No abstract

Studying the genetic control of molecular, anatomical and/or morphological phenotypes in model organisms is a powerful tool in the functional analysis of a gene. The goal of our research is to develop algorithms that discover phenotypes of behavior in model organisms, which may identify, categorize, and quantify these phenotypes under conditions of minimal a priori information. Starting from a non-invasive video monitoring of a model organism, we propose an eigen-decomposition of the organism's behavior captured in video. Traditional clustering techniques in space, time, and frequency can utilize this decomposition to characterize the categorical behaviors of an animal, and for an analysis of the behavioral repertoire. This supplies a quantified analysis of behavior with minimal assumptions, a crucial first step in the genetic analysis of behavior.

We have developed a machine vision-based method for automatically tracking deformations in the body wall to monitor ecdysis behaviors in the hornworm, Manduca sexta. The method utilizes naturally-occurring features on the animal's body (spiracles) and is highly accurate (>95% success in tracking). Moreover, it is robust to unanticipated changes in the animal's position and in lighting, and in the event tracking of specific features is lost, tracking can be reestablished within a few cycles without input from the user. We have paired our tracking technique with electromyography (EMG) and have also compared our in vivo results to fictive motor patterns recorded from isolated nerve cords. We found no major difference in the cycle periods of contractions during naturally-occurring ecdysis compared to ecdysis initiated prematurely through injection of the peptide Ecdysis-Triggering Hormone (ETH), and we confirmed that the ecdysis period in vivo is statistically similar to that of the fictive motor pattern.

The problem of elucidating the functional significance of genes is a key challenge of modern science. Solving this problem can lead to fundamental advancements across multiple areas such starting from pharmaceutical drug discovery to agricultural sciences. A commonly used approach in this context involves studying genetic influence on model organisms. These influences can be expressed at behavioral, morphological, anatomical, or molecular levels and the expressed patterns are called phenotypes. Unfortunately, detailed studies of many phenotypes, such as the behavior of an organism, is highly complicated due to the inherent complexity of the phenotype pattern and because of the fact that it may evolve over long time periods. In this paper, we propose applying color-based tracking to study Ecdysis in the hornworm -a biologically highly relevant phenotype whose complexity had thus far, prevented application of automated approaches. We present experimental results which demonstrate the accuracy of tracking and phenotype determination under conditions of complex body movement, partial occlusions, and body deformations. A key additional goal of our paper is to expose the computer vision community to such novel applications, where techniques from vision and pattern analysis can have a seminal influence on other branches of modern science.