Bioengineering Models of Cell Signaling

Asthagiri, Anand R.; Lauffenburger, Douglas A.

doi:10.1146/annurev.bioeng.2.1.31

Cited by 132 publications

(87 citation statements)

References 107 publications

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“…Elucidating the functional and structural relationships between these components remains a major challenge. [86][87][88] Even after evidence for physical association is obtained by techniques such as the two-hybrid system, the order in which these interactions occur and their kinetics must be explored. Strategies that provide insight into complex cellular processes are needed.…”

Section: Challenges To Studying Receptor Functionmentioning

confidence: 99%

Synthetic Multivalent Ligands as Probes of Signal Transduction

2006

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Cell surface receptors acquire information from the extracellular environment and coordinate intracellular responses. Evidence from biochemical and structural studies indicates that many receptors do not operate as individual entities, but rather as part of higher-order complexes (e.g. dimers and oligomers). Coupling the functions of multiple receptors may endow signaling pathways with the sensitivity and malleability required to govern cellular responses. Moreover, multireceptor signaling complexes may provide a means of spatially segregating otherwise degenerate signaling cascades. Despite the proposed importance of receptor-receptor processes in cellular signaling, questions concerning the mechanisms, extent, and consequences of receptor co-localization and interreceptor communication remain unanswered.Chemical synthesis can provide a variety of compounds with which to address the role of receptor assembly in signal transduction. The focus of this review is one such approach -the use of synthetic multivalent ligands to characterize receptor function. Multivalent ligands can be generated that possess a variety of sizes, shapes, valencies, orientations, and densities of binding elements. Their unique architectures imbue multivalent ligands with the ability to access binding modes not available to monovalent compounds. Multivalent ligands, therefore, are capable of illuminating aspects of inter-receptor processes that are not readily probed using conventional approaches. We suggest that, as focus shifts from investigations of the function of individual proteins and toward the analysis of multi-receptor signaling complexes, multivalent ligands will become even more valuable tools with which to ask sophisticated mechanistic questions. Further, multivalent ligands may provide new opportunities for manipulating receptor systems for the deconvolution of pathways, diagnosis, and, ultimately, the treatment of disease.

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Section: Challenges To Studying Receptor Functionmentioning

confidence: 99%

Synthetic Multivalent Ligands as Probes of Signal Transduction

2006

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“…The complexity of these networks has precluded comprehensive quantitative analysis of signaling. However, the networks can be divided into discrete signaling modules that can be quantitatively modeled and analyzed independently (1)(2)(3)(4)(5). Later, such models of modules can be experimentally verified and then reconnected to build larger signaling networks.…”

mentioning

confidence: 99%

Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction

Bornheimer

Maurya

Farquhar

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

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Heterotrimeric G protein signaling is regulated by signaling modules composed of heterotrimeric G proteins, active G proteincoupled receptors (Rs), which activate G proteins, and GTPaseactivating proteins (GAPs), which deactivate G proteins. We term these modules GTPase-cycle modules. The local concentrations of these proteins are spatially regulated between plasma membrane microdomains and between the plasma membrane and cytosol, but no data or models are available that quantitatively explain the effect of such regulation on signaling. We present a computational model of the GTPase-cycle module that predicts that the interplay of local G protein, R, and GAP concentrations gives rise to 16 distinct signaling regimes and numerous intermediate signaling phenomena. The regimes suggest alternative modes of the GTPasecycle module that occur based on defined local concentrations of the component proteins. In one mode, signaling occurs while G protein and receptor are unclustered and GAP eliminates signaling; in another, G protein and receptor are clustered and GAP can rapidly modulate signaling but does not eliminate it. Experimental data from multiple GTPase-cycle modules is interpreted in light of these predictions. The latter mode explains previously paradoxical data in which GAP does not alter maximal current amplitude of G protein-activated ion channels, but hastens signaling. The predictions indicate how variations in local concentrations of the component proteins create GTPase-cycle modules with distinctive phenotypes. They provide a quantitative framework for investigating how regulation of local concentrations of components of the GTPase-cycle module affects signaling.T he timing and amplitude of signaling in cellular signaling networks depends on the local concentrations and kinetics of component proteins. The complexity of these networks has precluded comprehensive quantitative analysis of signaling. However, the networks can be divided into discrete signaling modules that can be quantitatively modeled and analyzed independently (1-5). Later, such models of modules can be experimentally verified and then reconnected to build larger signaling networks.G protein-mediated signaling networks (6) are ideal for such quantitative analysis. A key signaling module in these networks is the GTPase-cycle module, which controls signaling by regulating G protein activity. This module consists of a heterotrimeric G protein, a guanine nucleotide exchange factor (GEF) such as agonist-bound and active G protein-coupled receptor, a GTPase-activating protein (GAP) such as regulator of G protein signaling (RGS) protein or phospholipase C-␤, GTP, GDP, and P i . The extent of G protein activation reflects the balance between the rates of GDP͞GTP exchange, which activates the G protein, and GTP hydrolysis, which deactivates it (7-9). GEFs facilitate GDP͞GTP exchange (9), and GAPs accelerate GTP hydrolysis (10, 11). Additional roles of GEFs (12) and GAPs (10,11,13,14) are not included in this module. At present, we lack quantitativ...

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“…The area under the activation curve is a good measure of a transient response and has proven useful to study signaling cascades, 21,22 reaction-diffusion systems, 23 cell surface receptors, 20 and was also found to link ERK activation with DNA synthesis. 24 To study the input-output relationship of cellular signaling models, simulations are typically used to obtain sensitivities of outputs on the different model components. Conclusions drawn from simulation-based studies, however, can depend strongly on the specific parameter values used in the simulations.…”

Section: Exact Computation Of the Integrated Response In Signalling Nmentioning

confidence: 99%

The EGFR demonstrates linear signal transmission

et al. 2014

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Cells sense information encoded in extracellular ligand concentrations and process it using intracellular signalling cascades. Using mathematical modelling and high-throughput imaging of individual cells, we studied how a transient extracellular growth factor signal is sensed by the epidermal growth factor receptor system, processed by downstream signalling, and transmitted to the nucleus. We found that transient epidermal growth factor signals are linearly translated into an activated epidermal growth factor receptor integrated over time. This allows us to generate a simplified model of receptor signaling where the receptor acts as a perfect sensor of extracellular information, while the nonlinear input-output relationship of EGF-EGFR triggered signalling is a consequence of the downstream MAPK cascade alone. Insight, innovation, integrationWe derived an analytical formula from nonlinear models of receptor signalling that have been used to model the signalling of the epidermal growth factor receptor (EGFR). The formula predicted that the cumulative EGFR signalling is linearly dependent on the ligand concentration outside the cell. Quantitative imaging of HeLa cells suggests that the cumulative EGFR signalling indeed depends linearly on the EGF concentration. Our mathematical approach therefore allows a simplified view on complex networks of receptor dynamics and describes the EGFR as a linear sensor of extracellular information encoded in ligand concentrations.

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Bioengineering Models of Cell Signaling

Cited by 132 publications

References 107 publications

Synthetic Multivalent Ligands as Probes of Signal Transduction

Synthetic Multivalent Ligands as Probes of Signal Transduction

Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction

The EGFR demonstrates linear signal transmission

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