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research

July 11, 2018, Filed Under: 2018, research

Deliberate practice: becoming an expert

Hi there!

I’m Maiya, a rising sophomore at the University of Michigan, where I study biochemistry and mathematics. I am also a participant in the BME CUReS Cancer REU here at UT in Professor Pengyu Ren’s lab, where I am supervised by two wonderful graduate students: formally, by Brandon Walker, and informally, by Rui Qi.

My research in Prof. Ren’s lab tidily combines my two fields of interest (mathematics and biochemistry) into the fascinating field of computational molecular modeling. Now, though I am studying both, I am much more familiar with biochemistry, and while my computer skills are better than say, my parents, I had only passing experience with programming and scripting before I arrived; this is where practice has come in.

My mentor, Brandon, emailed me before I arrived in Austin to send a couple of papers and mention that most of the projects they were envisioning me working on involved a hefty amount of computer programming, mostly in Python. Of the papers he sent, one was biomedical in nature, the other three outlined the chemical force field that the lab uses, the general concepts behind molecular dynamic modeling, and an example of the use of molecular dynamics to model the free energy of binding of water. I was able to comfortably read and understand the first, but the other three required hefty amounts of Googling, Wikipedia-ing, and wading through unfamiliar words and long equations rife with unfamiliar symbols and words like “Fourier expansion”. Lacking programming experience, I dove into the free Codecademy Python course I found, and hoped for the best.

I arrived in Austin and on my first day, Brandon helped me set up a workstation. Seems straightforward enough, except the lab uses the Linux CentOS operating system, and I’d never used Linux, and had only seen a command line interface when I accidentally opened Terminal on my Mac. He emailed me a protocol and set me to replicating some data on a protein called MELK, a kinase that is upregulated in some cancer cells. I spent a lot of time Googling how to do such-and-such a task and slowly worked my way through the protocol.

One of the first tasks he gave me was writing a script to read a specific kind of molecular coordinate file called a Tinker xyz file and calculate the maximum dimensions of the protein described by the file. I just had to find the most positive and most negative x, y, and z coordinates and then use those to make a ‘box’ that would be filled with water molecules, ions, and eventually the protein. I spent hours just trying to figure out how to read the file, let alone processing the data in it. I ended up modifying the file and using a Python module that automatically reads the modified file to achieve my goal.

screenshot of script written in python language
The afrorementioned first Python script I write after beginning research.

 

Over the last 5-ish weeks, I have seen a lot of change. My focus has turned from MELK to a protein called Aldolase, an enzyme that interconverts between fructose-1,6-biphosphate and glyceraldehyde-3-phosphate and dihydroxyacetone phosphate as a step in glycolysis. It has shown potential as a cancer drug target by acting as a regulator for a transcription factor called HIF-1a, which upregulates cell growth and glycolysis and therefore the production of ATP. Targeting Aldolase decreases the production of ATP, which acts as a feedback regulator of HIF-1a, decreasing both cell growth and further decreasing the production of ATP, which is a source of cellular energy.

screenshot of chemical reaction simluation in computer environment
Visualizing the binding pocket of Aldolase bound to an inhibitor using two different visualization environments: Pymol (left) and VMD(right). I do this to try to identify potential interacting atom pairs to track using a python script.

 

More tangibly, my programming ability has improved. Though I am by no means an expert yet, through 5 weeks of writing a variety of scripts to do little tasks, debugging, improving, and more debugging, my programming abilities have improved immensely.

 

At the beginning, I knew enough about programming to be able to recognize when a task could maybe be more efficiently done with a short program, but not to execute. Now, I can at least plan out and start writing a script to achieve a task, even if I hit roadblocks along the way.

 

One of my most recent tasks was writing a program that reads what is called a Tinker arc file (essentially a bunch of Tinker xyz files all strung together), computes and records the distance between specific sets of atoms, and plots them against frames or as a frequency distribution. The arc file is the output of a molecular dynamics simulation and can be thought of as video film: a bunch of snapshots that when played sequentially let you see an approximation of what reality looks like. Like that first program I had to write, this has to read the Tinker xyz format, but in this case, I can’t just modify the file due to the file’s prohibitively large size. Encountering the same issue again, namely, how to read this file type, has given me the chance to put the small pieces of coding skills that I’ve developed over the last several weeks to use and allowed me to tangibly see my own improvement.

Screenshot of script with improvements
The aforementioned new script. Note the function called openfile: this is the part of my program that ‘reads’ the file. While this is still not the most efficient way to open and read the file, it is certainly an improvement over altering the file itself.

 

screenshot of a chart showing potential for hydrogen bonds in different gels
One of the plots generated by my new script. This allows us to analyze whether there might be hydrogen bonds between certain atoms in the binding site.

 

Every script I write, every time I use a remote secure server host to access the lab cluster from somewhere else, every time I use the terminal interface instead of using Finder on my laptop to make folders or move or rename files, every time I have to check Google or Stack Exchange or a module’s documentation or tutorials…

I know I am improving as a programmer, as an engineer, and as a scientist.

I know I am improving because each little task amounts to practice, and each little snippet of practice sums to growth and brings me a baby step closer to expertise.

-Maiya Yu, University of Michigan

July 3, 2018, Filed Under: 2018, cancer, reflections, research

Reflecting on Research

Hi, I’m Josh, a rising sophomore at Johns Hopkins University majoring in Biomedical Engineering and a part of the UT Austin BME CURes Cancer REU.

Although our lab is currently in the “set up phase” of my project, much progress has been made. Our experiment itself is two-fold: part wet lab, part dry lab, and I have learned to enjoy the breaks that each one gives the other.

Screenshot of student's work in MATLAB
This is the type of analysis our lab performs where we take the percentage of cells that are alive after a certain dose of treatment and fit them to a curve using Matlab.

My project involves using the process of EMT (Epithelial to Mesenchymal transition) to predict the effect of matrix stiffness on chemotherapy resistance in mammalian cancer cells. Like I mentioned, we are currently in the setup phase: seeding, lasing, dosing, and performing live/dead assays in order to test the effect of a wider range of matrix stiffnesses on chemotherapy resistance using Dose/Response curves.

photos of lab work with cells
In these pictures, we are seeding 5 mM gels in preparation to dose and eventually perform a live/dead assay on them.

Our lab hopes to develop a mechanistic model that predicts the effect matrix stiffness has on drug resistance due to a change in the proportion of mesenchymal cells. This can be detected through confocal imaging by marking certain transcription factors like YAP.

This lab has taught me much more than to follow protocols. It has given me the ability to be independent and understand that I enjoy exploring new topics I am not caught up on in the scientific community.

I am suddenly gaining critical skills like reading published papers, and without the great flexibility of my mentors and PI, I would never be able to gain these tools that I will definitely need down the road. Excited to see what results the month of July holds!

-Joshua Krachman, Johns Hopkins University

July 18, 2017, Filed Under: 2017, cancer, research, texas4000

Letter to Texas 4000 Rider: Basil Hariri

San Jacinto recreation field at dusk. Photo by Elana Helou

Dear Basil,

My name is Elana, and I am also the child of Lebanese immigrants, so I truly relate to the feeling that all my accomplishments are only made possible by my parents’ sacrifices. Growing up, I always felt that I should be working towards something that would make their sacrifices worthwhile. So, once I was old enough to truly understand the extent that cancer and other diseases were affecting my family members, I decided that I wanted to work as a medical researcher to help bring relief to families all around the world that were experiencing some of these same effects.

My interest in research is largely fueled by experiences with people who I have known for most of my life, so I really found it inspiring that your story about why you ride focused on someone you only encountered for a few hours. I felt that it truly resonated with the fact that cancer is universal and affects so many people in so many ways, which can be easy to lose sight of when someone very close to you is fighting their own battle with the disease.

I admire your dedication to and courage in fighting cancer through your own physical toil, and I hope you enjoy the rest of your journey.

Sincerely,
Elana Helou, Smith College

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