Outputs¶

Overview¶

The module returns either structured Python objects, pandas dataframes, scalar optimization results, or generated plots.

Structured result object¶

net_gene_fitness() returns a ModelResult dataclass with:

gene
baseline_abundance
threshold_abundance
overabundance
abundance_mean
abundance_std
robustness_probability
burden_cost
toxicity_cost
net_fitness
pathway

Tabular outputs¶

`evaluate()`¶

Returns a dataframe sorted by descending net_fitness and then overabundance, with an added rank column.

`optimize_all()`¶

Returns a dataframe with:

gene
baseline_abundance
optimal_abundance
critical_threshold
optimal_overabundance
optimal_net_fitness

`pathway_summary()`¶

Returns grouped statistics by pathway:

number of genes,
mean and median overabundance,
mean net fitness,
mean robustness.

`simulate_intervention()`¶

Returns a dataframe of post-inhibition per-gene fitness results.

Scalar outputs¶

`tumor_fitness_score()`¶

Returns a scalar tumor score derived from the clipped mean gene fitness.

`optimize_global()`¶

Returns a tuple:

(optimal_abundance_vector: np.ndarray, best_global_fitness: float)
````

### `optimize_inhibition()`

Returns a tuple:

```python
(best_inhibition: float, residual_fitness: float)

Script-generated files¶

When running:

python rlto_model.py

the code creates:

diagnostic_plots/

and writes figures such as:

diagnostic_plots/MYC_component.png
diagnostic_plots/MYC_noise.png
diagnostic_plots/MYC_convergence.png
diagnostic_plots/MYC_sensitivity.png
diagnostic_plots/bivariate_MYC_KRAS.png
diagnostic_plots/bivariate_CHAOS_HOUSEKEEPER.png
diagnostic_plots/bivariate_TOXIC_STRUCTURAL.png
...
...
...

Example result interpretation¶

Field	Interpretation
`overabundance > 1`	mean abundance exceeds critical threshold
high `robustness_probability`	abundance distribution remains above threshold more reliably
high `burden_cost`	production cost is materially penalizing
high `toxicity_cost`	superlinear overexpression penalty is material
low or negative `net_fitness`	the gene state is unfavorable under current settings

JSON-style terminal summary¶

The script entrypoint ends by printing a JSON payload similar to:

{
  "Best_global_fitness": 5.8508,
  "Optimal_Inhibition_Target": 0.000006,
  "All_Genes_Optimization": [
    {
      "gene": "MYC",
      "baseline_abundance": 2301.83,
      "optimal_abundance": 70.10,
      "optimal_net_fitness": 7.92
    }
  ]
}

Note

Exact values depend on the model configuration and dataset. The numeric example above reflects the current README narrative and should be treated as an expected pattern rather than a guaranteed regression fixture.