Conference Agenda

Bacterial pathogens often reside in environments with rich microbial ecosystems. Therefore, antibiotic treatments of pathogens are influenced by the environmental microbiome. Recent research has illustrated that Bacillus subtilis PY79 and Escherichia coli K12 were respectively susceptible and tolerant to ampicillin in coculture, whereas they exhibited the opposite behaviour in monocultures. This research investigated whether the difference in antibiotic responses between monocultures and cocultures results from direct interactions between these two bacteria or indirectly through their interactions with the antibiotic. The research hypothesis was that coculture kinetics arise from the simple combination of the monoculture kinetics based on the interactions between each species and the antibiotic.

To validate this hypothesis, a model-based approach was followed. First, two population-level models were constructed to describe the microbial kinetics of either B. subtilis or E. coli in monoculture in the presence of ampicillin. Specifically, these models consist of a set of coupled differential equations that describe (i) microbial growth inhibition at low antibiotic concentrations, (ii) microbial inactivation kinetics at high antibiotic concentrations, and (iii) the degradation of the antibiotic due to bacterially produced beta-lactamase enzymes. The model parameters were estimated from a dataset of 36 shake flask experiments that contained measurements of the evolution of the viable cell densities and the concentration of ampicillin. Then, the obtained monoculture models were combined into a coculture model without adding any direct interactions between the bacterial species. This model was validated against another experimental dataset of 17 shake flask experiments on the coculture of B. subtilis and E. coli in the presence of ampicillin.

The obtained monoculture models fitted the experimental data accurately, and the parameters had narrow confidence bounds, indicating high identifiability of the selected model structures. These monoculture models captured specific differences between the behaviour of the two strains. B. subtilis was found to degrade the environmental ampicillin quickly but required a low environmental ampicillin level for growth. E. coli, on the other hand, had limited capability to degrade ampicillin but was able to grow at higher ampicillin levels. The coculture model was constructed by simply combining the monoculture models with their estimated model parameters. This coculture model was found to have good accuracy in predicting the experimentally measured coculture behaviour, as evaluated based on the root mean square error. Therefore, this model proved that the coculture behaviour of B. subtilis and E. coli arises as a simple combination of the monoculture behaviour, without direct interactions between the strains. Moreover, the models demonstrate that the difference in the observed antibiotic susceptibility between monocultures and cocultures is due to the cooperator-cheater dynamics that arise in cocultures because E. coli takes advantage of B. subtilis’ ability to degrade ampicillin.

This research used a data-driven semi-mechanistic population-level modelling approach to study the differences in antibiotic susceptibility observed between strains occurring in pure and mixed species systems. The results highlighted how model-based studies of these microbial dynamics help to understand the interactions that occur in antibiotic resistance between bacteria.

Human induced pluripotent stem (hiPS) cells are considered as the most promising sources in the field of regenerative medicine due to their various advantages compared with the conventional sources. Along with recent successful clinical studies, e.g., dilated cardiomyopathy and Parkinson’s disease, the realization of regenerative medicine using hiPS cells is becoming possible.

In hiPS cell manufacturing, the freezing process is one of the most important steps because it is necessary for the transportation and preservation of hiPS cell products. Generally, the products currently used in clinical applications are obtained via spheroids which are spherical cell aggregates. However, it is difficult to freeze hiPS cell-derived spheroids while maintaining high quality with the current freezing technology, which is a major obstacle in the product manufacturing¹.

In the field of process systems engineering, model-based approaches have been applied to bio-related processes, e.g., design and evaluation of therapies to modulate neutrophil dynamics² and sensitivity analysis of perfusion bioreactors³. Some contributions involve stem cell manufacturing processes, e.g., model-based assessment of temperature profiles in slow freezing for human induced pluripotent stem cells⁴ and design space determination of mesenchymal stem cell cultivation processes⁵. However, model application for the design of spheroid manufacturing processes is still in fancy.

This work presents development of a hybrid model for determining design spaces in freezing processes of hiPS cell-derived spheroids. We first developed a mechanistic model to describe the structure of spheroids and the phenomena. The mechanistic model was then extended to cover the cell survival rate through statistical modeling. Freezing experiments using hiPS cell-derived spheroids were performed to estimate the necessary parameter values for the extension. Given the spheroid radius, the concentration of cryoprotective agent, and the immersion time of the cryoprotective agent into the spheroid, the developed hybrid model can calculate the cell survival rate and the number of living cells in the spheroid after thawing, which are the quality and productivity indicators.

The application of the hybrid model was demonstrated in a case study. As a result, a feasible parameter range of the freezing process was obtained, given a set of constraints such as the cell survival rate and the number of living cells. The result would be useful for the freezing process design of hiPS cell-derived spheroids. In the ongoing work, we are investigating of the cell toxicity derived from cryoprotective agents.

References
1. Bissoyi A., et al., ACS Appl. Mater. Interfaces., 15, 2630–2638 (2023).
2. Ho T., et al., Comput. Chem. Eng., 51, 187–196 (2013).
3. Nașcu I., et al., Comput. Chem. Eng., 163, 107829 (2022).
4. Hayashi Y., et al., Comput. Chem. Eng., 144, 107150 (2021).
5. Hirono K., et al., AIChE J., 70, e18452 (2024).