Affecting the macrophage response to infection by integrating signaling and gene-regulatory networks
MetadataShow full item record
Obesity has reached epidemic proportions in recent years. The World Health Organization estimated in 2008 that 1.4 billion people were overweight of whom 500 million were obese. Obesity associates with a wide range of conditions, such as cardiovascular diseases, cancer, diabetes, and neurological disorders, and causes approximately 2.8 million deaths each year. Many studies have established that obesity strongly impacts the normal function of the immune system: it dysregulates production of inflammatory and anti–inflammatory cytokines, alters numbers of immune cells, and causes an overall weaker immune response. Developing therapies that aim to improve the immune response is crucial in order to increase the quality of life of obese subjects and to reduce their ever–increasing healthcare-related costs. The long-term objective of this work is to contribute to the development of therapies that can increase the immune response in obese macrophages. In particular, gene modifications adjusting the response to infection in obese macrophages closer to that of lean macrophages are desired. To this end, the present work focused on the Toll-like Receptors (TLRs), which play an essential role in the detection of pathogens and the initiation of both innate and acquired immune responses. Genes essential to the transmission of the infection signal were first identified using a model of the TLR signaling pathways. These genes provided the basis for reconstructing a gene regulatory network that not only accounts for information coming from the TLRs, but also regulates key reactions within the pathways. The topology and regulatory functions of this network were identified by applying novel computational techniques to time-series gene-expression datasets. The TLR signaling and gene-regulatory networks were then integrated to develop a modeling framework for macrophage that predicts the time behavior of several markers for infection. Finally, formal verification techniques were used to demonstrate that the model satisfies several properties characteristic of the response to infection in macrophage. The work detailed in this dissertation offers a suitable platform for developing and testing biological hypotheses that aim to improve responses to infection.