We present a multi-scale mathematical model and a novel numerical solver to study blood plasma flow and oxygen concentration in a prototype model of an implantable Bioartificial Pancreas (iBAP) that operates under arteriovenous pressure differential without the need for immunosuppressive therapy. The iBAP design consists of a poroelastic cell scaffold containing the healthy transplanted cells, encapsulated between two semi-permeable nano-pore size membranes to prevent the patient’s own immune cells from attacking the transplant. The device is connected to the patient’s vascular system via an anastomosis graft bringing oxygen and nutrients to the transplanted cells of which oxygen is the limiting factor for long-term viability. Mathematically, we propose a (nolinear) fluid–poroelastic structure interaction model to describe the flow of blood plasma through the scaffold containing the cells, and a set of (nonlinear) advection–reaction–diffusion equations defined on moving domains to study oxygen supply to the cells. These macro-scale models are solved using finite element method based solvers. One of the novelties of this work is the design of a novel second-order accurate fluid–poroelastic structure interaction solver, for which we prove that it is unconditionally stable. At the micro/nano-scale, Smoothed Particle Hydrodynamics (SPH) simulations are used to capture the micro/nano-structure (architecture) of cell scaffolds and obtain macro-scale parameters, such as hydraulic conductivity/permeability, from the micro-scale scaffold-specific architecture. To avoid expensive micro-scale simulations based on SPH simulations for every new scaffold architecture, we use Encoder–Decoder Convolution Neural Networks. Based on our numerical simulations, we propose improvements in the current prototype design. For example, we show that highly elastic scaffolds have a higher capacity for oxygen transfer, which is an important finding considering that scaffold elasticity can be controlled during their fabrication, and that elastic scaffolds improve cell viability. The mathematical and computational approaches developed in this work provide a benchmark tool for computational analysis of not only iBAP, but also, more generally, of cell encapsulation strategies used in the design of devices for cell therapy and bio-artificial organs.
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Robust and tunable performance of a cell-free biosensor encapsulated in lipid vesicles
Cell-free systems have enabled the development of genetically encoded biosensors to detect a range of environmental and biological targets. Encapsulation of these systems in synthetic membranes to form artificial cells can reintroduce features of the cellular membrane, including molecular containment and selective permeability, to modulate cell-free sensing capabilities. Here, we demonstrate robust and tunable performance of a transcriptionally regulated, cell-free riboswitch encapsulated in lipid membranes, allowing the detection of fluoride, an environmentally important molecule. Sensor response can be tuned by varying membrane composition, and encapsulation protects from sensor degradation, facilitating detection in real-world samples. These sensors can detect fluoride using two types of genetically encoded outputs, enabling detection of fluoride at the Environmental Protection Agency maximum contaminant level of 0.2 millimolars. This work demonstrates the capacity of bilayer membranes to confer tunable permeability to encapsulated, genetically encoded sensors and establishes the feasibility of artificial cell platforms to detect environmentally relevant small molecules.
more » « less- NSF-PAR ID:
- 10472759
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Precisely determining the intracellular concentrations of metabolites and signaling molecules is critical in studying cell biology. Fluorogenic RNA‐based sensors have emerged to detect various targets in living cells. However, it is still challenging to apply these genetically encoded sensors to quantify the cellular concentrations and distributions of targets. Herein, using a pair of orthogonal fluorogenic RNA aptamers, DNB and Broccoli, we engineered a modular sensor system to apply the DNB‐to‐Broccoli fluorescence ratio to quantify the cell‐to‐cell variations of target concentrations. These ratiometric sensors can be broadly applied for live‐cell imaging and quantification of metabolites, signaling molecules, and other synthetic compounds.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.add6605
