Just as one metabolite is converted to another by a chain of chemical reactions called a metabolic pathway (chapter 4), so a stimulus is connected to cellular responses by a signalling pathway. A diagram showing all the cell's metabolic pathways would be enormously complicated; a diagram showing all its signalling pathways would be at least as complicated. Just as metabolic pathways branch and converge, so do signalling pathways. The cell's information circuitry has many interconnections; signalling pathways "talk" to one another incessantly, just as metabolic pathways do. Metabolic pathways contain feedback loops, where end-products stimulate or inhibit earlier reactions; signalling pathways also contain positive and negative feedback loops. So there is a close formal analogy between metabolic and signalling pathways.
The difference is one of function. Metabolic pathways make energy available to the cell or manufacture the cell's molecular components; signalling pathways convey information.
To illustrate the complexity of cellular signalling, we shall consider two hypothetical and very simple signalling pathways (Fig. 9-1). Each begins with a receptor (R1 or R2) being activated by a ligand or physical stimulus (s1
16 It is difficult to fit a single general model to all cases. For example, some receptor molecules protrude through the membrane. When the stimulus molecule binds to the exterior portion of such a molecule, the interior portion is changed. In such a case, R and M represent parts of the same molecule. However, the scheme shown in the text conveys the general idea.
or s2). Receptor activation transduces the signal to the respective intracellular molecules, Mi and M2. Each of these molecules initiates a branched signalling pathway: M1 via A, B, and two targets of B - C1 and C2; M2 via X, Y, and two targets of Y - Z1 and Z2. The first pathway leads via C2 to response RESP1; the second leads via Z2 to response RESP2. RESP1 and RESP2 might be changes in one or more aspects of internal state, or gene expression, or membrane behaviour. We have to bear in mind that many signalling pathways operate simultaneously in a cell, not just two.
Now let us consider how these two pathways might interact. Suppose stimulus s1 inhibits response RESP2, while s2 inhibits response RESP1. Fig. 9-2 shows just some of the ways in which s2 might inhibit the s1 signalling pathway. There are just as many ways in which si can interfere with the s2 pathway, but we have omitted these - they would have made the diagram unreadable.
Even in this highly simplified scheme, you can see how complex the cross-talk among signalling pathways can be. In real life, a cell responds to many different stimuli; each stimulus might evoke several responses and inhibit several others; and many signalling pathways are considerably longer and more extensively branched than we have suggested in the diagrams. Moreover, we have not shown any feedback loops in these schemes. It is little wonder that the study of cell signalling is a very active and challenging area of research in cell biology today.
Several authors have noted an analogy with electronic engineering. The signalling pathway intermediates (Mb A, B, C1/2 and M2, X, Y, Z1/2 in the diagrams) behave like computer logic gates (AND, OR, NOR, etc.). The arrows in the diagram show the ways in which these gates might be interconnected. Cells contain thousands of different sorts of proteins and as many as 25-50% of these might act as signalling pathway components. Such a huge array of logic components, with multiple inputs and outputs, looks like a recipe for chaos. In fact, the system resembles a neural network comprising many interconnected parallel processing pathways; and like a neural network, the cell's signalling system has relatively few stable states despite its complexity. It is not even necessary for all individual components to function precisely. There are so many parallel pathways, i.e. there is so much redundancy in the system, that the components can compensate for one another. The response system as a whole functions precisely. Stuart Kauffman showed that a network as cross-connected as the signalling system of a living cell is likely to generate a small number of stable states rather than chaos. Interestingly, Kauffman claims that the number of attractors (stable states) of any such network is similar to the number of distinct cell types in the organism.
A system of this kind is capable of learning. Exposure to a given combination of stimuli activates17 some signalling pathways and inhibits others. As a result, responses appropriate to the cell's or organism's needs are evoked. After the response has begun, the cell still contains a specific pattern of activated and inactivated signalling components. This pattern might persist in the short term. If some of the stimuli are repeated while the pattern lasts, the same responses will be evoked. Therefore, signalling pathways confer a kind of short-term memory on the cell.
This is the second time we have used a computer analogy in this book. We compared the gene with an analogue computer in chapter 7. Now we have compared the stimulus-induced signalling pathways of the cell with a neural network. We shall return to these analogies in chapter 18.
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