Implications of Degeneracy and Pleiotropy for Homeostatic Regulation of Neuronal Properties
Bibliographic record
Abstract
Neuronal excitability is homeostatically regulated through coordinated adjustments of diverse ion channels. This is possible because the basis for excitability is degenerate—equivalent excitability can be achieved via different ion channels. But one must also consider that ion channels are pleiotropic, meaning that they affect multiple properties. Then how does a neuron co-regulate multiple (degenerate) properties by adjusting pleiotropic channels? The overarching goal of this thesis is to study the implications of pleiotropy and degeneracy for homeostatic regulation of neuronal properties. Using simulations, I showed that successful homeostatic regulation requires that the number of adjustable ion channels (nin) must exceed the number of regulated properties (nout). Those results reveal that homeostatic regulation can fail from having to regulate multiple properties concurrently, which is more challenging than regulating them separately. In collaboration with experimentalists, I conducted simulations to demonstrate that nociceptor excitability is degenerate on the basis of different subtypes of voltage-gated sodium channels (NaVs). This has critical implications for the efficacy of subtype-selective NaV inhibitors: Given that one subtype can compensate for another, the therapeutic effects of blocking NaV1.7 can be offset by compensatory changes in other NaVs (i.e. subversive compensation). I reviewed preclinical and clinical literature on NaV1.7-selective inhibitors to see whether there was evidence for subversive compensation, a possible explanation for why preclinical success did not translate into clinical success. I found that nearly all preclinical studies were single dosing, and would not have observed the effects of subversive compensation. Overall, this thesis shows that degeneracy is an important consideration when trying to understand homeostatic regulation of neuronal excitability under normal and pathological conditions. Indeed, degeneracy predicts that subtype-selective ion channel inhibitors might be particularly prone to having their therapeutic effects subverted by homeostatic regulation of other, non-targeted channels, which is obviously an important yet underappreciated factor in choosing drug targets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".