Abstract
BackgroundBiochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor.ResultsWe implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased.ConclusionsWe concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.
Highlights
Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner
Deterministic modeling of biochemical reaction network (BRN) In the previous, we have described how biochemical systems containing only small numbers of molecules can be modeled in detail
We propose that the frequency domain analysis for noise should always be performed when BRNs are modeled using stochastic approaches
Summary
Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Noise plays an essential role in most biochemical systems. It derives from reaction events that are discrete and occur at random times. The structure of a particular biochemical reaction network (BRN) determines the way the system evolves and defines the quality of noise . There exist several types of noise processes which occur in these systems. Knowing the characteristics of noise processes would help develop better models and understand the underlying principles of the biological phenomena better
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