Network Effects in Stem Cell Line Studies
In the field of biological research, many studies are done using cell lines. These are a type of in vitro study, in which some phenomenon is tested within the context of a living cell. Thus, these cell lines are used as model organisms for diseases. Many research labs generally use the same group of cell lines for their experiments. Specifically, getting stem cells to perform robustly in experiments is a very complicated and tedious process, and best practices have been learned through years of research from various scientists. The performance of these stem cell lines in experiments relies on several variables. The first is the growth medium that the cell line is grown in, which can greatly affect the performance of the cells. Additionally, stem cells can differentiate into specific tissues such as nerve cells or heart cells. Thus, the differentiation path the cell line takes can also affect its viability for a potential experiment. The third of many considerations is the cells’ ability to grow after being stored at a certain temperature. Such factors have all been tested through trial and error by the scientific community to arrive at certain protocols. Thus, researchers tend to prefer using cell lines that have been used by many other researchers, since it shows that the community has a lot of experience with it, and there could be more information available on it.
Due to this property, researchers’ use of cell lines exhibits network effects. This paper presents a model for the network effect starting with two users. Each time the user uses a specific cell line (A or B) the cost drops by a factor (1-r) for some constant r. The cost of using each cell line is the time spent learning how to optimally grow it and run experiments with it. The network effects are modeled by stating that if the second user uses the same cell line the cost drops further by another factor of (1-sr). This represents the “spillover knowledge” available due to another researcher using the same cell line. Thus, if there are no spillover effects, s=0. The function of the advantage of picking one cell line over the other takes the form (1 − (1 − r)(1 − sr)) where s is a positive constant. Therefore, as spillover knowledge increases the probability of “lock-in” increases. Lock-in is when users pick one of the two cell lines every time after a certain time point. It has been shown that this effect still holds regardless of the number of users. Since the use of cell lines in the life sciences research community exhibits network effects, the companies that own these cell lines could have disproportionate power in the field. [1]
1.Henkel, Joachim, and Stephen M Maurer. “Network Effects in Biology R&D.” American Economic Review, vol. 100, no. 2, 2010, pp. 159–164., https://doi.org/10.1257/aer.100.2.159.