Project Overview



Biophysics is a relatively new discipline of physics that seeks to understand living systems. Dr. Jeremy Schmit is a theoretical Biophysist who is working toward modeling various aspects of living systems. This summerís project stems from his interest in self assembling systems. Self-assembling systems are promenade in biological organisms. Understanding how these work will be beneficial to understanding how biological and neurodegenerative diseases organize/emerge, improve drug development, and may prove to be a novel technique for nonorganic assembly.


My project was to develop a self-assembling system and then begin to look at how it works. Initially I began by learning techniques in C++ to build a simulation. Then I replicated a section of a paper by Dr. Schmit on Amyloid fibril growth using direct templating. Direct templating develops systems based on a dichotomy that two molecules can bind either correctly or incorrectly. The next step was to build a 2-D environment for this system to grow in. Once developed, data was collected about its growth rate in relation to concentration. The data supports a general notion among two of Dr. Schmitís papers that these systems do not increase linearly with concentration. Rather, they grow until a critical point where if pushed further the molecules do not bind efficiently and growth levels off or decreases. The environment was then converted to 3-D. Simultaneously, it was of interest to develop methods to probe the geometry of the system during growth. These methods were developed but only a few were implemented and tested. This project warrants further investigation into the geometry of this system.



Research Log:




Growth by direct templating:


This was the heart of the project for the REU. I was tasked to develop and explore growth based on a two-body interaction between molecules. I started in 1-D by replicating part of Dr. Schmits paper referenced in the overview. The next step was to develop a 2-D environment. Working in 2-D produces a host of ergonomic problems that I did not expect. For example in two dimensions itís possible for an overlap of sites (figure 1). It required developing code so that each site could talk to its nearest neighbors so that an overlap of binding was avoided. Another example is dealing with boundary conditions. The end of the grid is not the boundary on the growing amyloid. So the simulation had to ignore this boundary and wrap around to the other side of the grid. These are just a few examples of the problems that came up in developing the environment of the simulation. It took a while to ensure that the program was doing everything properly but this turned out to beneficial in the end. After weeks of working in 2-D and collecting data and making upgrades to the code I moved to 3-D. Quite a few of the same problems arose from the conversion between 1-D and 2-D. Fortunately, because I spent a long time really making sure that any major issues were fixed, these issues were trivial.


Putting windows on the Blackbox:


Once the system was developed the next step was to figure out how to look inside of it while it works. This is perhaps the most exciting part of the project. The task was to gather data on how each site was binding and unbinding during growth. I was able to develop a method of looking at how these site are binding and unbinding. Furthermore, it was of interest to look at how kinked vs. nonkinked binding (pictured below) progressed through the simulation. This is where the project concluded. However, the initial data implies that there may be a difference in geometry between the most productive concentrations and less productive concentrations. This should be researched further, perhaps next yearís REU could explore this further.




A big part of science research is presentation of data. In simulation based research this usually involves some sort of movie or gif.In the beginning of the project the main concern was writing code and upgrading/debugging it. However, as the code took shape and I became confident in its accuracy, presenting and visualizing the simulation became very important. In the first few weeks I would make pictures out of the data outputs. Then I found Virtual Molecular Dyanmics (VMD) which is a nice software for visualizing simulations like this one. It took a while to write code that would not interrupt the simulation and interface with VMD to produce simulations. Eventually interfacing between the two became reality and simulations could now be presented visually.




Background photo: