Research was profoundly satisfying work: investigating an idea through a systematic process was reward enough, sometimes supplemented with a publication, presentation, or collaboration with others.
Cultivating the seed of a hypothesis with what other researchers had uncovered or from the input of impassioned colleagues was a rush and the desire to discover new treatment possibilities fueled many long days in the lab and late nights in the library.
As data was gathered and analyzed, there was either satisfaction in validation or excitement from the positivity of a negative or unexpected result. As one course of experimentation was completed, the results informed the design of the next series. Known variables were isolated, new ones were identified and a formula was devised to get to the underlying mechanism that would solve the research problem.
However, as engaging as the process was, it could not fully offset the frustration caused by the one factor the research methodology could not control: Time.
My research was focused on developing treatment therapies to prevent the rejection of translation organs and tissues.
How do you measure how successful a treatment regimen is? By how long transplanted tissue survives before the host rejects it.
Time. We would be excited as milestone days, weeks, and months passed but also anxious as until a full analysis was performed post-rejection, I would not know what the cause or contributing factors were.
As I began a deep dive into computing to analyze and present the data, I was struck by the idea of how I could address the Time obstacle: Create a computer model that mimicked the in vivo environment.
Such a model would not replace the need for research but help identify the most promising scenarios to test and subsequently speed the generation of data. This would be an engine that allowed for multiple input variables and countless iterations to be fed through at a rate that no lab bench could match.
Curiously, this sparked an interest in computing capabilities and eventually programming that led me away from the research world and into the burgeoning IT world.
With the advent of powerful processing resources and the recent momentum of AI, perhaps it is time to revisit the seed of that idea from the experimental lab and grow it into a research reality.
