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  • Review Article
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Non-transcriptional regulatory processes shape transcriptional network dynamics

Nature Reviews Microbiology volume 9, pages 817–828 (2011)Cite this article

Subjects

Key Points

  • The performance of bacterial transcriptional regulatory networks is often affected by post-transcriptional, post-translational and pleiotropic effects.

  • Despite their importance, non-transcriptional effects are often obscure or difficult to characterize without quantitative analytical techniques.

  • Feedback loops can arise via non-transcriptional interactions, and these loops have important effects on signal processing.

  • Stress-response networks, cell cycle regulators and small RNA-mediated control of gene expression are examples of bacterial signalling networks that depend strongly on non-transcriptional interactions.

  • Mathematical network analysis techniques used in combination with quantitative experimental approaches can reveal how non-transcriptional processes contribute to complex dynamic phenotypes.

  • Synthetic biological networks are a powerful tool for studying the role of non-transcriptional effects in natural networks. Synthetic networks are well defined and easily manipulated. Recent advances in synthetic-network design underscore the importance of non-transcriptional effects.

  • Synthetic-network construction complemented by quantitative network analysis will speed discovery and deepen our understanding of the fundamental organizing principles of biology.

Abstract

Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.

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Figure 1: Information flow in signalling networks can strongly depend on non-transcriptional details, with important physiological consequences.
Figure 2: Saturation creates an ultrasensitive switch.
Figure 3: Modulation of growth rate can create an implicit feedback loop with two resulting subpopulations of bacteria.
Figure 4: Complex feedback architecture with non-transcriptional interactions enables complex dynamic responses.
Figure 5: Engineering non-transcriptional processes for synthetic biology.

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Acknowledgements

The authors thank G. Balázsi, M. Laub, M. Bennett and M. Gennaro for useful comments on manuscript drafts and P. Lund for sharing his data for figure 4. This work is supported by grant R01-GM096189-01 from the US National Institutes of Health (O.A.I.).

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Authors and Affiliations

  1. Department of Bioengineering, Rice University, 6100 Main St., Houston, 77005, Texas, USA

    J. Christian J. Ray, Jeffrey J. Tabor & Oleg A. Igoshin

  2. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA

    J. Christian J. Ray

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Glossary

Networks

Sets of biochemical reactions or interactions that are employed for information processing in the cell. The term network can refer to either interactions on the whole-cell level or smaller circuits (subsystems) within the larger network.

Signal

In the context of this Review, the information that flows through a biological network. In a wider context, biological signals can take a variety of forms.

Nodes

Molecular entities, such as transcription factors or allosterically regulated enzymes, that take in a signal and then output a signal in response. When a node is described as upstream or downstream, this refers to its order in the information flow.

Pleiotropic

Of an interaction: in which one component or effect simultaneously affects many targets. In this Review, we refer to effects originating from coupling with global physiological processes in the cell.

Ultrasensitivity

A type of signal–response curve characterized by a high slope in the responsive range.

Michaelis–Menten kinetics

A model of enzyme kinetics that is often used to mathematically represent first-order saturation processes, in which the flux (V) is determined by the equation:

(in which [x] is the concentration of substrate or regulator x, Vmax is the maximum flux rate and Km is the Michaelis–Menten constant).

Hill kinetics

A generalization of Michaelis–Menten kinetics that allows a mathematical representation of higher-order, or cooperative, processes in which the flux

has nth-order effective cooperativity ([x] is the concentration of substrate or regulator x, Vmax is the maximum flux rate, Km is the Michaelis–Menten constant and n is the Hill coefficient).

Effective cooperativity

A measure of sensitivity: how much one molecular species affects the production of another.

Bistable switch

A system in which there are two stable steady states under the same conditions, as reflected in the signal–response curve. Which state the system adopts in practice depends on the initial conditions and noise.

Bet hedging

An evolved phenotype that employs heterogeneity to ensure that distinct subsets of a cellular population are adapted to different outcomes of an unpredictable future environment.

Noise

Variability in signals and responses from cell to cell that arises either intrinsically, from the nature of the physicochemical processes, or from extrinsic variability such as randomness in ribosome inheritance.

Jacobian matrix

A matrix for which the entries quantitate the sensitivity of each variable (often corresponding to chemical species) to each other variable.

Implicit feedback loop

A feedback loop for which its existence is not obvious, but which emerges from non-transcriptional interactions.

Toxin–antitoxin system

A small gene network that typically includes one gene encoding a toxin and another encoding a neutralizing antitoxin.

Coupled feedback loops

Multiple feedback loops that interact in some way, such as being nested or resulting from a single regulatory event that modulates multiple transcriptionally coupled genes.

Dynamic performance

The characteristics of a response to a signal over time.

Biphasic

Of a response: composed of two distinct, characteristic types of dynamics that are separated in time, such as an initial transient phase and a long-term persistent phase.

Robustness

Insensitivity of a dynamic performance to small parameter perturbations that would arise from intrinsic or extrinic noise, slight environmental variations, and so on (for the purposes of this Review; the term has many subtly different meanings in systems biology).

Oscillator

A network architecture that results in periodic oscillations of an output.

Signal matching

Adjusting the amount of signal produced by an upstream node so that it is within the range to which a downstream node is responsive (unsaturated).

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Ray, J., Tabor, J. & Igoshin, O. Non-transcriptional regulatory processes shape transcriptional network dynamics. Nat Rev Microbiol 9, 817–828 (2011). https://doi.org/10.1038/nrmicro2667

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