When studying biology, researchers often run into experimental limitations due to the complexity of living beings—especially in regards to human health and disease. Depending on the scientific questions being asked, researchers turn to a model system or model organism to simulate complex biological mechanisms and gain further understanding.

A model organism can be as simple as an
in vitro cell line, or as complex as a mouse or monkey in vivo system. Since all living things share fundamental genetic material, scientists can make comparisons between the model and a higher organism, like humans. There are ethical barriers to doing extensive research on humans, and model organisms help bridge the gap of knowledge at an augmented scale.

A model system is selected for a variety of reasons including genetics, life cycle/reproductive rate, and the ability to manipulate the system to target specific research questions. Researchers also want to use their funding wisely to collect as much useful data as possible. Model organisms are adaptable, plentiful, and economical, and have contributed to many major advances in the immunology field.

Yeast (Saccharomyces cerevisiae)

Commonly known as baker’s yeast, this single cell organism has been crucial in understanding the fundamental processes that drive the cell life cycle. Yeast is easy to grow in cell culture and replicates quickly, the genome is fully defined, and therefore genes are easy to manipulate. The 2016 Nobel Prize in Physiology and Medicine went to Yoshinori Ohsumi for his discovery of the genes that control autophagy—the process by which a cell will breakdown its cellular components to be recycled—using a yeast model. Autophagy is important to the innate and adaptive immune system response to infectious agents.

Zebrafish (Danio rerio)

These small fish are unique research organisms because they are optically transparent as embryos and larvae. This gives scientists the opportunity to easily visualize biological processes in vivo using fluorescent markers to highlight specific cells or genes. Transgenic zebrafish can be developed to model disease. Breeding colonies are easy to maintain and biological development is quick, so large sample sizes can be acquired in a short amount of time.

Mouse (Mus musculus)

Mice (and also rats) have long been the laboratory standard for immunology research. As mammals, they have a genetic identity quite similar to humans. Like zebrafish, they are easily bred and mouse colonies can be developed quickly and are relatively inexpensive to maintain. Inbreeding results in limiting genetic diversity, which can be useful for reproducibility in biological research. This also results in specific strains of mice which are optimal for immunology work. Nude/hairless mice are immunodeficient and have low levels of T cells. This makes them ideal for studying transplants, cancer, and autoimmune disorders.

Rhesus Monkey (Macaca mulatta)

Non-human primates are the most ideal research model for studying human immunology. They are genetically similar, mount similar immune responses, and develop similar disease pathologies to humans. This model allows for longitudinal observation of acute and chronic infection, treatment with novel therapies/vaccines, and collection of tissue biopsies–all of which are simply not possible in human studies. However, monkeys are also the most expensive research model, and cost may be prohibitive, often resulting in smaller sample cohorts.

There are other disadvantages to using model organisms, as well. A biological process in a lower organism will never perform exactly the same as in human systems. And despite the arguments made above for reproducibility, the cost of large scale animal studies might be a financial burden if resultant experimental data is underwhelming. And of course, ethical treatment of animals used in research is of utmost importance. Oftentimes, it is more humane to euthanize an animal rather than let it succumb to a harmful or painful disease.

Computer modeling is becoming a useful research tool, and in silico research systems can handle large data sets at an astronomical scale for little cost. However, these programs are only developed based on what researchers already know. Whether or not technology will advance enough to complete replace in vivo models is something time will tell.

Featured Image: Uri Manor, NICHD, Flickr CC BY 2.0

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