The ACME stands for the Accurate Chemical Master Equation method. The ACME method was developed by Drs. Jie Liang and Youfang Cao and colleagues in the University of Illinois at Chicago. The ACME provides a novel approach to directly solve the probability landscapes of stochastic biological networks from the underlying Chemical Master Equation (CME). The ACME does not require Gillespie simulations and Langevin approximations. It efficiently and optimally enumerates the finite state space up to a controlled truncation error. The ACME can solve the steady state probability landscape, time evolution probability landscapes, and the rare event first passage time distributions (FPTD) between any two states in stochastic biological networks. The ACME provides a powerful method for studying stochastic behavior of gene regulatory networks and circuits in systems biology and synthetic biology.
The ACME (formerly known as the finite buffer method) has been successfully applied to study many important biological processes, such as the cell fate determination in phage lambda infected E. coli cell (Cao, Lu, and Liang 2010), bistable genetic toggle switch (Cao, Terebus, and Liang 2016), MAPK cascade (Cao, Terebus, and Liang 2016), and the enzymatic futile cycle (Cao and Liang 2013). The ACME method can be potentially applied to study broad issues in systems biology, as well as any cellular processes controlled by stochastic regulatory networks, such as the regulation of stem cell development and differentiation, immune responses, and cell cancerogenesis.
The ACME software is open source. We provide downloads of the ACME source code, binary code for Windows, as well as an online simulation tool hosted on NanoHub.org. We also provide the Documentation and Example projects for ACME.