Bulletin of Mathematical Biology

, Volume 78, Issue 4, pp 617–661 | Cite as

State Space Truncation with Quantified Errors for Accurate Solutions to Discrete Chemical Master Equation

  • Youfang Cao
  • Anna Terebus
  • Jie LiangEmail author
Original Article


The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space, truncation of the state space is necessary for solving most dCMEs. It is therefore important to assess the consequences of state space truncations so errors can be quantified and minimized. Here we describe a novel method for state space truncation. By partitioning a reaction network into multiple molecular equivalence groups (MEGs), we truncate the state space by limiting the total molecular copy numbers in each MEG. We further describe a theoretical framework for analysis of the truncation error in the steady-state probability landscape using reflecting boundaries. By aggregating the state space based on the usage of a MEG and constructing an aggregated Markov process, we show that the truncation error of a MEG can be asymptotically bounded by the probability of states on the reflecting boundary of the MEG. Furthermore, truncating states of an arbitrary MEG will not undermine the estimated error of truncating any other MEGs. We then provide an overall error estimate for networks with multiple MEGs. To rapidly determine the appropriate size of an arbitrary MEG, we also introduce an a priori method to estimate the upper bound of its truncation error. This a priori estimate can be rapidly computed from reaction rates of the network, without the need of costly trial solutions of the dCME. As examples, we show results of applying our methods to the four stochastic networks of (1) the birth and death model, (2) the single gene expression model, (3) the genetic toggle switch model, and (4) the phage lambda bistable epigenetic switch model. We demonstrate how truncation errors and steady-state probability landscapes can be computed using different sizes of the MEG(s) and how the results validate our theories. Overall, the novel state space truncation and error analysis methods developed here can be used to ensure accurate direct solutions to the dCME for a large number of stochastic networks.


Stochastic biological networks Discrete chemical master equation State space truncation 



This work is supported by NIH Grant GM079804, NSF Grant MCB1415589, and the Chicago Biomedical Consortium with support from the Searle Funds at The Chicago Community Trust. We thank Dr. Ao Ma for helpful discussions and comments. YC is also supported by the LDRD program of CNLS at LANL.


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Copyright information

© Society for Mathematical Biology 2016

Authors and Affiliations

  1. 1.Department of BioengineeringUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Theoretical Biology and Biophysics (T-6), Center for Nonlinear Studies (CNLS)Los Alamos National LaboratoryLos AlamosUSA

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