Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease.

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Functional design of experiment for potency assay optimization and in-silico simulation.

For biotherapeutic analytics, robust and reliable potency assays are required. Design of experiment (DoE) approaches are used to investigate the impact of multiple assay parameters. Currently, specific assay features (e.g., half effective concentration) are modelled independently from each other. A joint interpretation of several assay features is thus difficult. In our functional DoE approach, we use the functional relationship of the assay features to describe the sigmoidal dose-response curve. With the composed functional form, the direct impact of assay parameters on the dose-response curve shape was modelled. Moreover, a multivariate desirability was defined and used for assay optimization. We believe that functional modelling contributes to understanding the joint influence of assay parameters and helps to design robust biotherapeutic analytics.