The Bagheri lab will computationally analyze and model the metabalomic, transcriptomic and proteomic data that culminates from time-course experimental quantification of mussel tissue under various treatment protocols. Each environmental treatment variable will be defined as a cue, -omic samples will be defined as signals, and organismal behavior will be defined as responses. Our main contribution will be the development of a directed, cyclic network that elucidates the cue-signal-response relationship across the cellular, tissue, and organismal scales. We will do so through the application of a novel network inference regression algorithm that we have recently shown is less computationally expensive, more accurate, and more scalable than the most common inference metrics. New computational strategies that allow for the inclusion of prior knowledge in regression approaches will also be explored. Taken together, we can begin to resolve the complex regulatory motifs (i.e., feedback and crosstalk) that are critical in governing organismal response within and among multiple biological scales. Resulting models will provide a means of assessing the impact of environmental stresses on the whole organism, as well as understanding and predicting complexity and emergence in biological systems.
|Effective start/end date||9/1/16 → 8/31/20|
- National Science Foundation (IOS-1557495)