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Komarova et al. (2005), Specificity in Cell Signaling

November 2007, model of the month by Dominic P. Tolle
Original model: BIOMD0000000125

Interconnected signalling pathways

Figure 1, from [1]

Biological organisms react to a vast array of different signals coming from both, external and internal sources. Signalling pathways are utilised to manage the various cues and bring about the required response. Often the same signalling machinery is shared amongst the many signalling pathways. The MAP kinase cascade in Saccharomyces cerevisiae, for example, is involved in mating, invasive growth and the osmotic stress response. With limited components used in numerous pathways, the question arises how signal specificity is maintained? How does a given input effect a given output while avoiding spurious output due to signalling cross-talk?

Komarova et al. [1] present a theoretical framework for addressing the issue of cross-talk in interconnected signalling pathways. Figure 1A shows an example of two connected signalling pathways, X and Y. The elements x0 and y0 represent each pathway's input, x1 and y1 represent the effector molecules and x2 and y2 are the respective pathways outputs. Pathway Y leaks into pathway X, as is shown by the arrow connecting node y1 to node x2. This leak exemplifies undesired signalling pathway cross-talk, as can be brought about by lack of substrate specificity of signalling molecules. It causes the signal input from one pathway, y0, to elicit the output of another pathway, x2 (Figure 1B).

The Authors define two properties of a signalling pathway, specificity, S, and fidelity, F. Specificity is the ratio of a pathway's authentic output to its spurious output (Figure 1C, top). Specificity of a pathway is said to be complete if there is no undesired activation of elements not contained within that pathway by the pathways input. In the example network in Figure 1A, the specificity for pathway X with respect to pathway Y in response to the input signal x0 is complete. Signal y0, on the other hand, gives rise to output x2 due to the cross-talk of components y1. If S < 1, y0 would elicit a stronger x2 output signal than y2 output signal. Fidelity is the ratio of a pathway's output when given an authentic signal to its output when given a spurious signal (Figure 1C, bottom). Again, a pathway's fidelity is complete if its output is only brought about by its authentic input, as happens in pathway Y. Pathway X exhibits finite fidelity. If F < 1, x2 would be activated more by y0 than by x0. The specificity of the entire network is then defined as the product of the pathway specificities and can be used as a metric to measure the specificity intrinsic to the network architecture.

In a model of a network of pathways with shared components, termed the basic architecture (Figure 2A), the authors show that the pathways' specificities are independent of upstream elements or elements at the level of the shared component, whereas the pathways' fidelities depend strongly on upstream elements. As such, specificity and fidelity can be independently modulated. Additionally the individual pathway fidelities and specificities are reciprocals of each other and one cannot be increased without decreasing the other. Compartmentalisation and the use of scaffold proteins are used to bestow specificity and fidelity to pathways (Figures 2D and 2E). Network specificity can be maximised if the exchange rates, in the form of compartmental diffusion exchange and scaffolding binding and unbinding rates respectively, are small in comparison to the deactivation rates of the signalling components.

Many biological pathways interconnect into large networks. Signal cross-talk needs to be minimised to allow organisms to respond to the various signals in the appropriate way. A theoretical framework for the analysis of pathway and network specificity is an important tool in understanding how this can be achieved.


Figure 2, from [1]

Bibliographic References

  1. Komarova NL, Zou X, Nie Q, Bardwell L. A theoretical framework for specificity in cell signaling. Mol Syst Biol, 1:2005.0023, 2005. [SRS@EBI]