Physio-omics? Robotic development, software engineering, and machine learning for high throughput, whole animal physiology assays as a path to disease mechanism and drug discovery
Authors: Ward Christopher , Lusk Savannah , Chang Andersen , Patel Dipak , Memos Nicoletta , Al-Madi Mahmoud , Ray Russell 
Published: May 2024

Measurement and analysis of cardio-respiratory function in animal models is essential for our understanding of functional and disease mechanisms that pave the way for human therapeutics and treatments. However, physiological phenotyping in conscious animals is often an onerous process requiring significant observer time, attention, and manual effort while being susceptible to bias and variability that ultimately limits the kinds of questions that can be asked and answered. Once data is collected, comprehensive and accurate analysis is challenging as it often requires either the use of expensive commercial software(s) with limited flexibility to meet the investigator’s needs or lab developed scripts that are often arduous and require specialized skills to utilize, resulting in incremental insights. Here we present an overview of our high throughput physiology assessment and analysis pipeline for interrogating disease mechanisms and enabling drug discovery in congenital cardio-respiratory disorders such as the lethal sudden infant death syndrome (SIDS). We first developed a closed loop robotic platform for automated neonate cardio-respiratory experiments (Looper) that enables automated interventional studies (i.e. hypoxic induction or drug application) based on real-time physiological feedback during the experiment. We show that deployment of multiple Looper platforms in parallel enables an order of magnitude increase in data collection for a single observer over a single workday and is highly scalable to achieve yet greater outputs. When combined with well designed genetically engineered or other disease mouse models, large scale genetic, molecular, and cellular mechanistic insights and drug discovery become feasible through physiological screening paradigms. Next, we developed an open-sourced cardio-respiratory assessment software tool (Breathe Easy) that enables facile deep interrogation of terabytes of data in a high throughput fashion while offering extensive customization capabilities to allow for unique case by case analysis by more skilled users. Breathe Easy output offers near publication ready graphical outputs with significance markings and statistical tables for rapid presentation of results through manuscripts and web-publishing. Lastly, we will present our early work leveraging our robotic and software pipeline to build an analytical machine learning framework that can predict cardio-respiratory failure (death) with high temporal specificity (more than 10 min out) in our SIDS like assay and our efforts to utilize the trained algorithms for 1) real-time prediction, feedback, and intervention application in our SIDS like assays on the Looper platform to screen small molecules for novel therapeutics; 2) elucidating unique physiological mechanisms through comparative training in distinct SIDS models; and 3) determining the potential for these machine learning approaches to be used in human polysomnographic recordings to identify at risk infants. R01HL161142, R01HL130249. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.