Principles of cell signaling - UT Southwestern

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39057_ch14_cellbio.qxd 8/28/06 5:11 PM Page 596 RECEPTORS TRANSDUCERS EFFECTORS Convergent and divergent signaling pathways Linear, parallel Convergent Divergent Multiply branched FIGURE 14.4 Signaling pathways use convergent and divergent branching to coordinate information flow. The diagrams at top show how even a simple, threelevel signaling network can sort information. Convergence or divergence can take place at multiple points along a signaling pathway. As an example of complexity, the lower portion of the figure shows a small segment (~10%) of the G protein-mediated signaling network in a mouse macrophage cell line. It omits several interpathway regulatory mechanisms and completely ignores inputs from non-G protein-coupled receptors. Pathway map courtesy of Lily Jiang, University of Texas Southwestern Medical Center. maps. Signaling networks are also spatially complex. They may include components in various subcellular locations, with initial receptors and associated proteins in the plasma membrane, but with downstream proteins in the cytoplasm or intracellular organelles. Such complexity is necessary to allow the cells to integrate and sort incoming signals and to regulate multiple intracellular functions simultaneously. The complexity and adaptability of signaling networks, like the one shown in the lower half of Figure 14.4, make their dynamics at the whole-cell level difficult or impossible to grasp intuitively. Signaling networks resemble large analog computers, and investigators are increasingly depending on computational tools to understand cellular information flow and its regulation. First, many signaling interactions that include only two or three proteins exert functions analogous to traditional computational logic circuits (see the next section). The theory and experience with such circuits in electronics facilitate understanding biological signaling functions as well. The enormous complexity of cellular signaling networks can be simplified by considering them to be composed of interacting signaling modules, i.e., groups of proteins that process signals in well-understood ways. A cellular signaling module is analogous to an integrated circuit in an electronic instrument that performs a known function, but whose exact components could be changed for similar use in another device. The concept of modular construction facilitates both qualitative and quantitative understanding of signaling networks. We will refer to many standard signaling modules later in the chapter. Examples include monomeric and heterotrimeric G protein modules, MAPK cascades, tyrosine (Tyr) kinase receptors and their binding proteins, and Ca2+ release/uptake modules. In each case, despite the numerous phylogenetic, developmental, and physiologic variations, understanding the basic function of that class of module conveys understanding of all its incarnations. Last, the evolutionary importance of modules is significant; once the architecture of a module is established it can be reused. For larger-scale networks, multiplexed, high-throughput measurements on living cells have been combined with powerful kinetic modeling strategies to allow an increasingly accurate quantitative depiction of information flow within signaling modules or entire networks. Such models, with sound and experimentally based parameter sets, can describe signaling processes in systems too complex for intuitive or ad hoc analysis. They are also vital as tests of understanding because they can predict experimental results in ways that can be used to test the validity of the model. Well-grounded models can then be used (cautiously) to suggest the mechanisms of systems for which data sets remain unattainable. At even greater levels of complexity, the theories and tools of computer science are increasingly giving useful systemslevel analyses of signal flow in cells. Using computational tools to analyze large arrays of quantitative data allows us to understand cellular information flow and its regulation. 596 CHAPTER 14 Principles of cell signaling
39057_ch14_cellbio.qxd 8/28/06 5:11 PM Page 597 Developing quantitative models of signaling networks is a frontier in signaling biology. These models both help describe network function and pinpoint experiments to clarify mechanism. 14.7 Cellular signaling pathways can be thought of as biochemical logic circuits Key concepts • Signaling networks are composed of groups of biochemical reactions that function as mathematical logic functions to integrate information. • Combinations of such logic functions combine as signaling networks to process information at more complex levels. As introduced in the preceding section, processes that signaling pathways use to integrate and direct information to cellular targets are strikingly analogous to the mathematical logic functions that are used to design the individual circuits of electronic computers. Indeed, there are biological equivalents of essentially all of the functional components that computer scientists and engineers consider in the design of computers and electronic control devices. To understand signaling pathways, it is, therefore, useful to consider groups of reactions within a pathway as constituting logic circuits of the sort used in electronic computing, as illustrated in FIGURE 14.5. The simplest example is when two stimulatory pathways converge. If sufficient input from either is adequate to elicit the response, the convergence would constitute an “OR” function. If neither input is sufficient by itself but the combination of the two elicits the response, then the converging pathways would create “AND” functions. AND circuits are also referred to as coincidence detectors—a response is elicited only when two stimulating pathways are activated simultaneously. AND functions can result from the combination of two similar but quantitatively inadequate inputs. Alternatively, two mechanistically different inputs might both be required to elicit a response. An example of the latter would be a target protein that is allosterically activated only when phosphorylated, or that is activated by phosphorylation but is only functional when recruited to a specific subcellular location. The opposite of an AND circuit is a NOT function, where one pathway blocks the stim- Logical (Boolean) A B A + B A B A + B A B A + B A OR B A AND B A NOT B Response Response Response Response Response Simple logic circuits Response Quantitative (Analog) A + fixed [B] Response Response Additive ulatory effect of another. Simple logic gates are observed at many locations in cellular signaling pathways. We can also think about convergent signaling in quantitative rather than Boolean terms by considering the additivity of inputs to a distinct process (see Figure 14.5, right). The OR function referred to above can be considered to be the additive positive inputs of two pathways. Such additivity could represent the ability of several receptors to stimulate a pool of a particular G protein or the ability of two protein kinases to phosphorylate a single substrate. Additivity may be positive, as in the examples above, or negative, such as when two inhibitory inputs combine. Inhibition and stimulation may also combine additively to yield an algebraically balanced output. Alternatively, multiple inputs can combine with either more or less than an additive effect. The NOT function, discussed above, is analogous to describing a blockade of stimulation. The AND function describes synergism, where one input potentiates another but alone has little effect. Even simple signaling networks can display complex patterns of information processing. One A log (agonist concentration) More than additive log (agonist concentration) Less than additive log (agonist concentration) B A + B A B A A + B B FIGURE 14.5 Signaling networks use simple logic functions to process information. Boolean OR, AND, and NOT functions (left) correspond to the quantitative interactions between converging signals that are shown on the right. 14.7 Cellular signaling pathways can be thought of as biochemical logic circuits 597
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Principles of cell signaling - UT Southwestern

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