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The Ninth q-bio Summer School - Albuquerque: Computer Lab 9
Contents
Using the Finite Buffer method (fb-dCME) to model stochastic viral dynamics
The Finite Buffer method (fb-dCME) provides an efficient and optimal algorithm for enumerating state spaces and directly solving the fundamental discrete Chemical Master Equations for modeling stochastic gene regulatory networks in systems biology. Instead of running millions of stochastic simulation trajectories using Gillespie's algorithm (Gillespie, 1977), the Finite Buffer method can accurately and efficiently capture the stochastic dynamics including important rare events in biological networks by directly solving the steady state and time-evolving probability landscapes for the underlying dCME. The fb-dCME method has been successfully applied to study important biological processes and identify key interactions in complex regulatory network, such as the cell fate determination and switching efficiency and stability issues in the epigenetic circuits of phage lambda, a virus to E. coli cell (Cao et al. 2010). The fb-dCME can be applied to study broad issues in systems biology, such as the regulation of stem cell development and differentiation, and cell cancerogenesis. In this session, we will apply the fb-dCME method to model the stochastic viral dynamics during initial HIV infection. We will build stochastic viral dynamics models to study the continuous and burst virion production processes, respectively. All models will be encoded in SBML format.
Online Resources
References
1. Youfang Cao and Jie Liang (2008). Optimal enumeration of state space of finitely buffered stochastic molecular networks and accurate computation of steady state landscape probability. BMC Systems Biology 2:30.
2. Youfang Cao, Hsiao-Mei Lu and Jie Liang (2010). Probability landscape of heritable and robust epigenetic state of lysogeny in phage lambda. Proceedings of the National Academy of Sciences USA, 107(43), 18445–18450.
3. John Pearson, Paul Krapivsky, and Alan Perelson (2011). Stochastic theory of early viral infection: Continuous versus burst production of virions. PLoS Computational Biology, 7(2): e1001058.
Installation Instructions
Please installing the fb-dCME package on your computer in advance, or register on the nanoHub.org to use the online version of the tool without installing. Following is the instructions for installations and nanoHub registration.
1. For Windows
Directly download the binary code from http://tanto.bioe.uic.edu/dcme/download/Win32_dCME.20140616_withMSVCR.rar or http://tanto.bioe.uic.edu/dcme/download/Win32_dCME.20140616.rar. It's ready to run.
2. For Linux and MacOS
Step (1) Installing the xerces-lib 3.1.1 XML parser.
Download the package from: http://tanto.bioe.uic.edu/dcme/download/xerces-c-3.1.1.zip or http://xerces.apache.org/xerces-c/download.cgi
Unzip and compile.
Step (2) Installing the libSBML package.
Download from: http://tanto.bioe.uic.edu/dcme/download/libsbml-3.4.1-src.zip or http://sourceforge.net/projects/sbml/files/libsbml/3.4.0/
Compile and install into your systems directory.
Step (3) Installing the fb-dCME package.
Download the source code from: http://tanto.bioe.uic.edu/dcme/download/FB-dCME_20130513.tar.gz
Unzip, compile, and install. The source code zip file includes a couple of example models.
3. Online simulation tool, no need to install the packages.
Open: https://nanohub.org/tools/fbsdcme and, click the "Launch tool" button.
You need to register on the nanoHub.org website to use the tool, it's FREE.
