Stochastic Biological System-of-Systems Modelling for iPSC Culture

TL;DR

This paper introduces a stochastic, modular Bio-SoS modeling framework for iPSC cultures, capturing cell interactions, heterogeneity, and environmental effects to improve understanding and prediction of cell culture outcomes.

Contribution

It presents a novel, integrated Bio-SoS framework combining stochastic reaction networks and reaction-diffusion models for iPSC culture analysis.

Findings

Model accurately predicts cell heterogeneity and aggregate behavior.

Variance analysis quantifies impact of aggregate size on cell quality.

Framework enhances data integration and process prediction.

Abstract

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.