A Hybrid of the Chemical Master Equation and the Gillespie Algorithm for Efficient Stochastic Simulations of Sub-Networks

• Published in: PloS one 2016-03, Vol.11 (3), p.e0149909-e0149909
• Main Author: Albert, Jaroslav
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Approximation; Approximations; Biology; Biology and Life Sciences; Chemical reactions; Computer and Information Sciences; Computer memory; Computer Simulation; Gene expression; Gene Expression Regulation; Gene Regulatory Networks; Humans; Methods; Modelling; Models, Biological; Models, Genetic; Physical Sciences; Physics; Promoter Regions, Genetic; Research and Analysis Methods; RNA, Messenger - genetics; Stochastic Processes; Stochastic systems; Stochasticity; Storage capacity; Switching theory; Systems Biology - methods
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0149909

Modeling stochastic behavior of chemical reaction networks is an important endeavor in many aspects of chemistry and systems biology. The chemical master equation (CME) and the Gillespie algorithm (GA) are the two most fundamental approaches to such modeling; however, each of them has its own limitations: the GA may require long computing times, while the CME may demand unrealistic memory storage capacity. We propose a method that combines the CME and the GA that allows one to simulate stochastically a part of a reaction network. First, a reaction network is divided into two parts. The first part is simulated via the GA, while the solution of the CME for the second part is fed into the GA in order to update its propensities. The advantage of this method is that it avoids the need to solve the CME or stochastically simulate the entire network, which makes it highly efficient. One of its drawbacks, however, is that most of the information about the second part of the network is lost in the process. Therefore, this method is most useful when only partial information about a reaction network is needed. We tested this method against the GA on two systems of interest in biology--the gene switch and the Griffith model of a genetic oscillator--and have shown it to be highly accurate. Comparing this method to four different stochastic algorithms revealed it to be at least an order of magnitude faster than the fastest among them.