Congenital CMV
Maternal viral and immune dynamics, placental passage, fetal exposure, model comparison, sensitivity, calibration, and surrogate modeling.
Duke Postdoctoral Research · Ongoing methodological development
Mechanistic and Multiscale Modeling for CMV and Immune Translation
I develop quantitative methods for two related translation problems across biological scales and study systems. The program combines maternal-placental-fetal CMV modeling with relaxed optimal transport for mapping immune-cell populations between human and non-human primate data.
One program, two translation problems
Model choice should reflect biological structure, data limitations, and the uncertainty that remains.
Congenital CMV transmission spans maternal immune dynamics, placental infection and barrier function, and fetal exposure. Cross-species immune studies introduce a second translation problem because cell proportions, technical effects, marker panels, and even cell subsets can differ between humans and non-human primates.
The unifying contribution is a disciplined process for model comparison. I compare alternative mechanisms, determine what the available observations can identify, and preserve ambiguity when the evidence does not justify a forced match or a single preferred model.
Human-NHP immune mapping
Shared-marker preprocessing, independent clustering, Gaussian summaries, relaxed optimal transport, bidirectional mapping, and uncertainty diagnostics.
Module 1 · Congenital CMV
A modular model represents maternal, placental, and fetal processes separately.
Ordinary differential equations represent viral and immune dynamics in maternal blood. Spatial or agent-based components describe placental infection and passage, while stochastic components represent variation in fetal exposure. Clinical cohort measurements and placental-organoid experiments provide complementary constraints.
Maternal compartmentViral and immune ODEs
→Placental interfaceSpatial or agent-based passage
→Fetal compartmentStochastic exposure and transmission
↕EvidenceClinical cohorts and organoid experiments
Status
These figures are model diagnostics, not validated predictions of vaccine efficacy. Claims about maternal protection or fetal transmission require project-specific calibration and external validation.
Model selection and computation
A faster surrogate is useful only if it preserves the dynamics relevant to the scientific question.
I compare candidate mechanistic formulations before selecting calibration targets, use sensitivity analysis to identify influential and weakly identifiable parameters, and evaluate surrogate models that may reduce simulation cost.
Gaussian-process regression, gradient-boosted trees with Bayesian optimization, and physics-informed neural networks are considered for distinct tasks. They are not interchangeable. The appropriate choice depends on data volume, computational cost, uncertainty requirements, and whether biological structure must remain explicit.
Mechanistic modelsODEs, agent-based simulation, stochastic processes
UncertaintySensitivity, calibration, identifiability, emulation error
SurrogatesGaussian processes, boosted trees, PINNs
InfrastructureDocker, SLURM/HPC, versioned workflows
Module 2 · Cross-species immune mapping
Relaxed transport keeps ambiguity and unmatched cell populations visible.
Marker panels, cell proportions, and technical effects differ across species, so nearest-centroid matching can impose misleading one-to-one correspondences. The workflow uses shared-marker preprocessing, independent clustering, covariance-regularized Gaussian representations, Wasserstein distances, and optimal transport with relaxed marginals.
The mapping is evaluated in both directions and reports top probability, entropy, and unmatched mass. These diagnostics distinguish concentrated correspondences from ambiguous or unsupported matches.
PreprocessShared markers and batch correction
→ClusterIndependent Leiden structure
→SummarizeGaussian means and covariance
→TransportRelaxed marginals and unmatched mass
→AuditEntropy, probability, bidirectionality
Takeaway
The contribution is a disciplined process for selecting and testing models.
The goal is not to present one preferred simulator or one forced cross-species map. It is to determine which model is appropriate, what the data can identify, and where uncertainty must remain explicit.
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