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  • Multi‐objective Bayesian algorithm automatically discovers low‐cost high‐growth serum‐free media for cellular agriculture application
Title: Multi‐objective Bayesian algorithm automatically discovers low‐cost high‐growth serum‐free media for cellular agriculture application
Abstract In this work, we applied a multi‐information source modeling technique to solve a multi‐objective Bayesian optimization problem involving the simultaneous minimization of cost and maximization of growth for serum‐free C2C12 cells using a hyper‐volume improvement acquisition function. In sequential batches of custom media experiments designed using our Bayesian criteria, collected using multiple assays targeting different cellular growth dynamics, the algorithm learned to identify the trade‐off relationship between long‐term growth and cost. We were able to identify several media with more growth of C2C12 cells than the control, as well as a medium with 23% more growth at only 62.5% of the cost of the control. These algorithmically generated media also maintained growth far past the study period, indicating the modeling approach approximates the cell growth well from an extremely limited data set.  more » « less
Award ID(s):
2021132
PAR ID:
10466545
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Wiley
Date Published:
Journal Name:
Engineering in Life Sciences
Volume:
23
Issue:
8
ISSN:
1618-0240
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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