Finite Buffer dCMEAn optimal state space truncation and enumeration method for direct solution of discrete chemical master equation 



Contributors: Youfang Cao HsiaoMei Lu Anna Terebus Jie Liang (PI) Developed by: Liang lab at Department of Bioengineering, University of Illinois at Chicago 
Finite Buffer dCME provides an efficient and optimal algorithm for enumerating state spaces and directly solving discrete Chemical Master Equations, which is a fundamental method for modeling stochastic gene regulatory networks in systems biology. Instead of running millions of stochastic simulation trajectories using Gillespie's algorithm (Gillespie, 1977), Finite Buffer method can accurately and efficiently capture the important rare events in biological networks by directly solving full stochastic probability landscapes for both time evolution and steady state of reaction networks with arbitrary stoichiometry. Finite Buffer method has been successfully applied to study many critical biological processes, 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). Finite Buffer dCME can be applied to study broad issues in systems biology, such as the regulation of stem cell development and differentiation, and cell cancerogenesis. It has also been used to identify key interactions in a complex regulatory network (Cao et al. 2010), which can potentially be used to aid in discovering novel drug targets and designing/optimizing genetic circuits in synthetic biology.
Dependencies
The FBdCME depends on the libSBML package for parsing reaction networks represended in the SBML format. The libSBML package needs to be properly installed and setted up in order to successfully compile the FBdCME package. The FBdCME has been tested compatible with libSBML 3.x, which can be downloaded from sourceforge.net or locally from here. A XML parser package may be required by the libSBML. The xerceslib 3.1.1 has been tested successfully compatible with libSBML and FBdCME. The xerceslib can be downloaded from the official site or locally from here. File format: the initial condition
Format of initial condition file: How to enumerate the state space using buildStateSpace
The buildStateSpace program is designed for
generating the state space of a biological reaction network starting from
a given initial condition. The biological reaction network is defined in a SBML file,
which includes all molecular species, reactions schemes, and parameters.
The initial condition is defined in a seperate file as described above.
Below is the command line parameters should be provided.
buildStateSpace m mToggle.xml i init.txt s statespace.txt How to generate the rate matrix using net2matrix
The net2matrix program is designed to
generate the transition rate matrix from finite state space enumerated using the
buildStateSpace program described above.
The net2matrix takes the same SBML file
and the enumerated state space file as inputs, and outputs the generated transition rate matrix.
net2matrix m mToggle.xml s statespace.txt t transmatrix.txt How to solve the steady state probability landscapes?
A number of numerical solvers can be used to efficiently solve the steady state probability
distribution of the dCME using the transition rate matrix generated using
the net2matrix program.
In this package, we included an iterative solver program
ssor
based on the Successive Over Relaxation method (SOR).
Other methods, such as BiCGSTAB and GMRES, can be easily applied.
The ssor program takes the transition
rate matrix generated using the net2matrix program
as the only input, and computes the steady state probability distribution over the enumerated
finite state space.
ssor m transmatrix.txt s steadystate.txt How to compute the timeevolving probability landscapes?
The mxexp program in the FBdCME
can compute the timeevolving probability landscapes of the dCME
based on the Expokit package.
The mxexp program takes the transition
rate matrix generated using the net2matrix program
as an input along with the specified the lengths of total time and time interval,
and it outputs a probability distribution for each time interval to the specified directory.
mxexp m transmatrix.txt t 300000 d 10 p inputd1.txt i ./fptd_dt10/ How to parse the probability distributions and visualize?
The probability distribution can be projected as a probability landscape onto a 1D or 2D plane of biologically interesting molecular species. Simple Perl scripts have been developed to parse the resulting probability landscapes and generate the desired 1D and 2D projected landscapes. These projected landscapes can be visualized using the R plotting scripts. All these scripts can be found in the example/scripts folder of the package, and they can be easily adapted to parse and plot different molecular species. References:(Please cite following papers for using Finite Buffer dCME) 1. Youfang Cao, HsiaoMei 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. 2. 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. 



© All rights reserved. Liang Lab 2014 