Towards a Quantitative Understanding of TNF's Signaling Functions /

A critical aspect of the macrophage inflammatory response to pathogen challenge is the rapid production of TNF that signals in autocrine and paracrine manners to carry out innate and adaptive immune responses. Toll-Like Receptors (TLRs) are highly expressed in macrophages, and recognize extracellular and intracellular pathogen signals leading to the activation of transcription factors and cytokine production. There have been numerous TLR-mediated signaling mechanisms identified that control the production of TNF, but it remains unclear how they coordinate together in macrophages. This dissertation details a systems biology approach to develop a quantitative understanding of how TNF is produced and signals in the context of the macrophage inflammatory signaling network. Chapter 1 presents an overview of the inflammatory and innate immune signaling network that coordinates the production of TNF in macrophages, as well as a description of the field of computational systems biology. Chapter 2 describes the quantitative, experimental characterization of modules for each step in TNF production : gene transcription, mRNA half-life stabilization, translation, and secretion. Mathematical models are designed from the module architechure that can recapitulate experimental data, and the three simple models are linked to provide a model for TNF production. In Chapter 3, the TNF production model is connected to previously described TLR, TNFR, and NF-[kappa]B signaling modules to create a multi-modular model for TNF production and signaling in the context of the inflammatory signaling network. Unexpectedly, the model predicts and it is subsequently experimentally confirmed that CpG-induced TNF signals in an autocrine manner to prolong NF-[kappa]B activation and modulate gene expression programs. In contrast, lipopolysaccharide signals in a primarily paracrine manner. Lastly, Chapter 4 provides a discussion on the unique modular approach to systems biology presented in this thesis, the stimulus-specific encoding of autocrine and paracrine TNF signaling functions, and comments on the ways the multi-modular model can be used to make new predictions as well as a potential direction for future work on the iterative expansion of the model to further describe the inflammatory and innate immune signaling networ