Project Update #6: Pairing the meta-learner with an uncertainty layer to generalize host-pathogen dynamics across dis...

Project Update #6: Pairing the meta-learner with an uncertainty layer to generalize host-pathogen dynamics across disparate viral families from sparse experimental data

The meta-learner utilized MAML and Reptile gradient updates to learn the shared host-pathogen competition mechanism. The inner loop adapted the model to specific viral infection kinetics (such as SARS-CoV-2 in lung cells or HIV-1 in immune cells) using only a few observation points. The outer loop updated the shared input projection and output prediction head, ensuring the network generalized the fundamental causal rule: irrespective of viral taxonomy, all rapid viral blooms impose a severe metabolic drain on host iron-sulfur production.

Through the shared projection, the meta-learner aligned cross-species causal graphs between human and mouse models. It normalized differences in basal metabolic rates and mitochondrial density across species while preserving the invariant kinetic ratio required to starve viral polymerases.

The uncertainty layer estimated variance across the multiscale graph edges. Aleatoric variance captured biological stochasticity in cellular cluster production and viral burst size, while epistemic variance quantified missing data in untested tissues (such as central nervous system compartments). By flagging edges where epistemic uncertainty was elevated, the system guaranteed that the proposed therapeutic window was statistically reliable before advancing to candidate molecule generation. 

What we learned: we were able to generalize the shared viral dependency on host ISC across diverse pathogen families and cross-species models from sparse data, while filtering out epistemic uncertainty before advancing to candidate molecule generation.

What's next: the virtual preclinical and clinitical translation layer