Automated Management of Zebrafish Strains and Zebrafish Facilities
10.12300/j.issn.1674-5817.2025.136
- VernacularTitle:斑马鱼品系及斑马鱼鱼房的自动化管理
- Author:
Jiwen BU
1
;
Ye HUA
2
;
Shirong JIN
2
;
Ningxin REN
1
;
Funing LI
1
;
Jiulin DU
1
Author Information
1. Laboratory of Sensory Integration & Behavior, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
2. Research Platform of Whole Brain Mesoscopic Neural Junction Map (Zebrafish), Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
- Publication Type:Journal Article
- Keywords:
Zebrafish;
Zebrafish facility;
Information management system;
Big data analysis;
Automated management
- From:
Laboratory Animal and Comparative Medicine
2026;46(3):416-425
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveThis study aims to improve the management efficiency of zebrafish strains and zebrafish facilities and to transform the traditional management model that relies on manual labor. The goal is to promote a comprehensive transition from traditional manual operation to standardized, digitalized, automated, and refined management, thereby significantly improving management quality and work efficiency. MethodsThe research group independently developed the "Zebrafish Facility and Zebrafish Strain Automation Management System V1.0" (Software Copyright Registration No. 2021SR0236837), which comprehensively recorded all operational data of the zebrafish facilities and completed data on zebrafish strains. By combining in-depth mining of these data with management experience, management regulations were transformed into executable system instruction sets, and a full-process automated closed-loop management framework of "Monitoring–Analysis–Early Warning–Task Assignment–Execution–Recording" was established. ResultsThis management system has been operating stably for 5 years and has been successfully applied in 3 zebrafish facilities, cumulatively supporting 78 researchers and efficiently managing 1 108 plasmids and 1 123 zebrafish strains, among which 291 strains has been incorporated into zebrafish sperm bank management. A Likert scale survey of 28 core users covering 14 evaluation dimensions across 4 major categories showed average scores of 4.46–4.89, all far above the midpoint (3 points) of the scale, indicating that users gave high evaluations to all dimensions of the system. Meanwhile, the standard deviations of most dimensions were low (s ≤ 0.44), reflecting good consistency in user evaluations. ConclusionThis study integrates information technology, big data analysis, and modern management concepts to achieve a virtuous cycle of "data collection–analysis and execution–system optimization" in zebrafish facility management. It significantly improves management efficiency and refinement and promotes the development of management models toward automation.
