The phenomena of stochasticity in biochemical processes have been intriguing
life scientists for the past few decades. We now know that living cells take
advantage of stochasticity in some cases and counteract stochastic effects in
others. The source of intrinsic stochasticity in biomolecular systems are
random timings of individual reactions, which cumulatively drive the
variability in outputs of such systems. Despite the acknowledged relevance of
stochasticity in the functioning of living cells no rigorous method have been
proposed to precisely identify sources of variability. In this paper we propose
a novel methodology that allows us to calculate contributions of individual
reactions into the variability of a system's output. We demonstrate that some
reactions have dramatically different effects on noise than others.
Surprisingly, in the class of open conversion systems that serve as an
approximate model of signal transduction, the degradation of an output
contributes half of the total noise. We also demonstrate the importance of
degradation in other relevant systems and propose a degradation feedback
control mechanism that has the capability of an effective noise suppression.
Application of our method to some well studied biochemical systems such as:
gene expression, Michaelis-Menten enzyme kinetics, and the p53 system indicates
that our methodology reveals an unprecedented insight into the origins of
variability in biochemical systems. For many systems an analytical
decomposition is not available; therefore the method has been implemented as a
Matlab package and is available from the authors upon request.