Aging and Adaptation in Infectious Diseases II/Session IV: Short Talks for Late Arrival
May 1, 2019
10:00 am - 12:00 pm
Jean Carlson (UCSB)
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- Andrea L. Graham (Princeton)
Research on laboratory mice has provided much of what we know about the fundamental biology of the mammalian immune system. Yet because so few life-long experiments have been conducted on mice, we know remarkably little about immunosenescence in “the model mammal.” Classic work on Biozzi mice is an important exception. I will describe some of the experiments and key insights of the work of Biozzi and colleagues in the 1960s-1980s, especially on links between antibody responsiveness and organismal longevity.
In this collaborative project, we seek to understand various observations on immunosenescence, such as the breakdown of innate-adaptive collaboration and increasing variability of individual performance later in life. Earlier theoretical works have yielded much insight on the capacity of innate and adaptive immunity separately, yet these models with only one arm of the defense cannot explain the observed inflamm-aging (aging with inflammation). By considering the crosstalk between innate and adaptive responses, we build an integrative model that shows promising results consistent with experiments. Our preliminary findings highlight potential determinants of individual fates as well as the timing of inflammation dominance.
- David Schneider (Stanford)
We have a data set that follows mice suffering from malaria from start to finish. We’ve looked at the microbiota, circulating immune cells, cytokines and metabolites to produce a time series that follows about 800 variables. We can map many of these, like the metabolites, onto function based networks that were worked out decades ago. We can also make networks de novo based only on the data. We added variation to this system by measuring these parameters across 8 different mouse strains that show extreme variation in their survival as well as testing aged mice for one strain. The problem I now face is showing how these networks vary over strain space and age in a way that helps the viewer understand the biology behind these changes. Should we be modeling the trajectory of the infections through interesting phase spaces? Should we be observing how the networks change over time and genetic space, and how should we do that?