As an undergraduate
student majoring in bioengineering at George Mason University, I have become
deeply interested in cancer research through in-class exercises on drug
absorption and excretion with anticancer drugs. I am interested in researching
and working in the bioengineering field, specifically in cell-cell interaction
and analysis. The research project I am currently conducting is with the
Undergraduate Research Scholars Program in the lab of Dr. Nitin Agrawal. Dr.
Agrawal’s research primarily focuses on biological signals in tumor cell
migration and abnormal growth as well as tumor cell interaction and
development. By working on a research project involved with co-culturing cell
analysis, I have been able to obtain a hands-on learning experience on
researching in this field to gain insight in cancer cell investigation. I can see this project being related to my long term goals because
I hope to work with cancer research in the future.
On a
weekly basis I will be passaging several ratios of co-culture of two cells:
breast cancer cells (MDA-MB-4175) and human lung cancer cells (NCIH460). A
co-culture of two cells is developed in in-vitro models to offer an
in-vivo-like environment. Co-cultures are valuable for exhibiting and analyzing
the interaction and signaling between various types of cell.6
Co-cultures can also be utilized to observe intercellular communication, cell
movement, and stimulation and preservation of cell function and
differentiation.6 In this research project I am studying and
observing the interactions between breast cancer cells (MDA-MB-4175) and human
lung cancer cells (NCIH460) to discover the effects of co-culturing two primary
cancer cells.
One thing I discovered
this week is that the growth rate of the human lung cancer cells (NCIH460) is
faster than the growth rate of breast cancer cells (MDA-MB-4175) so this may
create a bias in the results obtained in this experiment. If there was a future
experiment on the co-culture of two different cell types, the growth rates
should be similar to avoid a bias in the results.
