Network Robustness Due to Multiple Positive Feedback Loops: A Systematic Study of a Th Cell Differentiation Model
Bibliographic record
Abstract
Positive feedback loops have been identified in many biological signal transduction systems. Their importance in a system's bistability has been well established by identifying multiple steady states of a network under different parameters. In this paper, we identify the contribution of positive feedback loops to network robustness by a systematic comparison between network structures and responses to perturbations at a pre-steady state. Our study is based on a T helper (Th) cell differentiation model in which positive feedback loops give rise to a subnet robustness against both positive and negative perturbations from outside the subnet. Although it is unclear whether this pre-steady state exists in vivo, the results from in silico modeling are in agreement with the reported in vivo observations. Being highly heterogeneous and rarely at a steady state, the disease cells, such as cancer cells, may gain potential resistances to certain drugs in a similar way. From the reverse engineering point of view, our results confirm that, while data from perturbation experiments are very effective in identifying causal relationships among the network components, caution should be taken, as in some circumstances, a direct interaction could be invisible due to positive feedback loops.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".