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Towards genome-scale signalling-network reconstructions

Nature Reviews Genetics volume 11, pages 297–307 (2010)Cite this article

Subjects

Key Points

  • Signalling networks convert observations about the environment into chemical and physical representations that ultimately modulate cellular behaviour.

  • Signalling networks are comprised of sensors, transducers and actuators.

  • The more transducers there are between a sensor and its cognate actuator, the greater the opportunity for a signal to be influenced by other signals.

  • Qualitative modelling approaches provide insights into how a signal is propagated through or integrated into a network.

  • Signalling-network reconstructions represent our knowledge regarding the network in a format that is amenable to conversion into mathematical models.

  • Signalling networks are comprised of components that are expressed in temporal fashion and may be distributed heterogeneously.

  • Signalling networks evolve to aid the organism in which they are located to overcome its specific challenge. Therefore, despite a high degree of similarity of parts, related organisms may have substantially different signalling networks.

  • The symbolic nature of signals makes it easier for signalling networks to be rewired by evolution than is the case for metabolic networks.

Abstract

Biological signalling networks allow living organisms to issue an integrated response to current conditions and make limited predictions about future environmental changes. Small-scale dynamic models of signalling cascades, including mitogen-activated protein kinase cascades, have been developed to generate hypotheses about signal transduction. Owing to technical limitations, these models and the hypotheses they generate have focused on a limited subset of signalling molecules. Now that we can simultaneously measure a substantial portion of the molecular components of a cell, we can begin to develop and test systems-level models of cellular signalling and regulatory processes, therefore gaining insights into the 'thought' processes of a cell.

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Figure 1: Pathways, circuits, components and modularity in signalling networks.

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Acknowledgements

This work was supported in part by the US National Institute of Allergy and Infectious Diseases and the US Department of Health and Human Services through interagency agreement Y1-AI-8401-01.

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  1. Department of Bioengineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, 92093-0412, California, USA

    Daniel R. Hyduke & Bernhard Ø. Palsson

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  1. Daniel R. Hyduke
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Correspondence to Daniel R. Hyduke.

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Competing interests

Bernhard Ø. Palsson serves on the scientific advisory board of Genomatica, Inc.

Related links

Related links

DATABASES

The Alliance for Cellular Signaling

BioGRID Interaction Database

Database of Quantitative Cellular Signaling

DOMINE (database of protein domain interactions)

IUPHAR database of receptors and ion channels

Kyoto Encyclopedia of Genes and Genomes Pathway Database

SNAPPI (Structures, Interfaces and Alignments for Protein–Protein Interactions)

UCSD–Nature Signaling Gateway

FURTHER INFORMATION

Daniel R. Hyduke's homepage

Bernhard Ø. Palsson's homepage

CellNetAnalyser

COBRA Toolbox

Nature Reviews Genetics article series on Modelling

Glossary

Biological network

A set of biological entities that act in an integrated fashion. Typical components of biological networks include organisms (for example, ecosystems, biofilms, and host–parasite or symbiont relationships), tissues (for example, the integrated operations of lungs, brain and heart), cells (for example, biofilms and different cells in a tissue) and the molecular components of cells (for example, proteins, DNA and RNA). Here, we focus on the molecular components of cells.

Interactome

A set of molecular components of the cell, such as proteins, and the interactions between them. The interactions can be physical (protein A binds protein B) or correlative (perturbing protein A alters protein B's activity).

Synthetic lethal

A genetic interaction in which the deletion of two genes at the same time results in lethality. An organism in which one gene is deleted and the other gene is present will still be viable.

Formalism

A description of a property of a process in mathematical terms.

Modules

A set of components that work in an integrated fashion. Personal computers are modular systems: they have keyboards, displays, motherboards and hard drives, each of which represents a module. Each module is relatively easy to replace but is composed of an integrated set of components that may be difficult to replace.

Reverse engineering

The process of discovering the technological principles of a device, object or system through analysis of its structure, function and operation. It often involves taking a system apart and analysing its workings with the aim of making a new device or program that does the same thing without using any physical part of the original.

Manual curation

In the context of this Review, this is the process by which a researcher assesses whether a given research paper possesses information that is relevant to a network under study and then examines the evidence put forth in the paper. This is opposed to artificial intelligence-guided text parsing or assuming a causal interaction based on correlative evidence in omics data sets.

Serovar

A group of related microorganisms that are classified based on a characteristic set of antigens.

Structural analysis

In the context of signalling networks, structural analysis focuses on exploring the connectivity of elements in the network and identifying routes from inputs to outputs. Structural analysis serves to construct a 'road map' of a network and then identify plausible routes from point A to point B. Connectivity refers to the number of different elements in the network with which a particular component may interact.

Genetic interaction

An interaction in which one gene product alters the phenotypic effect of a second gene product. The most common phenotype of interest for genetic interaction studies is growth. Here, the interactions are observed through gene-deletion studies in which the growth phenotype of a single deletion is compared with the growth phenotype of a double deletion.

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Hyduke, D., Palsson, B. Towards genome-scale signalling-network reconstructions. Nat Rev Genet 11, 297–307 (2010). https://doi.org/10.1038/nrg2750

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