Extrinsic noise passing through a Michaelis-Menten reaction: a universal response of a genetic switch.
The study of biochemical pathways usually focuses on a small section of a protein interactions network. Two distinct sources contribute to the noise in such a system: intrinsic noise, inherent in the studied reactions, and extrinsic noise generated in other parts of the network or in the environment. We study the effect of extrinsic noise entering the system through a nonlinear uptake reaction which acts as a nonlinear filter. Varying input noise intensity varies the mean of the noise after the passage through the filter, which changes the stability properties of the system. The steady-state displacement due to small noise is independent on the kinetics of the system but it only depends on the nonlinearity of the input function. For monotonically increasing and concave input functions such as the Michaelis-Menten uptake rate, we give a simple argument based on the small-noise expansion, which enables qualitative predictions of the steady-state displacement only by inspection of experimental data: when weak and rapid noise enters the system through a Michaelis-Menten reaction, then the graph of the system's steady states vs. the mean of the input signal always shifts to the right as noise intensity increases. We test the predictions on two models of lac operon, where TMG/lactose uptake is driven by a Michaelis-Menten enzymatic process. We show that as a consequence of the steady state displacement due to fluctuations in extracellular TMG/lactose concentration the lac switch responds in an asymmetric manner: as noise intensity increases, switching off lactose metabolism becomes easier and switching it on becomes more difficult.
Ochab-Marcinek
Anna
A
Department of Soft Condensed Matter, Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland. ochab@ifka.ichf.edu.pl
D000465:Algorithms; D001671:Biochemistry / Q000379:methods; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004926:Escherichia coli / Q000378:metabolism; D053263:Gene Regulatory Networks; D007700:Kinetics; D007785:Lactose / Q000737:chemistry / Q000378:metabolism; D008954:Models, Biological; D008957:Models, Genetic; D008962:Models, Theoretical; D016011:Normal Distribution; D011336:Probability; D012441:Saccharomyces cerevisiae / Q000378:metabolism
D007785:Lactose