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The Tenth q-bio Summer School - Albuquerque: Computer Lab 7
Contents
Using the accurate chemical master equation (ACME) modeling method to study stochastic viral dynamics
The ACME provides an efficient and optimal algorithm for enumerating state spaces and directly solving the discrete Chemical Master Equations (dCME) for modeling stochastic biological networks in systems biology. Instead of running millions of stochastic simulation trajectories using Gillespie's algorithm (Gillespie, 1977 JCP), the ACME can accurately 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 ACME 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 PNAS). The ACME can be used 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 use the ACME method to study the stochastic controls in HIV intracellular circuits during initial HIV infection.
Online Resources
Online simulation of ACME Java plugin is required to run the tool.
Download SBML file for the bistable Schlogl model
Download SBML file of the stochastic viral dynamics model continuous viral production
Download SBML file of the stochastic viral dynamics model with burst viral production
Download Initial state file of the stochastic viral dynamics model
References
1. Youfang Cao, Anna Terebus and Jie Liang (2016). State space truncation with quantified errors for accurate solutions to discrete Chemical Master Equation. Bulletin of Mathematical Biology. 78:617–661.
2. Youfang Cao, Anna Terebus and Jie Liang (2016). Accurate Chemical Master Equation solution method with multi-finite buffers for time-evolving and steady state probability landscapes and first passage times. SIAM: Multiscale Modeling and Simulation. 14(2):923–963.
3. 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.
4. 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.
5. 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
You can install the ACME package on your computer in advance, or register on the nanoHub.org to use the online version of the tool. Following is the instructions for installations and nanoHub registration.
1. For Windows
Directly download the binary code from [1] or [2]. Unpack it and run.
2. For Linux and MacOS
Step (1) Installing the xerces-lib 3.1.1 XML parser.
Download the package from: [3] or [4]
Unzip and compile.
Step (2) Installing the libSBML package.
Compile and install into your systems directory.
Step (3) Installing the ACME package.
Download the source code from: [7]
Unzip, compile, and install. The source code zip file includes a couple of example models.
3. Online simulation on NanoHub.
Open: [8] and, click the "Launch tool" button. Java plugin is required to run the tool.
You need to register on the nanoHub.org website to use the tool, it's FREE.
