Jacob Beierle, Ph.D.

My goal is to create new methods of behavioral quantification and implement these methods to identify candidate genes and targets for high need clinical populations. Specifically, I aim to harness machine learning (ML) to assess spontaneous pain in models of neuropathic pain, migraine, and post-surgical pain. Using ML, animals can be observed at high spatial and temporal resolution over extended periods to infer pain-related behavior. This approach enables reproducible and automated assessment of spontaneous behaviors at speeds impossible for human grading. My work proposes to leverage the Kumar lab’s existing ML platforms and the genetic diversity of mouse strains available at JAX to create and validate an ML informed scale of neuropathic pain, migraine, and post surgical pain. Constructing these tools will yield: 1) an automated, sensitive, sharable, and reproducible assays of pain, 2) a robust measures of spontaneous pain behaviors, and 3) a tool to identify the genetic determinants of pain.

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Jacob Beierle on Orcid