Dynamic Hybrid Modeling of Serial Transfer Culture for Describing Growth of Synthetic Human Gut Microbiota

Human gut microbiota is an ecosystem involving millions of different microorganisms, its composition and dynamics are related to the host's health and disease. Current approaches to simplifying its study integrate synthetic microbial communities (SynComs), enabling the analysis of multiple bacteria interaction from a pairwise perspective. For this, batch culture is commonly implemented. However, the response after reaching the stationary condition of this in vitro system does not reflect the biomimetic condition of microbiota in a human gut, where a continuous process is performed. On the other hand, performing continuous culture represents a challenge due to cost, operation volume, and the requirement for prior knowledge of bacterial community kinetic characterization. This work proposes a serial transfer culture approach to approximate the stationary conditions of continuous culture while reducing operation volume by an experimental setup with microbioreactors. The system is modeled as a hybrid dynamic set of ordinary differential equations with a switching sequence between batch cultures that depends on three parameters. Numerical simulation and experimental implementation of a mono-specie culture are analyzed to determine the best combination switching parameters: 1 h time of first transfer, 2 h period of transfer and 0.6 transferred volume factor had the lower error norm with 0.602. The continuous, batch, and serial transfer approaches are compared, and their stationary conditions are used to predict the ecological response by the Generalized Lotka-Volterra model, all three conditions identified a negative intraspecies interaction, but magnitudes differed. This behavior is also identified when experimental data are used for parameterization.Clinical Relevance- The study of human gut microbiota dynamics is a current challenge for personalized medicine. Better in vitro and in silico platforms can accelerate the development of new treatments.