Variability and stochasticity in Systems and Synthetic Biology
Biochemical systems evolve stochastically in time as a result of both the network’s inherent reactions and fluctuations due to the cell’s highly dynamic environment. This stochasticity leads to both advantageous and detrimental cell phenotypes depending on the reactions that exploit these noise sources. Computer simulations of large stochastic biochemical networks are often extremely time consuming due to the number of reactions that are required to take place. It is therefore desirable to develop an efficient approach to help us understand how stochastic fluctuations impact biological networks. In this work, the validity of an analytical method is shown that captures the effects of both intrinsic stochasticity and fluctuations from the cellular environment on the system properties of small biochemical networks.
The method is based on an approximation of the chemical master equation (CME) known as the linear noise approximation (LNA), and is valid for external fluctuations that are slower than the dynamics of internal processes. The analytical nature of the method makes it several orders of magnitude faster than simulation-based approaches. This allows us to predict effects of extrinsic fluctuations on a network’s ability to maintain function, reliability of signal transduction, and could provide insights into the design principles of synthetic networks.