Stem Cell Fate Decision Making: Modeling Approaches

Lifelike Transformative Materials for Biohybrid Implants: Inspired by Nature, Driven by Technology

Today's living world is enriched with a myriad of natural biological designs, shaped by billions of years of evolution. Unraveling the construction rules of living organisms offers the potential to create new materials and systems for biomedicine. From the close examination of living organisms, several concepts emerge: hierarchy, pattern repetition, adaptation, and irreducible complexity. All these aspects must be tackled to develop transformative materials with lifelike behavior. This perspective article highlights recent progress in the development of transformative biohybrid systems for applications in the fields of tissue regeneration and biomedicine. Advances in computational simulations and data-driven predictions are also discussed. These tools enable the virtual high-throughput screening of implant design and performance before committing to fabrication, thus reducing the development time and cost of biomimetic and biohybrid constructs. The ongoing progress of imaging methods also constitutes an essential part of this matter in order to validate the computation models and enable longitudinal monitoring. Finally, the current challenges of lifelike biohybrid materials, including reproducibility, ethical considerations, and translation, are discussed. Advances in the development of lifelike materials will open new biomedical horizons, where perhaps what is currently envisioned as science fiction will become a science-driven reality in the future.

Mathematics of neural stem cells: Linking data and processes

Adult stem cells are described as a discrete population of cells that stand at the top of a hierarchy of progressively differentiating cells. Through their unique ability to self-renew and differentiate, they regulate the number of end-differentiated cells that contribute to tissue physiology. The question of how discrete, continuous, or reversible the transitions through these hierarchies are and the precise parameters that determine the ultimate performance of stem cells in adulthood are the subject of intense research. In this review, we explain how mathematical modelling has improved the mechanistic understanding of stem cell dynamics in the adult brain. We also discuss how single-cell sequencing has influenced the understanding of cell states or cell types. Finally, we discuss how the combination of single-cell sequencing technologies and mathematical modelling provides a unique opportunity to answer some burning questions in the field of stem cell biology.

Implementing computational modeling in tissue engineering: where disciplines meet

In recent years, the mathematical and computational sciences have developed novel methodologies and insights that can aid in designing advanced bioreactors, microfluidic set-ups or organ-on-chip devices, in optimizing culture conditions, or predicting long-term behavior of engineered tissues in vivo. In this review, we introduce the concept of computational models and how they can be integrated in an interdisciplinary workflow for Tissue Engineering and Regenerative Medicine (TERM). We specifically aim this review of general concepts and examples at experimental scientists with little or no computational modeling experience. We also describe the contribution of computational models in understanding TERM processes and in advancing the TERM field by providing novel insights.

Morphological switch is associated with increase in cell-cell contacts, ALP, and confluence above a minimum island area to perimeter ratio

During osteogenic differentiation in vitro, stem-like cells seeded at a low-density spread and are isolated. As the cells proliferate and mature, they become more cuboidal in shape with more cell-cell contacts. However, the coordination of this switch in cell morphology from elongated to cuboidal, cell-cell contacts, and differentiation is not known. In this work, we present results from experiments and a simulation of cell proliferation on protein-micropatterned islands that, independent of island size (25-1,000 μm) or shape (circles, squares, and hollow squares), shows a distinct morphological switch that is better described as a function of island confluence than time in culture, the standard measure in cell culture experiments. The simulation and experiments show cell morphology and island cell density versus confluence collapse to a single curve for all islands if the island area to perimeter ratio is ≥25 μm. Cell-cell contacts in the simulation and alkaline phosphatase (ALP) expression in experiments, a common marker for osteogenic differentiation, show exponential growth with confluence, rapidly increasing after the switch at ≈0.5 confluence. Furthermore, cell morphology, density, contacts, and ALP are better predicted by confluence than time in culture. The variability with time in culture leads to challenges in not only interpreting data but also in comparing data across research laboratories. This simulation can be used to predict cell behavior on different size and shape islands and to plan and optimize experiments that explore cell behavior as a function of a wide range of island geometries.

Regulation and mechanism of YAP/TAZ in the mechanical microenvironment of stem cells (Review)

Stem cells receive cues from their physical and mechanical microenvironment via mechanosensing and mechanotransduction. These cues affect proliferation, self‑renewal and differentiation into specific cell fates. A growing body of evidence suggests that yes‑associated protein (YAP) and transcriptional coactivator with PDZ‑binding motif (TAZ) mechanotransduction is key for driving stem cell behavior and regeneration via the Hippo and other signaling pathways. YAP/TAZ receive a range of physical cues, including extracellular matrix stiffness, cell geometry, flow shear stress and mechanical forces in the cytoskeleton, and translate them into cell‑specific transcriptional programs. However, the mechanism by which mechanical signals regulate YAP/TAZ activity in stem cells is not fully understand. The present review summarizes the current knowledge of the mechanisms involved in YAP/TAZ regulation on the physical and mechanical microenvironment, as well as its potential effects on stem cell differentiation.

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices.

Development of a kinetic model expressing anomalous phenomena in human induced pluripotent stem cell culture

During culture with feeder cells, deviation from the undifferentiated state of human induced pluripotent stem cells (hiPSCs) occurs at a very low frequency. Anomalous cell migration in central and peripheral regions of hiPSC colonies has been suggested to be the trigger for this phenomenon. To confirm this hypothesis, sequential cell migration prior to deviation must be demonstrated. This has been difficult using in vitro methods. We therefore developed a kinetic model with a proposed definition of anomalous cell migration as continuous relatively fast or slow cell migration. The developed model was validated via in silico reproduction of deviation phenomenon observed in vitro, such as the positions of deviated cells in a colony and the frequency of deviation in culture. This model suggests that anomalous cell migration-driven hiPSC deviation can be explained by two factors: a mechanical stimulus, represented by cell migration, and duration of the mechanical stimulus. The factor "duration of mechanical stimulus" sets our model apart from others, and helps to realize the ultra-rare trigger (approximately 10^-5) of deviation from the undifferentiated state in hiPSC culture.

Proliferation of cells with aggregation and communication

Cell proliferation is often considered to occur via front propagation with constant velocity. This scenario proposed by Fisher, Kolmogorov, Petrovsky, and Piskunov is based on the solution of the corresponding mean-field reaction-diffusion equations and does not take into account that due to adhesion the cells have tendency to aggregate and that the rate of cell division may depend on the cell-cell communication. Herein, the author presents extensive Monte Carlo simulations taking both these factors into account and illustrating that the former factor can dramatically modify the spatio-temporal kinetics of cell proliferation. In particular, the conventional relation between the front velocity and diffusion coefficient may fail, the front velocity may appreciably increase with increasing time, and/or the front may be partly or fully smeared on the realistic length scales.

Integrated Multiscale Biomaterials Experiment and Modeling

The integration of modeling and experimentation is an integral component of all engineering design. Developing the technologies to achieve this represents a critical challenge in biomaterials because of the hierarchical structures that comprise them and the spectra of timescales upon which they operate. Progress in integrating modeling and experiment in biomaterials represents progress towards harnessing them for engineering application. We present here a summary of the state of the art, and outlooks for the field as a whole.