Modelling of control experiments.

<p>A) Cell growth and viability, B) Glucose and lactate concentration profiles, C) Glutamate and mAb concentration profiles, D) Cell cycle distribution.</p

GSA indexes.

<p>Outputs: X_V = viable cell density, f_G1 = G₁/G₀ cell fraction, f_S = S cell fraction, f_G2 = G₂/M cell fraction, and mAb = antibody titre.</p

Schematic representation of the discretized cell cycle model.

<p>Schematic representation of the discretized cell cycle model.</p

Proliferation assay—Proliferation marker Ki-67 and viability.

<p>Proliferation assay—Proliferation marker Ki-67 and viability.</p

Model prediction of an undisturbed cell cycle experiment.

<p>A) Cell growth and viability, B) Glucose and lactate concentration profiles, C) Glutamate and mAb concentration profiles, D) Cell cycle distribution.</p

Model parameters values.

<p>Model parameters values.</p

Modelling of after DMSO arrest release experiment.

<p>A) Cell growth and viability, B) Glucose and lactate concentration profiles, C) Glutamate and mAb concentration profiles, D) Cell cycle distribution, E) Cell cycle distribution delay simulation. Grey lines: simulation with estimated parameters; Black lines: simulation with DMSO delay parameters (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004062#pcbi.1004062.t002" target="_blank">Table 2</a>)</p

Proliferation assay—Cyclin expression profiles.

<p>A) Cyclin E1 expression—EdU positive cells, B) Cyclin E1 expression—EdU negative cells, C) Cyclin B1 expression—EdU positive cells, D) Cyclin B1 expression—EdU negative cells, E) Average cyclin E1 (for G₁/G₀ phase) and cyclin B1 (for G₂/M) expression before and after glutamate exhaustion.</p

Model variables.

<p>Model variables.</p

Improving Embryonic Stem Cell Expansion through the Combination of Perfusion and Bioprocess Model Design

<div><p>Background</p><p>High proliferative and differentiation capacity renders embryonic stem cells (ESCs) a promising cell source for tissue engineering and cell-based therapies. Harnessing their potential, however, requires well-designed, efficient and reproducible expansion and differentiation protocols as well as avoiding hazardous by-products, such as teratoma formation. Traditional, standard culture methodologies are fragmented and limited in their fed-batch feeding strategies that afford a sub-optimal environment for cellular metabolism. Herein, we investigate the impact of metabolic stress as a result of inefficient feeding utilizing a novel perfusion bioreactor and a mathematical model to achieve bioprocess improvement.</p><p>Methodology/Principal Findings</p><p>To characterize nutritional requirements, the expansion of undifferentiated murine ESCs (mESCs) encapsulated in hydrogels was performed in batch and perfusion cultures using bioreactors. Despite sufficient nutrient and growth factor provision, the accumulation of inhibitory metabolites resulted in the unscheduled differentiation of mESCs and a decline in their cell numbers in the batch cultures. In contrast, perfusion cultures maintained metabolite concentration below toxic levels, resulting in the robust expansion (>16-fold) of high quality ‘naïve’ mESCs within 4 days. A multi-scale mathematical model describing population segregated growth kinetics, metabolism and the expression of selected pluripotency (‘stemness’) genes was implemented to maximize information from available experimental data. A global sensitivity analysis (GSA) was employed that identified significant (6/29) model parameters and enabled model validation. Predicting the preferential propagation of undifferentiated ESCs in perfusion culture conditions demonstrates synchrony between theory and experiment.</p><p>Conclusions/Significance</p><p>The limitations of batch culture highlight the importance of cellular metabolism in maintaining pluripotency, which necessitates the design of suitable ESC bioprocesses. We propose a novel investigational framework that integrates a novel perfusion culture platform (controlled metabolic conditions) with mathematical modeling (information maximization) to enhance ESC bioprocess productivity and facilitate bioprocess optimization.</p></div