Combining Experiments and Computer Simulation to Improve the Stem Cell Differentiation Process

NIH RePORTER · NIH · R15 · $418,653 · view on reporter.nih.gov ↗

Abstract

ABSTRACT The long term goal of this work is to increase the yield as well as accelerate the process of stem cell differen- tiation. Herein we choose adipogenesis to illustrate our approach, because it is important in developing tissue replacements in situation that require soft tissue repair, including breast reconstruction. The goal is accomplished through a combination of experimental and computer simulation techniques that allow improved prediction and control of adipogenesis. A key element of this approach is a recently developed high-speed computer simulation of cellular and subcellular dynamics. The speed of the simulation allows the use of experimental observations in new ways, which allow 1) determination of the initial mechanical state of stem cells that lead to adipogenesis and 2) identification of areas in the differentiation process where time can be saved, thereby reducing the duration of adipogenesis. Knowing the initial mechanical states that lead to adipogenesis allows prescreening of stem cells so that the less viable ones can be eliminated, thereby increasing the process yield. The time history of the forces acting between cellular and subcellular structures and the fluid surrounding them during adipogenesis is examined to determine areas where the process time can be reduced. The high-fidelity, high-speed simulation produces these time histories in a short amount of time, which allows us to iteratively search for time savings and initial conditions and verify them using experiments. In this manner, the typical trial and error search for conditions favorable to adipogenesis is shifted to the simulation, rather than accomplished through lengthy experiments. This approach yields solutions for increasing the yield and decreasing the processing time for adipogenesis in much less time than a purely experimental or numerical approach could. The success of this study will yield a template for combining experimental and computer simulations that can be applied to increase understanding of many biological processes that affect health and quality of life.

Key facts

NIH application ID
10114755
Project number
1R15EB030842-01
Recipient
UNIVERSITY OF TEXAS ARLINGTON
Principal Investigator
Alan Paul Bowling
Activity code
R15
Funding institute
NIH
Fiscal year
2021
Award amount
$418,653
Award type
1
Project period
2021-09-01 → 2024-08-31