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Genetic, Pharmacological, and Computational Investigation of Spontaneous Pain

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Introduction: Opioid analgesics are commonly used to treat pain but have serious side effects, including addiction, dependence, and death from overdose. The Mouse Grimace Scale (MGS) was developed to quantify characteristic facial expressions associated with spontaneous pain. The MGS is reproducible across labs and was used to evaluate the efficacy of analgesics. However, the MGS has not been widely adopted due to its high resource demands and low throughput. To overcome this limitation, we adapted a machine learning model to classify the presence or absence of pain from mouse facial expressions. We called this model the automated Mouse Grimace Scale (aMGS). However, our original “aMGS 1.0” is limited in several respects. We are working to overcome all of these limitations by developing a more sophisticated version of our automated pain classifier (aMGS 2.0). The machine learning algorithm and associated platform will include a cloud-based data repository and analytic tools to facilitate curation of public data, continuous improvement of the model over time, and integration of new analytic tools.

Opportunity: This research opportunity for motivated undergraduate students in an array of disciplines (biology, psychology, biomedical engineering, etc.) will be partially remote (labeling grimace data) with the opportunity to participate in data collection efforts across multiple areas including, rodent handling and surgery, rodent pain behavioral assays, bioinformatics (quantitative trait locus mapping, single-cell RNA-sequencing, automated behavioral data analysis), and engineering (design and manufacturing of mouse behavioral chambers, building low-cost data acquisition systems, etc.). With aMGS 2.0 in place, we intend to investigate an array of questions relevant to the affective component of pain, including elucidating the underlying biological mechanisms and identifying therapeutic interventions.

Interested applicants can reach out to Rahul Patel ( with a resume and cover letter outlining professional goals and aspirations with respect to this opportunity. Students will be expected to dedicate a minimum of 8-10 hours/week to this research project. Credit/pay/volunteer options can be discussed/considered on an individual basis.

Faculty Advisor
Mark Zylka, PhD
Research Supervisor
Rahul Patel, BA
Faculty Email:
Type of Position
Application Deadline