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August 2015 – COMPUTE CANCER
https://computecancer.wordpress.com/2015/08
All about models and implementation techniques. Cancer Stem Cell CA in Python. If you don’t have Python, the easiest way to get it and nearly all of its scientific packages is to download the Anaconda distribution from https:/ store.continuum.io/cshop/anaconda/. My personal favorite is PyCharm). One final note. I’ve taken screenshots of the code to make it easier to read, but the raw code can be found in this .doc file: CSC CA in python. Next, we’ll import Numba, a ‘just in time’ compil...Using numbaR...
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Wrapping C++ code of agent-based tumor growth model into MATLAB – COMPUTE CANCER
https://computecancer.wordpress.com/2015/06/13/wrapping-c-code-of-agent-based-tumor-growth-model-into-matlab
All about models and implementation techniques. Wrapping C code of agent-based tumor growth model into MATLAB. Last week I posted a C implementation of basic cancer stem cell driven tumor growth model. About 100 lines of a code allowed to perform simulation, but there was nothing about exporting the results, doing more complicated analysis, visualization, or performing data fitting. Of course each of those tasks can be performed by writing another large piece of the code. Int N; bool* lattice; vector cel...
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About – COMPUTE CANCER
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All about models and implementation techniques. After many years of dealing with mathematical/computer models of cancer I came to a conclusion that we researchers/modelers really need a place in which we could show tricks that we utilize in our codes to make them faster. I hope that this blog will be a venue for exchange of our clever implementation techniques. Leave a Reply Cancel reply. Enter your comment here. Fill in your details below or click an icon to log in:. Address never made public). Java, C ...
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COMPUTE CANCER – Page 2 – All about models and implementation techniques
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All about models and implementation techniques. The above equation has of course analytical solution, but let us use a MATLAB ordinary differential equations solver to find a numerical solution. Generally it is a good idea to start with the solver that has the highest order – in our case it will be. Based on the Runge-Kutta method. We will also consider another solver,. But situation changes when the growth rate is large (while keeping small relative difference between the calculated solutions). Let us c...
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spuri4096 – COMPUTE CANCER
https://computecancer.wordpress.com/author/spuri4096
All about models and implementation techniques. 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. When I started, I was working with an ABM written by @. And Heiko in C (available here. One of the biggest time hogs was the Random class which was implemented in the 1990s, and after 20 years, much...
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Quick parallel implementation of local sensitivity analysis procedure for agent-based tumor growth model – COMPUTE CANCER
https://computecancer.wordpress.com/2015/06/26/quick-parallel-implementation-of-local-sensitivity-analysis-procedure-for-agent-based-tumor-growth-model
All about models and implementation techniques. Quick parallel implementation of local sensitivity analysis procedure for agent-based tumor growth model. In the last couple of posts I’ve shown how to implement agent-based model of cancer stem cell driven tumor growth, both in MATLAB. Few weeks ago I’ve shown here. We will start the coding (in MATLAB) with setting all simulation parameters. NSim = 100; %number of simulations to perform for a given set of parameters tmax = 30*3; %number of days to simulate...
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jpoleszczuk – COMPUTE CANCER
https://computecancer.wordpress.com/author/jpoleszczuk
All about models and implementation techniques. Some ideas about how to efficently store simulation data. After my last post. About visualization of 3D simulated tumors @jc atlantis. In this post I will show few tricks (in C ) how to efficiently store output of 2D agent-based model presented in one of the previous posts. As a result we will reduce the size of generated simulation output from about 1Gb to reasonable 35Mb. Each cell is described by two variables: remaining proliferation potential (p,.
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Cancer stem cell driven tumor growth model in C++ – COMPUTE CANCER
https://computecancer.wordpress.com/2015/06/06/cancer-stem-cell-driven-tumor-growth-model-in-c
All about models and implementation techniques. Cancer stem cell driven tumor growth model in C. Because of the feedback that I’ve received after publishing first three posts, I’ve decided to change a tool of interest from MATLAB to C . Today, I’ll show how to quickly implement a cancer stem cell driven tumor growth model in C . It is almost the same model as implemented in my previous post. I’ll explain why it is “almost” the same at the end of this post). The parameters of the model are defined as foll...
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Setting complex domains for agent based models using bitmaps – COMPUTE CANCER
https://computecancer.wordpress.com/2015/06/09/setting-complex-domains-for-agent-based-models-using-bitmaps
All about models and implementation techniques. Setting complex domains for agent based models using bitmaps. In the previous posts (CA in MATLAB. I’ve shown how to implement cancer stem sell driven tumor growth model. In both codes I have set the boundaries of the lattice to. Without adding those sites to additional. I’ve prepared two exemplary images that I will use further in the simulations. The first one is adapted from the paper by Enderling et al. And the second is a generated text using PowerPoint.
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Quick implementation of hexagonal lattice for ABM – COMPUTE CANCER
https://computecancer.wordpress.com/2015/08/07/quick-implementation-of-hexagonal-lattice-for-abm
All about models and implementation techniques. Quick implementation of hexagonal lattice for ABM. In the agent based modeling we are typically more interested in the rules governing the cell fate rather than the basic setting of the lattice (if we don’t look at the off lattice model). However, the particular setting of the computational domain can have an effect on the model dynamics. In 2D we typically consider the following lattices and neighborhoods:. And C version here. Static const int indcNeigh[] ...
