Economical Media

Lead: Dr. David Kaplan

The overarching goal of Aim 5 is to develop serum-free media that is economically viable for cell proliferation and differentiation to optimize the development of low-cost and robust defined serum-free media.

• bioprocessing

• ingredients

• recycle

• molecular modeling

Bioactive Protein Sources – utilize bioprocessing (enzymatic digestion) methods to generate bioactive peptides for cost-effective serum-free media

Generate and Characterize Protein Hydrolysates – screen and characterize protein hydrolysates (e.g., from insects, algae, yeast, marine byproducts, plants, and agri-wastes – enzymatically hydrolyzed

Assessments in Cell Culture – evaluate protein hydrolysates towards muscle and fat cell culture goals

Modeling – apply machine learning to develop and optimize low-cost serum-free growth media for cell-lines to optimize processes

Figure 1. Progress in media development on the project. Activities include serum-free options and modes to replace or reduce growth factors using agricultural materials. This has resulted in a significant cost in media.
Figure 2. Optimizing media using machine learning to develop cost-effective cultivated meat requires different layers of research experiments including design of the experiments, different machine learning techniques, characterizing cells and measuring cells performance as input (Layer 1); mathematical modeling, and data validation (layer 2); and scaling up in reactor and 3D environment (Layer 3).
Figure 3. A comparison of different machine learning methods to optimize serum-free media for developing cost-effective cultivated meat. DOE was applied to design the media optimization experiments as data input for several different mathematical models and machine learning techniques including Response Surface Methodology (RSM), Artificial Neural Network (ANN), Random Forest (RF), Decision Tree (DT) and Hybrid Stepwise Selection. Based on the data set, RSM provided the most reliable model with the highest Regression Coefficient indicating that RSM could be applied for media optimization by reducing time, cost and energy input.