Noise-based switches and amplifiers for gene expression

Abstract

The regulation of cellular function is often controlled at the level of gene transcription. Such genetic regulation usually consists of interacting networks, whereby gene products from a single network can act to control their own expression or the production of protein in another network. Engineered control of cellular function through the design and manipulation of such networks lies within the constraints of current technology. Here we develop a model describing the regulation of gene expression and elucidate the effects of noise on the formulation. We consider a single network derived from bacteriophage and construct a two-parameter deterministic model describing the temporal evolution of the concentration of repressor protein. Bistability in the steady-state protein concentration arises naturally, and we show how the bistable regime is enhanced with the addition of the first operator site in the promotor region. We then show how additive and multiplicative external noise can be used to regulate expression. In the additive case, we demonstrate the utility of such control through the construction of a protein switch, whereby protein production is turned ''on'' and ''off'' by using short noise pulses. In the multiplicative case, we show that small deviations in the transcription rate can lead to large fluctuations in the production of protein, and we describe how these fluctuations can be used to amplify protein production significantly. These results suggest that an external noise source could be used as a switch and͞or amplifier for gene expression. Such a development could have important implications for gene therapy.R egulated gene expression is the process through which cells control fundamental functions such as the production of enzymatic and structural proteins and the time sequence of this production during development (1, 2). Many of these regulatory processes take place at the level of gene transcription (3), and there is evidence that the underlying reactions governing transcription can be affected by external influences from the environment (4).Because experimental techniques are increasingly capable of providing reliable data pertaining to gene regulation, theoretical models are becoming important in the understanding and manipulation of such processes. The most common theoretical approach is to model the interactions of elements in a regulatory network as biochemical reactions. Given such a set of chemical reactions, the individual jump processes (i.e., the creation or destruction of a given reaction species) and their associated probabilities are considered. In its most general form, this approach often leads to a type of Monte Carlo simulation of the interaction probabilities (5). Although this approach suffers from a lack of analytic tractability, its strength is its completeness-fluctuations in species' concentrations are embedded in the modeling process. These internal fluctuations are important for systems containing modest numbers of elements, or when the volume is small.Rate equations or...

Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics

“…Boolean networks (Glass & Kauffman 1973), and continuous approaches, relying on ordinary or stochastic differential equations (Smith 1987;Mahaffy et al 1992;Mestl et al 1996;Chen et al 1999Chen et al , 2005. The relevance of noise has been experimentally demonstrated (Haitzler & Simpson 1991;Ko 1992;Guptasarma 1995;Fiering et al 2000;Hasty et al 2000).…”

The large-scale organization of metabolic networks

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An important goal in biology is to uncover the fundamental design principles that provide the common underlying structure and function in all cells and microorganisms [6][7][8][9][10][11][12][13] . For example, it is increasingly appreciated that the robustness of various cellular processes is rooted in the dynamic interactions among its many constituents [14][15][16] , such as proteins, DNA, RNA, and small molecules.

Recent scientific developments improve our ability to identify the design principles that integrate these interactions into a complex system.

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A rigorous framework for multiscale simulation of stochastic cellular networks

“…Next, we use the RRA to simulate a bistable system developed by Hasty et al 34 as an example to discuss noisebased switches and amplifiers for gene expression. The biochemical reactions are given by…”