Macrophages'ability to recognize pathogens is crucial for the immune system response. Recognition is done by a set of membrane receptors and a signaling network that connects the receptors to activation of key transcription factors. High sensitivity to pathogen detection is crucial for disease prevention, whereas high selectivity is important for preventing unnecessary inflammation. The goal of this work is to quantitatively investigate how the structure of the signaling network affects sensitivity and selectivity toward pathogen detection. Specifically, I will focus on two elements in the network design: cross-talk between pathways within a single cell and cross-talk between cells via paracrine secretion. The first part of this work, investigates how sensitivity and selectivity are affected when macrophages are presented with multiple pathogen derived molecules. The main hypothesis is that by using cross talk between signaling pathways, macrophages are able to increase their sensitivity without hampering their response selectivity. To test this hypothesis I will perform systematic experiments to measure the transcriptional response using promoter reporter systems. These experiments would be augmented by computational modeling that will test how specific biochemical interactions in the signaling network results in increased sensitivity and selectivity towards pathogen detection. In the second part of my work, I will investigate the hypothesis that communication between cells via paracrine secretion increases the overall effective sensitivity of a population of cells when compared with the sensitivity of a single cell in that population. To test this hypothesis I will use hydrogels that create diffusion barriers and novel microfluidics devices that will enable me to control the number of communicating cells. In addition, computational modeling will test the affect of stochasticity in gene expression on pathogen recognition. The signaling network that is responsible for pathogen detection is important for human health and is related to several diseases. Aside for pathogen related infections, many inflammatory disorders (such as COPD, and Asthma) are associated with this signaling network. A better understanding of the functional significance of the signaling network structure both within and between cells, as well as predictive computational models of macrophages response are essential for long term progress and the rational therapy design.
