- Bioprocess design
When perfusing fluid through a porous structure, flow will generally obey Darcy’s Law of permeability, which describes the relationship between pressure, length of flow, and the liquid’s velocity. To maintain a given rate of flow through a dense scaffold, higher pressures may be needed, resulting in shear stress that is intolerable to cells within the scaffold. Shear stress from fluid flow through a scaffold of just several millimeters in thickness can influence traits such as proliferation, differentiation, and extracellular matrix deposition. Additionally, filling of void space within the scaffold due to cell proliferation may result in dynamic shifts in fluid flow and shear forces that may need to be controlled to ensure uniform nutrient access and cell viability. The number of parameters at play in these environments makes the optimization of scaffold designs, media formulations, and bioprocess variables especially challenging for thick tissues.
Computational modeling can drastically reduce the amount of empirical experimentation that must be done to develop a deep enough understanding of a system to generate predictions for rational optimization. To accelerate R&D pertaining to cultivating thick tissues, computational fluid dynamic models using a variety of potential scaffold biomaterials, porosities, geometries, and parameters such as void space filling and medium viscosity is needed to better understand and predict the shear stress and fluid flow rates that cells may be subjected to within a scaffold. These models can inform the rational design of scaffolds and media optimization and, in turn, methods for scaffold fabrication and cell integration.
Combining these models with empirical experiments using different cell types can assist in understanding the effects of scaffold and bioprocess design on key aspects like cell proliferation, differentiation, maturation, and extracellular matrix deposition. Additionally, local microenvironments created as cells progressively occupy void space within scaffolds may necessitate the creation of sensor systems to dynamically adjust flow rates; computational models can inform such adaptive control systems.
Computational modeling can drastically reduce demand on scarce experimental resources by reducing the number of experiments required to optimize process variables. Computational models may fundamentally influence how the manufacturing of intact cultivated meat products is conducted. For instance, if a scaffold is designed to be embedded within a large-volume bioreactor, its maximum size may be limited due to shear stress or nutrient mass transfer limitations. This could influence the overall differentiation strategy, potentially favoring alternative bioreactor systems or scaffold designs that rely more heavily on innovative approaches like additive manufacturing technologies or in situ scaffold fabrication. Alternatively, modeling may suggest that large scaffolds can effectively be used that would accelerate the development of large perfusion bioreactors with embedded scaffolding that are currently of limited availability.
- Computational models in perfusion systems with scaffolds have been developed for the study of several tissues in regenerative medicine, including considerations for leveraging biophysical fluid forces exerted on cells to aid differentiation.
- The Cultivated Meat Modeling Consortium is a group of computational modelers and scientists with interest in modeling cell behavior on scaffolds. See their first white paper.
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Related GFI research grants
Learn about GFI grantee Dr. Sara Oliveira’s work 3D bioprinting scaffolds for cultivated meat the International Iberian Nanotechnology Laboratory in Portugal.
GFI is building plant-based tissue scaffolds for cultivated meat with Dr. Masatoshi Suzuki at University of Wisconsin, Madison
GFI’s 2021 Competitive Research Grant RFP is designed to catalyze whole-cut alt protein meat products—a critical step on the journey toward a safe and sustainable global food system.
Learn about Dr. Reza Ovissipour’s research using machine learning to optimize growth media for fish cells at Virginia Tech.
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