Scaffolds with a High Surface Area-to-Volume Ratio and Cultured Under Fast Flow Perfusion Result in Optimal O₂ Delivery to the Cells in Artificial Bone Tissues

Featured Application

Optimization of Scaffold Design and Flow Perfusion Culturing Conditions for Maximal Delivery of Oxygen to the Cells Embedded Deep Inside of Engineered Tissues.

Abstract

Tissue engineering has the potential for repairing large bone defects, which impose a heavy financial burden on the public health. However, difficulties with O₂ delivery to the cells residing in the interior of tissue engineering scaffolds make it challenging to grow artificial tissues of clinically-relevant sizes. This study uses image-based simulation in order to provide insight into how to better optimize the scaffold manufacturing parameters, and the culturing conditions, in order to resolve the O₂ bottleneck. To do this, high resolution 3D X-ray images of two common scaffold types (salt leached foam and non-woven fiber mesh) are fed into Lattice Boltzmann Method fluid dynamics and reactive Lagrangian Scalar Tracking mass transfer solvers. The obtained findings indicate that the scaffolds should have maximal surface area-to-solid volume ratios for higher chances of the molecular collisions with the cells. Furthermore, the cell culture media should be flown through the scaffold pores as fast as practically possible (without detaching or killing the cells). Finally, we have provided a parametric sweep that maps how the molecular transport within the scaffolds is affected by variations in rates of O₂ consumption by the cells. Ultimately, the results of this study are expected to benefit the computer-assisted design of tissue engineering scaffolds and culturing experiments.

1. Introduction

Incidences of bone disorders constitute a significant economic burden to societies globally. In the United States alone, the total annual cost (direct and indirect) of treating an estimated 126.6 million people affected by musculoskeletal disorders exceeds $213 billion [1]. Moreover, with an increasingly obese and ageing population, this trend is expected to continue further. Unfortunately, according to U.S Department of Health & Human Services, only ~10% out of the 115,000 people who needed a lifesaving organ transplant in 2018 have actually received it. This is because despite the overwhelming demand, almost no FDA approved [2] artificial tissue products are commercially available today.

A major hurdle standing in the way of producing viable engineered bone is product size limitations. These in turn stem from the inability to deliver sufficient amounts of metabolites (e.g., O₂, nutrients, etc.) to the inner pore spaces of scaffolds, given that the cells consume them in large quantities as they build tissue. Among these, O₂ plays a critical role in the cell growth and proliferation, and thus its high concentrations have been correlated with both increased cellularity [3] and cell viability [4]. Conversely, a deficiency in O₂ can result in a hypoxic cell state, which is commonly associated with decreased metabolic activity and potentially undesirable differentiation behavior [5,6,7]. Hence, optimal oxygen transport is important in maintaining tissue function and overall survival within the artificial tissues. For that reason, bone tissue engineering scaffolds are typically cultured in perfusion bioreactors, the idea behind which is to facilitate the mass transfer using flow.

However, understanding what scaffold fabrication parameters and flow culturing conditions result in the optimal O₂ delivery to the cells is made difficult by the complexity of the pore network architectures in which they reside. This is because most large scaffolds are not transparent enough for microscopy, and it is also difficult to measure the O₂ concentrations at different locations within the scaffolds. Furthermore, the O₂ uptake rate by the cells changes over time [3]. All these complications make the problem even more difficult to solve manually. For these reasons, computer simulation of the O₂ transport and consumption offers itself as a viable alternative for obtaining insight into the microenvironment, which is experienced by the cells seeded on the surfaces of the scaffold pores.

Yet, modeling of mass transport (and specifically of O₂) within scaffolds is uncommon when compared to flow parameters, such as stimulatory fluid shear stress, permeability, and pressure (see Table II in Ref. [8]). Furthermore,

Table 1. Literature overview of O₂ simulations in tissue engineering scaffolds, shows that image-based simulation of convection with diffusion and reaction has not yet been done.

Scaffold Type	Simulated Geometry	O2 Diffusion	O2 Convection	O2 Reaction	Varied Parameter	Citation
45S5 Bioglass-PCL Robocast, Bioactive Glass 70S30C Sol-Gel Foamed and Titania Foam Replicated	Micro-computed Tomography	Yes	No	No	Void Fraction	Fiedler et al. [12]
Cardiac Tissue Eng.	Idealized	Yes	Yes	No	Squeeze Pressure	Ferroni et al. [11]
Microchanneled Hydrogel	Idealized	Yes	No	No	Microchannel Configuration	Arrigoni et al. [13]
Periodically Self-Repeated Representative Volume Element	Idealized	Yes	No	Yes	Geometry of the Repeating Element	Li et al. [14]
Bone Tissue Eng. Molded Tantalum	Idealized	Yes	Yes	Yes	Flow rate	Bergemann et al. [4]
Homogeneous Porous Medium	Idealized	Yes	Yes	Yes	Flow rate, Porosity	Yan et al. [15]

below summarizes our overview of the few O₂ models that we did find in literature. From it, it can be seen that the studies commonly use idealized geometries for the scaffolds (e.g., a homogeneous porous medium) instead of realistic image-based. In reality, however, the scaffold architectures may be inhomogeneous. Moreover, many of the models either do not take into account specificities of bone tissue engineering, such as the need for flow perfusion, which generates a stimulatory shear environment natural to the bone canaliculi [9,10]. Instead, many models either target tissue engineering in general, or they may be specific to other tissue engineering disciplines; for example, Ferroni et al. [11] modeled a cardiac scaffold, which is cultured under pulsatile flow (not the case for bone). Finally, few of the models take into account O₂ consumption by the cells. And among those that do, the rate is typically assumed to be constant. Thus, we were not able to find a single bone tissue engineering model that accounted for all of the following: the realistic scaffold structure, O₂ diffusion, convection, and variable consumption rates.

Therefore, in this work we aim to shed insight on how scaffold manufacturing parameters and flow culturing conditions affect the O₂ transport and uptake by the cells in realistic bone tissue engineering scaffolds. To do this, we use two types of commonly-implemented types: the salt leached foam and the non-woven fiber mesh poly-L-lactic acid (PLLA) scaffolds. Their geometries are scanned in 3D using high resolution micro-computed tomography (µCT), and are imported into our image-based Lattice Boltzmann Method (LBM) flow [16,17,18,19] and reactive Lagrangian Scalar Tracking (rLST) mass transport [20] solvers. A big advantage of the latter is it can model particles with a range of reactivity, which is informative about how cells that are not necessarily starved for O₂ consume it. In this way, a more complete picture of O₂ transport within the different types of BTE scaffolds can be constructed. The overall computational scheme is depicted in Figure 1.

2. Materials and Methods

2.1. Scaffold Fabrication

The full details of the scaffold preparation protocols can be found in our previous publications [16,17]. Briefly, the scaffolds were non-woven fiber meshes constructed using polymer micro-fibers produced with spunbonding. The polymer used in the production of fibers was poly-L-lactic-acid (grade 6251D, 1.4% D enantiomer 108,500 MW, 1.87 PDI, NatureWorks LLC). A custom Brabender extruder (19.1 mm (0.75 in.) diameter × 381 mm length) was used to pressurize and melt the polymer. A manually circulated collection screen was used to collect a random even layering of fibers. Layers of fibers were stacked and measured until the stack reached a mass of 9.0 ± 0.1 g within an area of 162.8 cm². The collected non-woven fiber stack then had a 7 cm center cut sheet obtained from it. Finally, using an 8 mm diameter die, discs were punched from the layered fiber sheets. The resulting scaffolds used in culturing were 8 mm diameter and ~2.3 mm thickness. Average fiber diameter was measured optically, using a Nikon HFX-II microscope.

The porous foam scaffolds were prepared using solvent casting/particulate leaching method [21,22,23,24]. Briefly, poly-L-lactic acid (PLLA, 114,500 MW, 1.87 PDI, Birmingham Polymers) was dissolved into chloroform 5% w/v. The solution was then poured over a bed of sodium chloride crystals. Solvent was allowed to evaporate for 24 h. The resulting salt-polymer composite was inserted into an 8 mm diameter cylindrical mold and compressed at 500 psi. During compression, the composite was heated to 130 °C and held at constant temperature and pressure for 30 min. Using a diamond wheel saw (Model 650, South Bay Technology, Inc.), the resulting composite rod was cut into 2.3 mm thick discs. The discs were placed into deionized water (DIH₂O) under agitation for 2 days to leach out NaCl. Entire DIH₂O volumes were replaced twice per day. Leached discs were then removed from DIH₂O and placed under vacuum to remove moisture from the scaffolds. The resulting products were 8 mm diameter, 2.3 mm thick discs. Porosity of scaffolds was determined by measuring the solid volume (mass of the scaffold divided by the density of PLLA) and by comparing it to the total scaffold volume (assuming a cylindrical scaffold shape).

2.2. 3D Imaging and Virtual Reconstruction

The full details of our scanning procedure can be found in our previous publication [16,17,25]. Briefly, scaffolds were then scanned via µCT using a ScanCo VivaCT40 system (ScanCo Medical, Bassersdorf, Switzerland) to obtain 10 µm resolution, 2D intensity image slices at the optimum settings of 88 µA (intensity), and 45 kV (energy). The acquired X-ray images were filtered for noise reduction and assembled into 3D reconstructions of the scaffolds using a custom Matlab code (MathWorks Inc., Natick, MA). The scans were segmented using global thresholding. Threshold values were chosen such that the porosity of scaffolds from 3D reconstructions were within 1% of experimentally calculated porosities. Figure 2 is a typical 3D reconstruction of each scaffold type. Experimental porosities were obtained by measuring the solid volume (mass of the scaffolds divided by the density of scaffold materials) and comparing with total scaffold volume (assuming a cylindrical scaffold) as reported in [16,17,20].

2.3. Fluid Flow Modeling: Lattice Boltzmann Method (LBM)

LBM was chosen for the present application because it is especially appropriate for modeling pore-scale flow through porous media (such as scaffolds) due to the simplicity with which it handles complicated boundaries [16,17,18,20,26,27,28,29]. This is because LBM uses structured meshes for complex geometries, unlike classical CFD approaches, which will rather utilize unstructured meshes. Another advantage of LBM is that it uses a direct method based on first principles at the mesoscopic scale rather than modeling the terms of the fluid flow governing equations at the macroscopic scale. In addition, the LBM method has gained popularity within the scientific computing community because of the ease with which it can be parallelized on supercomputers [30].

A previously developed custom-written, in-house code was used in this work [16,17,18,20,26,29,31]. The D3Q15 lattice [32], in conjunction with the single-relaxation time Bhatnagar, Gross, and Krook [33] collision term approximation, was used to perform simulations. The no-slip boundary condition was applied at solid faces using the “bounce-back” technique [34]. To take advantage of the inherent LBM parallelizability, domains were decomposed using message passing interface [18,26]. The code has been validated for several flow cases for which analytical solutions are available: forced flow in a slit, flow in a pipe, and flow through an infinite array of spheres [16,26].

Each simulation domain was composed of a scaffold placed inside of a pipe. This is meant to mimic the cassette holder that typically fixes the scaffold in the perfusion bioreactors. The pipe’s length was taken to be approximately 10 times greater than the scaffold thickness, in order to avoid periodicity artifacts, and to ensure that a uniform parabolic profile is developed before the flow reaches the scaffolds. Simulations were performed for flow rates ranging between 0.15–1 mL/min. This is considered a suitable range for culturing bone tissue in typical perfusion bioreactors. Convergence was defined as when the average and highest velocities computed for the simulation domain vary by less than 0.01% for two consecutive time steps.

2.4. Oxygen Transport Modeling: Reactive Lagrangian Scalar Tracking (rLST)

The full details of the rLST code can be found in our prior publications [20,26]. Briefly, the trajectories of the rLST particles are determined by contributions from convection (obtained using the velocity field from the LBM simulations) and diffusion (i.e., Brownian motion obtained from a mesoscopic Monte-Carlo approach). For example, the new X position of a marker at time t+1 is calculated from the previous position at time t as follows:

$${\overrightarrow{X}}_{t+1}^{}={\overrightarrow{X}}_t^{}+{\overrightarrow{U}}_t^{(LBM)}\Delta t+\Delta {\overrightarrow{X}}_t^{(random)}$$

(1)

where ${\overrightarrow{U}}_t$ is the fluid velocity at the particle’s location at time t, as obtained from the LBM solver. On the other hand, the random jump has a standard deviation that is given by $\sigma =\sqrt{2D_0\Delta t}=\sqrt{2\nu \Delta t/Sc}$, where D₀ is the nominal molecular diffusivity (i.e., the diffusivity that the particles would have if their motion was purely Brownian). It can also be expressed in terms of the dimensionless Schmidt number Sc, which depends on the carrier fluid’s viscosity. The molecular diffusivity of O₂ in the cell culture medium (assumed to be an aqueous solution at the physiological temperature of T = 37 °C) was 2.62 × 10⁻⁵ cm²/s, which corresponded to a Schmidt number of 328.14.

The rLST simulations were performed using 1 million particles, which was found to be sufficient to reproduce analytical results from the Taylor–Aris formula, during the validation runs. Their initial positions were distributed uniformly in a release plane at the pipe’s entrance. Furthermore, in order to model the O₂ consumption by the cells, each of the rLST particles had a probability ‘q’ to react upon colliding with the scaffold walls: ranging from q = 0 (non-reactive) to q = 1 (fully reactive).

It was also assumed that the scaffold’s surface was uniformly covered with a monolayer of the cells, each of them capable of consuming the O₂. Since second order reactions (reactions between solute particles) were not considered for this model, any interactions between the rLST markers were neglected (i.e., they did not affect each other’s path). This approximation is good for a dilute solution. The simulation was allowed to evolve for a total of 10,000-time steps, which were needed to achieve equilibration. The ’Mersenne Twister’ random number generator with a cycle of length (2¹⁹⁹³⁷ −1) was used for all random number generation in the rLST code [35].

3. Results

As previously discussed, efficient delivery of O₂ is vital to the cell survival in the 3D bone tissue engineering scaffolds. Yet, choosing the optimal scaffold manufacturing parameters and the culturing flow conditions is non-trivial, due to the complex transport phenomena occurring in the pore networks of the engineered tissue constructs. For this reason, we have performed image-based simulation of the fluid flow and of the O₂ transport that occur within two common types of bone tissue engineering PLLA scaffolds: the salt-leached foam and the non-woven fiber mesh.

Their geometries were varied by controlling the amount and the size of the leached salt grains for the former, and the fiber diameter for the latter. In order to compare the scaffolds on the same scale, here the results are plotted as a function of the “specific surface area”—defined as the ratio of surface area to total volume (as opposed to just solid volume). Furthermore, in order to analyze how the transport of O₂ is affected by the rate at which it is consumed by the cells, we considered different probabilities with which its molecules can react upon colliding with the scaffold walls. Specifically, we examined the condition of a uniform coverage of the scaffold surfaces with O₂-starved cells in Figure 3, Figure 4, Figure 5 and Figure 6, while Figure 7 considers the case of the cells not starved for O₂. Namely, the former corresponds to an infinite surface reaction rate (i.e., instantaneous consumption of every O₂ molecule that collides with a scaffold wall). It should be treated as a limiting case scenario, which allows for a comparison of different scaffold structures on an equivalent basis.

Figure 3 is a plot of the O₂ “survival distance” in the stream-wise direction (the X-direction), as a function of the scaffold structure and the cell-culture media perfusion flow rate. The survival distance is defined as the distance that the rLST markers (representing the O₂ molecules) travel on average, until they are “consumed” via a collision with a scaffold wall. From this figure, it is apparent that the survival distance in the stream-wise direction increases as the flow rate goes up. This is consistent with the Taylor–Aris dispersion theory, which states that the effective diffusivity in the stream-wise direction should increase with the square of the Peclet number [20,34]:

$$P{e}_m=Re\ast Sc$$

(2)

where Re is the Reynolds number and Sc is the Schmidt Number. Its physical meaning is the ratio of the transport by advection to transport by diffusion. In this study, Sc is fixed for an O₂ molecule in an aqueous cell culture media at T = 37 °C, as was discussed in the methods section. Hence, according to Equation (2), the Peclet number will increase proportionally with the value of Re, which depends on the velocity of the fluid. This leads to a higher effective diffusivity of the solute in the stream-wise direction, and thus, the O₂ can make it further into the scaffold before it is fully consumed by the cells.

Another trend that is apparent from Figure 3 is that the survival distance of O₂ is also inversely proportional to the specific surface area of the scaffold. This makes sense, because the O₂ molecules have a smaller chance to collide with the scaffold’s surface when it has less area exposed. Furthermore, Figure 3 shows that the foam scaffolds have a lower specific surface area than the fiber ones. This leads to noticeably longer survival distances of O₂ in the foam scaffolds.

Overall, the result demonstrates that it is plausible to modify the scaffold’s geometry in order to increase the efficiency of the O₂ transport within its structure. To that end, Equation (3) shows how the surface area-to-total volume ratio ‘S’ of a fiber mesh scaffold is related to its porosity and fiber diameter (see Supplementary Materials for derivation):

$$S_{Fiber}=\frac{4\left(1-\epsilon \right)}{D_{Fiber}}$$

(3)

where D_Fiber is the fiber diameter and ε is the scaffold porosity. Similarly, Equation (4) shows how the surface area-to-total volume ratio ‘S’ of a foam scaffold is related to its porosity and salt grain diameter (see Supplementary Materials for derivation):

$$S_{Foam}=\frac{6\mathsf{\epsilon }}{D_{SaltGrain}}$$

(4)

where D_SaltGrain is the diameter of the salt grains used for leaching, and ε is the scaffold porosity. Therefore, for a known scaffold porosity and diameter combination, one can relate these geometric parameters to the surface area-to-total volume ratio of the scaffold (and thus, to the results of this manuscript). It is also important to note that while S_Fiber goes down with an increasing porosity ε, S_Foam displays the opposite behavior, as can be seen from the Equations (3) and (4).

Interestingly, the survival distances in the Y and Z directions (i.e., perpendicular to the flow) go down with the increasing flow rate (see Figure 4). The reason behind this is likely due to the particles becoming entrained by the carrier fluid. Consequently, they move less in the Y and Z directions per time step, relative to their displacement in the X. As a result, there is a smaller probability of collisions with the walls in the former directions. However, since both the Y and Z survival distances are an order of magnitude smaller than those in the X direction, the total survival distance is dominated by the trend displayed by the latter.

Another quantity of interest is the survival time of the O₂ molecules in the scaffolds. Figure 5 plots this quantity as a function of the flow rate and the specific surface area of the scaffolds for the limiting case of fully reactive rLST particles. Conversely to the survival distance in Figure 3, the survival time in Figure 5 decreases with the flow rate, though the effect of the specific surface area remains the same, and the survival time varies inversely-proportionally to it. Combining the results from both of the figures, it becomes apparent that fully reactive molecules get carried to a farther distance by a higher flow rate. However, they take a shorter time to get consumed by the cells on the surface of the scaffolds. This is especially true for the scaffold geometries with a higher exposed surface area.

Ultimately, it is of interest to measure the effective first order reaction rate constant k_eff, with which the O₂ molecules get consumed in the scaffolds, after accounting for the mass transfer limitations. To that end, Figure 6 is a plot of k_eff as a function of the flow rate and specific surface area of the scaffold for the limiting case of an infinitely fast surface consumption of O₂. It reaffirms the previously observed trends, which show that the O₂ is consumed faster in the scaffold structures with the higher surface area-to-solid volume ratios. Furthermore, it also supports the finding that increasing the cell culture media flow rate through the scaffold leads to a faster O₂ consumption in its pores.

If the cells are not starved for O₂, however, the rLST particles can be made to have a finite (as opposed to infinite) probability of becoming consumed upon collision with the scaffold walls. Thus, Figure 7 explores the role that the different cell affinities for consuming the O₂ have on its transport in the pores. In this case, the rLST particles with the different reactivities are all released simultaneously, and their survival times are compared as a function of the cell culture media flow rate and the scaffold type.

Two trends are immediately apparent from Figure 7: 1) the O₂ in the fiber scaffolds (solid lines) has a shorter survival time than in the foam ones (dotted lines), regardless of the cells’ affinity for its uptake. This is consistent with the trends in the previous section, which showed that the fiber scaffolds have a higher specific surface area than the foams. This makes them more efficient at delivering the O₂ molecules to the cells; and 2) the second trend essentially says that for a given surface reaction rate, the consumption of O₂ will take longer at the slower flows. This is again consistent with a similar trend that was shown in Figure 5, where the survival time increased with the slower flow rate.

4. Discussion

In this manuscript, we carried out a study of how the O₂ mass transfer is affected by the scaffold manufacturing and the flow perfusion culturing parameters in bone tissue engineering scaffolds. The knowledge obtained from the reported results is needed in order to overcome the product-size limitations, which are commonly experienced due to hypoxia and necrosis in the center of large scaffolds. To solve the problem, we used an image-based approach of scanning the true scaffold structures in 3D using a high resolution µCT, and then fed the obtained geometries to our in-house parallelized fluid flow (LBM) and mass transport (rLST) solvers. Two scaffolds commonly used in bone tissue engineering, the salt leached foam and the nonwoven fiber mesh, were used for this study. The O₂ transport results were parametrized as a function of the specific surface area of the scaffold, the flow rate in the bioreactor, and the affinity to consume the molecule by the cells.

The main conclusions from our work are that the scaffolds with the higher specific area (i.e., surface area-to-solid volume ratio) result in more frequent molecular collisions with the cells. This increases the chances of the O₂ uptake by them, which results in a higher consumption (i.e., shorter survival times and distances) of the molecules in the scaffold. Furthermore, by increasing the flow rate in the bioreactor, the O₂ transport can be both facilitated (as seen from the shorter survival time in Figure 4) and delivered deeper into the scaffold (as seen from the longer survival distance in Figure 3). Thus, the overall effective O₂ reaction coefficient k_eff can be increased by maximizing both the specific surface area of the scaffold and the flowrate through its pores. This is evident from Figure 6.

Interestingly, the conclusion that an increased flow rate improves the O₂ delivery to the cells has also been reported by Bergemann et al. [4]. However, increasing it indefinitely is not an option because there is a trade-off with the shear forces exerted on the cells by the flow. Specifically, values in the range of 0.1–25 dynes/cm² [36,37,38] are generally considered to be beneficial because they mimic the natural microenvironment in bone canaliculi [9,10] and have been shown to promote tissue regeneration [39,40,41,42]. On the other hand, an excessive shear in the range of 26–54 dynes/cm² and higher can cause cell lysing and/or detachment from the scaffold [43,44]. Therefore, there is some optimal flow rate, which was found to be 45 μL/min by the Bergemann et al. study [4]. However, this value is specific to their scaffold-and-cell combination, and it could vary for other alternatives. Therefore, both image-based numerical simulations and cell viability assays are necessary for tuning the optimal conditions for other types of cultures. Whereas, at least the physical understanding provided in our study should be applicable across all scaffold types because they are expressed in terms of the specific surface area.

Finally, Figure 7 in our manuscript provides insight into how the O₂ transport depends on its consumption by the cells. In this case, we are not assuming that the cells are O₂-starved (and as a result take up every oxygen molecule that collides with them). Instead, we vary consumption rate of the O₂ at the scaffold surface in order to measure how this affects its transport in the scaffold. The results reported in this figure allow other researchers to understand the changes in the scaffold’s transport microenvironment over time. They also show how the flowrate and specific surface area trends are affected by the cell-specific O₂ consumption rates.

The limitations of our study are that the cells are assumed to have a uniformed coverage on the scaffold surface. In reality, they would likely prefer some areas of the scaffold more than others. Furthermore, the tissue they lay down and the forces they exert on the scaffold would likely alter the internal structure of the scaffold (and in turn the flow field in its pores). Unfortunately, our model does not account for these types of influences by the cells on the mass transfer of O₂. Additionally, the scaffold’s structure in the real experiment could change due to wetting forces, fiber flexibility, and the natural degradation of PLLA. The latter produces acidic byproducts, whose removal is facilitated by the fluid flow [45], yet in our study, the scaffold’s structure remains static throughout the virtual experiment.

However, the results presented in this manuscript expose qualitative trends that should hold true regardless of the necessary simplifications. Therefore, it is expected that they should contribute significantly to the field of bone tissue engineering scaffold design and experiment optimization.

5. Conclusions

Mass transfer of O₂ in scaffolds cultured in perfusion bioreactors is of interest to the fields of bone and other types of tissue engineering. Specifically, understanding how to control it can be instrumental to resolving product size limitations when it comes to culturing organ-sized scaffolds. Therefore, we performed an image-based simulation study, in which we showed that the scaffolds with the higher surface area-to-solid volume ratio result in a more efficient transfer of O₂. Additionally, we showed that the effect can be increased further by flowing the cell culture media through the scaffold faster. Serendipitously, this also delivers the O₂ deeper into the scaffold pores, which is key to overcoming the product-size limitations mentioned earlier. Furthermore, we provided a parametric sweep over the rates of O₂ consumption by the cells situated on the scaffold surfaces. This visual aid yields insight into how the different cell affinities for consuming the O₂ can affect the molecule’s transport through the biological porous media. Finally, the computational framework presented in this study can serve as a viable tool for optimizing the scaffold design and experimental culturing protocols for other types of tissue engineering as well.