The Lauffenburger laboratory at MIT is undertaking computational systems modeling effort in collaboration with scientists at ERDC and USAFSAM as well as Cal Tech, with the objective of improving capability for translating information concerning neurocognitive pathophysiological phenotypes in terms of underlying molecular factors, across zebrafish, rodents, and humans. The conceptual approach is to construct a computational model, employing machine learning frameworks, based on animal experiment data that relates molecular / cellular data to phenotype, and then to translate that ‘relational’ model to an analogous model for human molecular / cellular to phenotype relationships.
In the first aim our goal is to develop a computational scaffold for translating between species for which comparable omic data types are available, permitting determination of dysregulated biological processes underlying cognitive pathophysiology. As an initial application we are pursuing schizophrenia as a cognitive pathology for which transcriptomic data are available in mouse and human contexts. In the second aim our goal is to then map data from zebrafish contexts, where diverse types of data are generated such as hits from genetic and chemical studies (such as are available from publications by our collaborators), to the mouse-human translation scaffold. Integration of results from these two aims together should produce a seamless “multi-way” translation capability across all three species classes. Following successful accomplishment of these aims for the schizophrenia application, we will proceed further to environmental chemical cognition stressor applications.