| Title |
Dendritic trafficking faces physiologically critical speed-precision tradeoffs
|
|---|---|
| Published in |
eLife, December 2016
|
| DOI | 10.7554/elife.20556 |
| Pubmed ID | |
| Authors | |
| Abstract |
Nervous system function requires intracellular transport of channels, receptors, mRNAs, and other cargo throughout complex neuronal morphologies. Local signals such as synaptic input can regulate cargo trafficking, motivating the leading conceptual model of neuron-wide transport, sometimes called the 'sushi-belt model' (Doyle and Kiebler, 2011). Current theories and experiments are based on this model, yet its predictions are not rigorously understood. We formalized the sushi belt model mathematically, and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies. However, the model also predicts an unavoidable, morphology dependent tradeoff between speed, precision and metabolic efficiency of cargo transport. With experimental estimates of trafficking kinetics, the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree. These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 25% |
| United Kingdom | 6 | 21% |
| Greece | 2 | 7% |
| Switzerland | 2 | 7% |
| Belgium | 1 | 4% |
| Canada | 1 | 4% |
| France | 1 | 4% |
| Unknown | 8 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
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| Scientists | 8 | 29% |
| Practitioners (doctors, other healthcare professionals) | 2 | 7% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
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| India | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 84 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 26% |
| Researcher | 21 | 24% |
| Student > Master | 7 | 8% |
| Student > Bachelor | 5 | 6% |
| Student > Doctoral Student | 4 | 5% |
| Other | 14 | 16% |
| Unknown | 13 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 22 | 25% |
| Agricultural and Biological Sciences | 19 | 22% |
| Biochemistry, Genetics and Molecular Biology | 11 | 13% |
| Physics and Astronomy | 9 | 10% |
| Engineering | 4 | 5% |
| Other | 10 | 11% |
| Unknown | 12 | 14% |