Agent-Based Modeling (ABM) of Tumors in Java

Before I begin I would like to quickly introduce myself — my name is Sameer Puri and I am a high school student working in an internship under Dr. Heiko Enderling through the HIP IMO program at Moffitt Cancer Center.

Over the past 5 weeks, I have been developing an ABM in Java for my research. My principal goal in developing this ABM was to quickly generate tumors with 1 million cells. (And prove that Java can perform better in some cases…!)

When I started, I was working with an ABM written by @jpoleszczuk and Heiko in C++ (available here). Having a strong background in Java and little past experience with C++, it was difficult for me to understand. Structs, vectors, memcpy, and other features of C++ are not present in Java. Instead of trying to learn C++, I converted the code to Java, my language of expertise.

Optimization

I decided to optimize the code before making any extensions for my research. In Java, finding slow-down points is simple, since an array of profiling tools are available to find which part of the code is slowing down the ABM. I utilized the web-based profiler WarmRoast, developed by sk89q (on GitHub here).

• One of the biggest time hogs was the Random class which was implemented in the 1990s, and after 20 years, much more efficient pseudo-random number generators have emerged. I replaced it with the XorShift generator, developed by George Marsaglia. The XorShift is theoretically 3 times faster than the default generator. However, the speed-up was far more significant — the call for a random double was so fast that it disappeared from the WarmRoast profiler’s results.

• The returnEmptyPlace method was optimized with a strategy known as pregeneration, or exchanging memory for lower CPU time. The returnEmptyPlace method looks for an empty spot around a cell in the ABM, shuffling a list of 8 directions each time. Instead, I pregenerated all 40,320 permutations of the 8 direction vectors surrounding a lattice square. Now, the returnEmptyPlace method simply uses the speedy XorShift generator to select a random permutation of the directions list to use.
• I also optimized the shuffling of the list of cells in the main simulation loop. I created a new class called SPCollections which uses the XorRandom generator for shuffling. There was a large speed-up here as well.
• Using NetBeans’ built-in hashcode generator, I added hashcodes for the Cell and Indx classes, which will speed up the usage of HashMaps or other hash-based structures.
• Since memory was not a serious concern for me, I made one optimization. The original model in C++ used the char primitive type for its lattice. I turned the lattice into a byte lattice, which has half the memory footprint (chars are 16 bits, bytes are 8 bits).

Additions

• For my research, I had to grow 10 tumors in silico with one million cells each. To speed up simulation I utilized parallel computing with an ExecutorService. With my ABM, you can specify the number of “trials” you want to run. For each trial run, a simulator instance is created and run on a separate thread. Most CPUs today have at least 4 cores. Code in most programming languages tend to make use of only one core unless programmers specify methods that allow for parallel execution of code. By creating a separate thread for each simulation, the threads can operate on separate cores and maximize CPU usage!
• To universalize my data, I saved it utilizing Java serialization. With serialization, Java can output objects to files, which can be loaded later on without needing to code a loader. Others will be able to easily load data generated using my ABM and run their own operations on it.
• I coded an image generator to automatically generate tumor images. Once a simulation is finished, the image generator takes the serialized output of the simulation and generates three types of PNG images for each dumped instance: proliferation, quiescence, and nutrition. It also creates an animated GIF to show the growth of the tumor over time. Each image has the time step (1 step = 1 hour), number of stem cells and number of regular cancer cells at the upper left corner.

Proliferation during Development

Yellow = Glioma Stem Cell Reddish = few divisions ==> Black = many divisions

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Nutrition during Development (on rim)

Nutrients of lattice on the outer edge is displayed. Unoccupied (0) and occupied (9) lattice spots are white

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Quiescence during Development

Orange = quiescent Red = can divide/proliferate

 

 

 

 

Download

You can download my ABM here