Cancer RLTO Model¶

A cancer-adapted RLTO-inspired Python model for deterministic analysis of robustness, burden, toxicity, optimization, and stress-test diagnostics.

What this project does¶

The repository provides a single Python module, rlto_model.py, that models tumor-cell fitness as a trade-off between:

• robustness against abundance fluctuations,
• linear burden from producing protein,
• superlinear toxicity from overabundance,
• and a global resource constraint at the tumor level.

The implementation supports:

• per-gene fitness evaluation,
• optimization of gene abundance,
• global optimization across all genes,
• simulation and optimization of gene inhibition,
• pathway-level summaries,
• synthetic stress-test data,
• and diagnostic plots for landscape shape, convergence, sensitivity, and bivariate interactions.

Project status¶

This documentation is generated from the current repository state and is intentionally aligned to the code rather than to narrative claims in older prose.

The project currently looks like a script-oriented module rather than a packaged library. The documentation therefore emphasizes direct script execution and module import patterns.

Typical workflow¶

1. Build a dataframe of gene parameters or use the built-in demo dataset.
2. Construct a Microenvironment.
3. Build a CancerRLTOModel from the dataframe.
4. Evaluate fitness or run optimization routines.
5. Generate diagnostic plots if needed.
6. Interpret outputs in terms of robustness, overabundance, burden, toxicity, and intervention response.

Quick example¶

from rlto_model import CancerRLTOModel, Microenvironment

env = Microenvironment(hypoxia=0.6, nutrient_limitation=0.4)
df = CancerRLTOModel.demo_dataset()
model = CancerRLTOModel.from_dataframe(df, microenvironment=env)

summary = model.evaluate()
optimized = model.optimize_all()
score = model.tumor_fitness_score()

print(summary.head())
print(optimized[["gene", "optimal_abundance", "optimal_net_fitness"]])
print(score)

Output conventions¶

Running the module as a script:

python rlto_model.py

will:

• print optimization summaries to stdout,
• optimize the built-in synthetic “Rogue’s Gallery” stress-test dataset,
• and write diagnostic figures into diagnostic_plots/.

See Outputs for the expected file structure.

Feature summary¶