Reverse engineering of gene regulatory networks: A systems approach
Bibliographic record
Abstract
In the last decade many computational approaches have been introduced to model networks of molecular interactions from gene expression data. Such networks can provide an understanding of the regulatory mechanisms in the cells. System identification algorithms refer to a group of approaches that capture the dynamic relationship between the input and output of a system, and provide a deterministic model of its function. These approaches have been extensively developed for engineering systems, and have reasonable computational requirements. In this paper, we present two system identification methods applied to reverse engineering of gene regulatory networks. Gene regulatory networks are constructed as systems where the output to be estimated is an expression profile of a gene, and the inputs are the potential regulators of that gene. The first reverse engineering method is based on orthogonal search and selects terms from a predefined set of gene expression profiles to best fit the expression levels of a given output gene. The second method consists of multiple cascade models; each cascade includes a dynamic component and a static component. Several cascades are used in parallel to reduce the difference of the estimated expression profiles with the actual ones. To assess the performance of the proposed methods, they are applied to a temporal synthetic dataset, a simulated gene expression time series of songbird brain, and yeast Saccharomyces Cerevisiae cell cycle. Results are compared to known mechanisms of the underlying data and the literature, and demonstrate that the proposed approaches capture the underlying interactions as networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".