Progress update #4: Applying context-aware adapters to evaluate how modulating the host ISC machinery behaves across...

Progress update #4: Applying context-aware adapters to evaluate how modulating the host ISC machinery behaves across different contexts

Without training separate models for every viral infection, each step was evaluated with a specific method.

Every biological condition was parameterized by a seven-factor context: species (human vs. rodent validation models), cell type (epithelial cells, CD4+ T-lymphocytes, hepatocytes, cardiomyocytes), donor, genotype, microenvironment (intracellular redox state, reactive oxygen species, and infection status), dose, and exposure time. The context embedding generator encoded these variables into a normalized 16-dimensional vector.

In the multiscale graph, this modulation dynamically adjusted node and edge weights based on infection status and tissue type. In healthy, uninfected cells where oxidative stress was basal and viral replication was absent, edge weights heavily penalized ISCU suppression as a toxicity liability. Conversely, in actively infected cells where intracellular context vectors reflected high viral transcriptional stress and massive iron-sulfur consumption, the FiLM layer shifted the weight of ISCU throttling to an overwhelming efficacy benefit.

The system evaluated thousands of cellular context shifts in milliseconds, passing modulated representations directly into downstream evaluation. 

What we learned: how modulating the host ISC complex behaves across diverse tissues and infection states within a single model, ensuring target suppression is penalized as a liability in healthy cells while scored as an efficacy benefit in infected cells.

What's next:  The Meta-Learner and Uncertainty Layer