Number Of Messenger RNA Research Articles

Resilient circadian oscillator revealed in individual cyanobacteria.

Circadian oscillators, which provide internal daily periodicity, are found in a variety of living organisms, including mammals, insects, plants, fungi and cyanobacteria. Remarkably, these biochemical oscillators are resilient to external and internal modifications, such as temperature and cell division cycles. They have to be 'fluctuation (noise) resistant' because relative fluctuations in the number of messenger RNA and protein molecules forming the intracellular oscillators are likely to be large. In multicellular organisms, the strong temporal stability of circadian clocks, despite molecular fluctuations, can easily be explained by intercellular interactions. Here we study circadian rhythms and their stability in unicellular cyanobacteria Synechoccocus elongatus. Low-light-level microscopy has allowed us to measure gene expression under circadian control in single bacteria, showing that the circadian clock is indeed a property of individual cells. Our measurements show that the oscillators have a strong temporal stability with a correlation time of several months. In contrast to many circadian clocks in multicellular organisms, this stability seems to be ensured by the intracellular biochemical network, because the interactions between oscillators seem to be negligible.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks

BackgroundIntrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks.ResultsWe have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from . BioNetS also can be run as a stand alone package. All the required files are accessible from .ConclusionsWe have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.