adapted to a blood pressure set-point higher than that of normotensive individuals. This suggests that the development and maintenance of hypertension may be due to a malfunctioning blood pressure control system. Consequently, to treat hypertension effectively, we must clarify the poorly understood molecular mechanisms underlying the processes by which these blood pressure controllers adapt from the healthy state to the hypertensive state. In this dissertation, quantitative modeling and analysis was employed to understand how the nucleus tractus solitarius (NTS) in the brain, the primary controller within this control system, undergoes this adaptation. In particular, we have focused on a critical mechanism for the central regulation of blood pressure: the activation of tyrosine hydroxylase (TH) transcription by angiotensin II type 1 receptor (AT1R) signaling in the NTS. Great emphasis is placed herein on the model development, the simulation studies, and the model analysis studies of the TH gene regulatory network activated by AT1R in the NTS.;Despite numerous experimental investigations, our current understanding of the mechanisms linking AT1R activation to TH transcription remains incomplete. To gain insight into the dynamics of AT1R-induced TH gene expression, a mechanistic, ordinary differential equations model was developed to represent the processes bridging AT1R-activated signaling kinases (FRK, ERK, and JNK) with the downstream activation of transcription factors Elk1 and members of the AP-1 family (Fos:Jun, Jun:Jun, and Jun:ATF2). In addition, the molecular process by which AT1R activation increased c-Fos and c-Jun protein levels were represented in our model. Simulation studies were conducted in order to predict the AP-1 response to AT1R activation, and to investigate the effect of signaling kinase inhibitors on the predicted AP-1 response. The results of these simulation studies revealed that the response of AP-1 involves a combination of AP-1 transcription factor dimers that provides a robust long-term AP-1 response to FRK and ERK inhibition, and a fragile long-term AP-1 response to JNK inhibition. Furthermore, these results, which were validated by published experimental data from pathway inhibitor studies, suggested an imbalance in the relative role of signaling kinases on gene regulatory network outcome.;The contribution of signaling kinases to AT1R gene regulatory network outcome was quantified by model analysis studies that considered explicitly the heterogeneous population of NTS neurons. To understand how heterogeneity, which was represented by variability in the abundance and activity of multiple gene regulatory network components simultaneously, influences model output, a novel multivariate analysis approach was developed that integrates variance-based global sensitivity analysis with decision tree analysis. The results of this model analysis study indicated that: (i) AP-1 is sensitive to a small subset of network reaction parameters, (ii) the sensitivity of AP-1 shifts in time from FRK-mediated processes to JNK-mediated processes, (iii) network reaction parameters influence AP-1 directly only during the initial response, and (iv) TH is sensitive to FRK-mediated processes and insensitive to JNK-mediated processes.;The results from the aforementioned model simulation and analysis studies indicated a prominent role of signaling kinases in AT1R gene regulatory network outcome; however, current mechanistic understanding of how AT1R activates signaling kinases remains incomplete. To clarify the role of signaling kinases on AT1R network outcome further, a simple kinetic model was developed to represent the dynamics of AT1R-activated signaling kinases. A model analysis study was then performed to quantify the effect of simultaneous perturbations in signaling kinases dynamics on AT1R network outcome. The results of this model analysis study revealed (i) AP-1 is highly sensitive to the dynamics of JNK signaling, (ii) AP-1 is almost insensitive to the dynamics of ERK, (iii) TH and AP-1 are highly sensitive to the initial time delay of JNK signaling.;Taken together, the results of this dissertation provide quantitative insight into the dynamics of a key molecular mechanism underlying blood pressure control. Such quantitative insight could provide new targets for developing effective treatments of hypertension. In addition, the gene regulatory network model developed here lays the foundation for future modeling efforts to describe additional molecular processes involved in the NTS adaptation to the hypertensive state. Finally, the multivariate model analysis approach developed in this work is broadly applicable to other mathematical models describing biological systems.