Synthetic gene circuits for cell state detection and protein tuning in human pluripotent stem cells.

During development, cell state transitions are coordinated through changes in the identity of molecular regulators in a cell type- and dose-specific manner. The ability to rationally engineer such transitions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein, we report the generation of synthetic gene circuits that can detect a desired cell state using AND-like logic integration of endogenous miRNAs (classifiers) and, upon detection, produce fine-tuned levels of output proteins using an miRNA-mediated output fine-tuning technology (miSFITs). Specifically, we created an "hPSC ON" circuit using a model-guided miRNA selection and circuit optimization approach. The circuit demonstrates robust PSC-specific detection and graded output protein production. Next, we used an empirical approach to create an "hPSC-Off" circuit. This circuit was applied to regulate the secretion of endogenous BMP4 in a state-specific and fine-tuned manner to control the composition of differentiating hPSCs. Our work provides a platform for customized cell state-specific control of desired physiological factors in hPSC, laying the foundation for programming cell compositions in hPSC-derived tissues and beyond.

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex.

Dynamic reciprocity revisited.

The cellular microenvironment - which includes the cells, extracellular matrix (ECM), and local transport processes - affects the cell which in turn responds by synthetic or degradative processes causing the composition and the structure of ECM, and the local transport processes, to change which in a coupled manner influence the cell, and so forth.

Virtual cells in a virtual microenvironment recapitulate early development-like patterns in human pluripotent stem cell colonies.

The mechanism by which morphogenetic signals engage the regulatory networks responsible for early embryonic tissue patterning is incompletely understood. Here, we developed a minimal gene regulatory network (GRN) model of human pluripotent stem cell (hPSC) lineage commitment and embedded it into "cellular" agents that respond to a dynamic morphogenetic signaling microenvironment. Simulations demonstrated that GRN wiring had significant non-intuitive effects on tissue pattern order, composition, and dynamics. Experimental perturbation of GRN connectivities supported model predictions and demonstrated the role of OCT4 as a master regulator of peri-gastrulation fates. Our so-called GARMEN strategy provides a multiscale computational platform to understand how single-cell-based regulatory interactions scale to tissue domains. This foundation provides new opportunities to simulate the impact of network motifs on normal and aberrant tissue development.

Hand-mixed and premixed antibiotic-loaded bone cement have similar homogeneity.

Since low-dose antibiotic-loaded bone cement (ALBC) was approved by the FDA for second-stage reimplantation after infected arthroplasties in 2003, commercially premixed low-dose ALBC has become available in the United States. However, surgeons continue to mix ALBC by hand. We presumed hand-mixed ALBC was not as homogeneous as commercially premixed ALBC. We assessed homogeneity by determining the variation in antibiotic elution by location in a batch, from premixed and hand-mixed formulations of low-dose ALBC. Four hand-mixed methodologies were used: (1) suspension--antibiotic powder in the liquid monomer; (2) no-mix--antibiotic powder added but not mixed with the polymer powder before adding monomer; (3) hand-stirred--antibiotic powder stirred into the polymer powder before the monomer was added; and (4) bowl-mix--antibiotic powder mixed into polymer powder using a commercial mixing bowl before the monomer was added. Antibiotic elution was measured using the Kirby-Bauer bioassay. None of the mixing methods had consistently dissimilar homogeneity of antibiotic distribution from the others. Based upon our data we conclude hand-mixed low-dose ALBC is not less homogeneous than commercially premixed formulations.

DP2 antagonism reduces airway smooth muscle mass in asthma by decreasing eosinophilia and myofibroblast recruitment.

Increased airway smooth muscle mass, a feature of airway remodeling in asthma, is the strongest predictor of airflow limitation and contributes to asthma-associated morbidity and mortality. No current drug therapy for asthma is known to affect airway smooth muscle mass. Although there is increasing evidence that prostaglandin D2 type 2 receptor (DP2) is expressed in airway structural and inflammatory cells, few studies have addressed the expression and function of DP2 in airway smooth muscle cells. We report that the DP2 antagonist fevipiprant reduced airway smooth muscle mass in bronchial biopsies from patients with asthma who had participated in a previous randomized placebo-controlled trial. We developed a computational model to capture airway remodeling. Our model predicted that a reduction in airway eosinophilia alone was insufficient to explain the clinically observed decrease in airway smooth muscle mass without a concomitant reduction in the recruitment of airway smooth muscle cells or their precursors to airway smooth muscle bundles that comprise the airway smooth muscle layer. We experimentally confirmed that airway smooth muscle migration could be inhibited in vitro using DP2-specific antagonists in an airway smooth muscle cell culture model. Our analyses suggest that fevipiprant, through antagonism of DP2, reduced airway smooth muscle mass in patients with asthma by decreasing airway eosinophilia in concert with reduced recruitment of myofibroblasts and fibrocytes to the airway smooth muscle bundle. Fevipiprant may thus represent a potential therapy to ameliorate airway remodeling in asthma.

On the genealogy of tissue engineering and regenerative medicine.

In this article, we identify and discuss a timeline of historical events and scientific breakthroughs that shaped the principles of tissue engineering and regenerative medicine (TERM). We explore the origins of TERM concepts in myths, their application in the ancient era, their resurgence during Enlightenment, and, finally, their systematic codification into an emerging scientific and technological framework in recent past. The development of computational/mathematical approaches in TERM is also briefly discussed.

Synergistic activity of polarised osteoblasts inside condensations cause their differentiation.

Condensation of pre-osteogenic, or pre-chondrogenic, cells is the first of a series of processes that initiate skeletal development. We present a validated, novel, three-dimensional agent-based model of in vitro intramembranous osteogenic condensation. The model, informed by system heterogeneity and relying on an interaction-reliant strategy, is shown to be sensitive to 'rules' capturing condensation growth and can be employed to track activity of individual cells to observe their macroscopic impact. It, therefore, makes available previously inaccessible data, offering new insights and providing a new context for exploring the emergence, as well as normal and abnormal development, of osteogenic structures. Of the several stages of condensation we investigate osteoblast 'burial' within the osteoid they deposit. The mechanisms underlying entrapment--required for osteoblasts to differentiate into osteocytes--remain a matter of conjecture with several hypotheses claiming to capture this important transition. Computational examination of this transition indicates that osteoblasts neither turn off nor slow down their matrix secreting genes--a widely held view; nor do they secrete matrix randomly. The model further reveals that osteoblasts display polarised behaviour to deposit osteoid. This is both an important addition to our understanding of condensation and an important validation of the model's utility.

A multi-paradigm modeling framework to simulate dynamic reciprocity in a bioreactor.

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications.