New methods for learning network models underlying Immune-microbiome interactions from heterogeneous (multi-omics) data-sets

I will discuss our efforts to model diverse immune cell types interactions with their environment and with the microbiome. The first part of the talk will detail new efforts to learn large scale biophysically motivated network models of both rare and common immune cell types. Here I will focus on ways of integrating new genomics data-types to constrain network model selection. This inference results in both network models and estimates of regulatory factor activity in a host of conditions. I’ll detail our method for learning networks and discuss key challenges that stand in our way as we aim for ever more biologically detailed model descriptions. In the second part of the talk I will discuss new methods for learning microbial interaction networks (from both environmental and human associated datasets). I’ll recent efforts to both learn these networks and also use the networks to model connections to complex meta-data (such as host immune factors, genetics and metabolites) as well as new methods for making sense of microbiome time series. The talk will finish with some discussion of limitations and implications for experimental design.