Ipsita Banerjee

Perfusion-decellularized pancreas as a natural 3D scaffold for pancreatic tissue and whole organ engineering.

Approximately 285 million people worldwide suffer from diabetes, with insulin supplementation as the most common treatment measure. Regenerative medicine approaches such as a bioengineered pancreas has been proposed as potential therapeutic alternatives. A bioengineered pancreas will benefit from the development of a bioscaffold that supports and enhances cellular function and tissue development. Perfusion-decellularized organs are a likely candidate for use in such scaffolds since they mimic compositional, architectural and biomechanical nature of a native organ. In this study, we investigate perfusion-decellularization of whole pancreas and the feasibility to recellularize the whole pancreas scaffold with pancreatic cell types. Our result demonstrates that perfusion-decellularization of whole pancreas effectively removes cellular and nuclear material while retaining intricate three-dimensional microarchitecture with perfusable vasculature and ductal network and crucial extracellular matrix (ECM) components. To mimic pancreatic cell composition, we recellularized the whole pancreas scaffold with acinar and beta cell lines and cultured up to 5 days. Our result shows successful cellular engraftment within the decellularized pancreas, and the resulting graft gave rise to strong up-regulation of insulin gene expression. These findings support biological utility of whole pancreas ECM as a biomaterials scaffold for supporting and enhancing pancreatic cell functionality and represent a step toward bioengineered pancreas using regenerative medicine approaches.

Mathematical model for evaluation of mass transfer limitations in phenol biodegradation by immobilized Pseudomonas putida

A mathematical model is proposed to analyze the mass transfer limitations in phenol biodegradation using Pseudomonas putida immobilized in calcium alginate. The model takes into account internal and external mass transfer limitations, substrate inhibition kinetics and the dependence of the effective diffusivity of phenol in alginate gel on cell concentration. The model is validated with the experimental data from batch fermentation. The effect of various operating conditions such as initial phenol concentration, initial cell loading, alginate gel loading on the biodegradation of phenol is experimentally demonstrated. Phenol degradation time is found to decrease initially and reach stationary value with increase in cell loading as well as gel loading. The model predicts these trends reasonably well and shows the presence of external mass transfer limitations. A new concept of effectiveness factor is introduced to analyze the overall performance of batch fermentation.

Parametric process synthesis for general nonlinear models

This paper presents a new approach towards parametric analysis of MINLP models in the context of process synthesis problems under uncertainty. The approach is based on the idea of High Dimensional Model Representation technique which utilize a reduced number of model runs to build an uncertainty propagation model that expresses the variability of optimal solution in the uncertain space. Based on this idea, a systematic procedure is developed where in the first step the possible changes in the optimal design configurations due to parametric uncertainty are identified. In the next step, the variability of optimal solution with parameter uncertainty for each design is captured. Having obtained a parametric expression of optimal objective for each design, the optimal solution can be determined by comparing the solutions for different designs. The proposed approach provides information about variation of the optimal objective and optimal design configuration over the entire uncertain space. This information can then be judiciously utilized in any decision making depending on specific process requirements. The main advantage of the proposed approach is that it does not depend on the nature or existence of a mathematical model to describe the input-output relationship of the process.

Computationally efficient black-box modeling for feasibility analysis

Computational cost is a major issue in modern large-scale simulations used across different disciplines of science and engineering. Computationally efficient surrogate models that can represent the original model with desired accuracy have been explored in the recent past. However, with the exception of few efforts, most of these techniques rely on a reduced order representation of the original complex model, resulting in a loss of information. In this paper we demonstrate the applicability of high dimensional model representation (HDMR) technique in addressing this issue while preserving the original model dimension. We will discuss the applicability of this surrogate modeling technique in the field of feasibility analysis drawing examples from process systems and materials design. It will be shown that the original physical models can be essentially considered as a black box, and same methodology can be applied across all the examples studied. It is found that the accuracy of the surrogate models depends on the order of the approximation and number of sampling points employed. While first-order approximation is largely inadequate, second-order approximation is sufficient for the model systems studied. Sampling requirement is also dramatically low for the construction of these surrogate models.

Development of an adaptive chemistry model considering micromixing effects

Abstract In numerical simulation of combustion models, solution of the chemical kinetics is often the most expensive part of the calculation, since accurate description of kinetic mechanism involves large number of species and reactions, leading to a large set of coupled ODEs, often too complex to be considered in their entirety along with a detailed flow simulation. Hence the need for representing the complex chemical reactions by simple reduced models, which can retain considerable accuracy while rendering computational feasibility. Realistically, under different conditions and at different points in time, different reactions become important, which has been exploited to develop an adaptive reduction scheme such that the reduced reaction model adapts itself to the changing reactor conditions.A methodology is developed in this paper to construct reduced mechanisms by solving an optimization problem, where the objective is to determine the range of conditions along the reaction trajectory over which a prespecified number of reactions can predict the actual profile within an allowable tolerance. Such an adaptive reduced mechanism is then coupled with the reactive flow algorithm, which selects an appropriate mechanism depending on reactor condition and integrate the corresponding ODEs for the specified valid range. These ideas are demonstrated using the mechanism of CO/H2 combustion in air.

Early differentiation patterning of mouse embryonic stem cells in response to variations in alginate substrate stiffness

BackgroundEmbryonic stem cells (ESCs) have been implicated to have tremendous impact in regenerative therapeutics of various diseases, including Type 1 Diabetes. Upon generation of functionally mature ESC derived islet-like cells, they need to be implanted into diabetic patients to restore the loss of islet activity. Encapsulation in alginate microcapsules is a promising route of implantation, which can protect the cells from the recipient’s immune system. While there has been a significant investigation into islet encapsulation over the past decade, the feasibility of encapsulation and differentiation of ESCs has been less explored. Research over the past few years has identified the cellular mechanical microenvironment to play a central role in phenotype commitment of stem cells. Therefore it will be important to design the encapsulation material to be supportive to cellular functionality and maturation.ResultsThis work investigated the effect of stiffness of alginate substrate on initial differentiation and phenotype commitment of murine ESCs. ESCs grown on alginate substrates tuned to similar biomechanical properties of native pancreatic tissue elicited both an enhanced and incrementally responsive differentiation towards endodermal lineage traits.ConclusionsThe insight into these biophysical phenomena found in this study can be used along with other cues to enhance the differentiation of embryonic stem cells toward a specific lineage fate.

Impact of co-culture on pancreatic differentiation of embryonic stem cells.

Promise of cellular therapy for type 1 diabetes has inspired the search for transplantable cell sources, and embryonic stem cells (ESCs) have emerged as strong candidates. We have developed a directed differentiation protocol to obtain insulin‐producing cells from ESCs. The ESCs are first induced towards a homogeneous monolayer of definitive endoderm‐like cells by co‐culture with primary hepatocytes. Pancreatic commitment is induced by plating the ESC‐derived endoderms on Matrigel, along with Sonic hedgehog inhibition and retinoid induction. More than 70% of differentiated cells positively upregulated Pdx‐1, along with pro‐endocrine transcription factors Ngn3, β2/neroD1, Nkx2.2 and Nkx6.1. Final maturation to islet‐specific cells is achieved by co‐culturing the ESC‐derived pancreatic endocrine cells with endothelial cells, which resulted in Insulin 1 upregulation in 60% of the cell population, along with high levels of IAPP and Glut2. The differentiated cell population also secreted high levels of insulin. Our findings illustrate the significant effect of co‐culture in different stages of differentiation and maturation of ESCs in vitro. Such a high yield of pancreatic islet cells has not yet been reported. Our findings establish a robust protocol for islet differentiation. Copyright

Inducing endoderm differentiation by modulating mechanical properties of soft substrates

Early embryonic stem cell (ESC) differentiation is marked by the formation of three germ layers from which all tissues types arise. Conventionally, ESCs are differentiated by altering their chemical microenvironment. Recently however, it was established that a mechanical microenvironment can also contribute towards cellular phenotype commitment. In this study, we report how the cellular mechanical microenvironment of soft substrates affects the differentiation and phenotypic commitment of ESCs. Mouse ESCs were cultured in a fibrin hydrogel matrix in 2D and 3D cultures. The gelation characteristics of the substrates were modulated by systematically altering the fibrinogen concentration and the fibrinogen‐thrombin crosslinking ratio. Analysis of the ESCs cultured on different substrate conditions clearly illustrated the strong influence that substrate physical characteristics assert on cellular behaviours. Specifically, it was found that ESCs had a higher proliferation rate in gels of lower stiffness. Early differentiation events were studied by analyzing the gene and protein expression levels of early germ layer markers. Our results revealed that lower substrate stiffness elicited stronger upregulation of endoderm related genes Sox17, Afp and Hnf4 compared to stiffer substrates. While both 2D and 3D cultures showed a similar response, the effects were much stronger in 3D culture. These results suggest that physical cues can be used to modulate ESC differentiation into clinically relevant tissues such as liver and pancreas. Copyright

Extracellular Matrix Aggregates from Differentiating Embryoid Bodies as a Scaffold to Support ESC Proliferation and Differentiation

Embryonic stem cells (ESCs) have emerged as potential cell sources for tissue engineering and regeneration owing to its virtually unlimited replicative capacity and the potential to differentiate into a variety of cell types. Current differentiation strategies primarily involve various growth factor/inducer/repressor concoctions with less emphasis on the substrate. Developing biomaterials to promote stem cell proliferation and differentiation could aid in the realization of this goal. Extracellular matrix (ECM) components are important physiological regulators, and can provide cues to direct ESC expansion and differentiation. ECM undergoes constant remodeling with surrounding cells to accommodate specific developmental event. In this study, using ESC derived aggregates called embryoid bodies (EB) as a model, we characterized the biological nature of ECM in EB after exposure to different treatments: spontaneously differentiated and retinoic acid treated (denoted as SPT and RA, respectively). Next, we extracted this treatment-specific ECM by detergent decellularization methods (Triton X-100, DOC and SDS are compared). The resulting EB ECM scaffolds were seeded with undifferentiated ESCs using a novel cell seeding strategy, and the behavior of ESCs was studied. Our results showed that the optimized protocol efficiently removes cells while retaining crucial ECM and biochemical components. Decellularized ECM from SPT EB gave rise to a more favorable microenvironment for promoting ESC attachment, proliferation, and early differentiation, compared to native EB and decellularized ECM from RA EB. These findings suggest that various treatment conditions allow the formulation of unique ESC-ECM derived scaffolds to enhance ESC bioactivities, including proliferation and differentiation for tissue regeneration applications.

Model Independent Parametric Decision Making

Accurate knowledge of the effect of parameter uncertainty on process design and operation is essential for optimal and feasible operation of a process plant. Existing approaches dealing with uncertainty in the design and process operations level assume the existence of a well defined model to represent process behavior and in almost all cases convexity of the involved equations. However, most of the realistic case studies cannot be described by well characterised models. Thus, a new approach is presented in this paper based on the idea of High Dimensional Model Reduction technique which utilize a reduced number of model runs to build an uncertainty propagation model that expresses process feasibility. Building on this idea a systematic iterative procedure is developed for design under uncertainty with a unique characteristic of providing parametric expression of the optimal objective with respect to uncertain parameters. The proposed approach treats the system as a black box since it does not rely on the nature of the mathematical model of the process, as is illustrated through a number of examples.