UCLA

Macrophages are immune sentinel cells that are distributed in every organ. Their physiological function is to detect pathogens, tissue damage, and immune cytokines to initiate and coordinate a multi-phased immune response that is appropriate for the immune threat. How macrophages specify the appropriate response remains unknown. However, recent experimental studies indicate that the dynamics of the signal-responsive transcription factor, nuclear factor kappa B (NFkappaB), constitute a temporal code that conveys to the nucleus information about the presence and type of immune threat in the extra-cellular environment. Here, I constructed a pipeline to fit a high resolution mathematical model of the NFkappaB signaling pathway to single cell experimental data. To address model fitting challenges due to high cell-to-cell variability, I developed a novel feature based objective function based on six so-called ‘signaling codons’ (i.e. duration, peak, total activity, oscillation content, etc.) identified as crucial for NFkappaB stimulus specificity using mutual information and classification analysis. In addition, I documented the performance of varying optimization algorithms on our large parameter space of 95 biochemical reactions and identified sensitive parameters that specifically tune informative signaling codons. Applications of this high-resolution model include identifying key circuit design principles that encode the observed stimulus-specific use of signaling codons and pinpointing crucial sources of molecular noise that diminish NFkappaB information encoding.