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Purdue Undergraduate Students Used the Difference in Swimming Behaviour to Classify Different Strains of Zebrafish


Zebrafish has become a popular research model for neurobiology and drug discovery. One reason for this popularity is that zebrafish embryos are small, which makes it easy for scientists to monitor the behaviour of many individual animals in parallel. In these behavioural studies, the zebrafish are often exposed to sudden environmental changes to trigger characteristic swimming behaviour. However, different strains of normal wild-type (WT) zebrafish may respond differently to the same stimulus. This has limited how much scientists can learn from the results obtained from different WT strains. To address this problem, several undergraduate and graduate students in the Leung Lab collaborated with neural engineers from the City University of Hong Kong to classify three commonly-used WT zebrafish strains by machine-learning approaches. Specifically, these students utilized an approach termed support vector machine (SVM) to evaluate a light-induced swimming response in developing zebrafish larvae. The results show that not only the WT strains respond differently under the same stimulus, but also the initial seconds of the response that is visually-driven provide the best information to distinguish the WT strains. This information will guide the efficient design of zebrafish behavioural study to acquire critical information for advancing neurobiology and drug discovery.

This work has just been published in Computers in Biology and Medicine.

The Purdue students involved in this study are:

  1. Gaonan Zhang. Gaonan was a biology major and is the co-first author of the paper. He is now a PULSe graduate student at Purdue University.
  2. Robert Carmer. Robert was a statistics major who started working on this project under the support of The Howard Hughes Medical Institute Undergraduate Summer Research. He is now an associate in the Argus Information & Advisory Services, LLC.
  3. Prahatha Venkatraman. Prahatha is a graduate student in the Department of Biological Sciences. She is currently a Schlumberger Foundation Faculty for Future Fellow.

The Neural Engineers from the City University of Hong Kong are:

  1. Yuan Gao. Yuan is the co-first author of the paper. He is a graduate student with Prof. Chan.
  2. Rosa Chan. Rosa is an Assistant Professor in the Department of Electronic Engineering at City University of Hong Kong.


Gao Y, Zhang G, Jelfs B, Carmer R, Venkatraman P, Ghadami M, Brown SA, Pang CP, Leung YF, Chan RH, Zhang M. Computational classification of different wild-type zebrafish strains based on their variation in light-induced locomotor response. Comput Biol Med. 2015 Nov 30;69:1-9.

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