Modeling of cryopreservation pathway operation at an aquatic biomedical stock center for zebrafish

Abstract

Aquatic biomedical model organisms play a substantial role in advancing our understanding of human health, however, comparably little work has been directed towards developing dependable, high-throughput storage programs for valuable genetic resources. The Zebrafish International Resource Center (ZIRC) has developed a standardized cryopreservation pathway and stored thousands of genetic lines in their repository for use by the biomedical research community. This has yet to be replicated in other facilities, and an overall repository-level pathway has never been analyzed for aquatic species. To encourage repository development for other biomedical models and to improve the ZIRC storage process and system, this study used discrete-event simulation modeling to systematically analyze the cryopreservation pathway for efficiency, and to identify improvements. The models reflected "real-world" working conditions and were used to simulate key outputs, such as production capacity over time (throughput) and steps in the process that limit production (bottlenecks). With these models, recommendations were identified to eliminate waiting times and increase efficiency. These included following proper husbandry protocols because male quality significantly affected production time, and the use of part-time operators to assist with steps that had longer Waiting Times (i.e., time samples spent in a queue) to increase production capacity. Simulation process modeling is a powerful tool that can improve the operations of existing repositories. It can also support repository development at other biomedical stock centers, and at other facilities devoted to aquatic species such as research, conservation, and aquaculture production hatcheries.

Keywords: Cryopreservation; Germplasm; Repository; Simulation model; Zebrafish.

Fig. 1.

Process flow diagram of the C2 and C4 zebrafish cryopreservation pathways used in this study. Ovals represent the start and end points; rectangles represent steps in the pathway; rectangles with a curved side represent steps where data were recorded; trapezoids represent steps where operators manually entered data; diamonds represent a decision point (a Quality Assurance checkpoint); hexagons represent set-up steps, and rectangles with a rounded side represent a delay in the protocol. Arrows indicate the flow of materials. Short parallel lines represent transition points between protocol stages. The text near Steps 4, 5, 12 and 29 represent the decision weights in the models that show their occurrence rate (Logic Rule 7). Colored backgrounds indicate where each step took place (white: Shared Workspace, green: Main Fish Room, and yellow: Quarantine Fish Room).

Fig. 2.

Linear regressions of Number of Fish on the Total Time (h) and Time in System (h). All four models (C2, C4, C2 Part Time, and C4 Part Time) are represented. Symbols represent data points generated in Simio. Models C2 Part Time and C4 Part Time have an additional part-time operator assigned to assist with steps that have Waiting Times above 1 min per fish.

Fig. 3.

Power models analyzing the effects of Number of Operators on Total Time (h) and Time in System (h) for C2 and C4 models. Shapes represent averages generated in Simio.

Fig. 4.

Waiting times for individual steps. Steps 1–29 in the C2 and C4 models before and after an addition of an operator to steps with Waiting Times averaging above the 1-min threshold. Plot A represents the original C2 model; Plot B represents the C2 model with an additional operator in Step 21. Plots C represents the original C4 model; Plot D represents the C4 model with an additional operator in Steps 1, 7, 15, 21, 23,24, 28, and 29.