A deterministic-stochastic crossover algorithm for simulation of complex biochemical systems

Understanding biology at the system level has gained much more interest recently due to the rapid development in genome sequencing and high-throughput measurements. Mathematical descriptions of biological systems are normally formalized using two different approaches. The deterministic method is very efficient in predicting the overall behavior of the system but ignores the inherent fluctuations and correlations at lower concentration. The stochastic method captures the intrinsic randomness but is often computationally expensive. Our group has developed a deterministic-stochastic crossover algorithm for simulating biological networks. This method features with XML-based model representation, multiple compartments and events. More importantly, the improved algorithm is able to simulate Michaelis-Menten type reactions directly. Simulation on a complex glycolysis system showed that our algorithm retained the high efficiency of deterministic method and still reflected the random fluctuations at lower concentration. The ability of revealing the stochastic property with high efficiency makes our new algorithm useful for researches and applications based on systems biology.