Mohammad Mehrian1, Toon Lambrechts2, Marina Marechal3, Frank P Luyten3, Ioannis Papantoniou4, Liesbet Geris5. 1. Biomechanics Research Unit, GIGA In Silico Medicine, University of Liege, CHU - BAT 34, Quartier Hopital, Liege, Belgium; Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium. 2. Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; M3-BIORES, KU Leuven, Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium. 3. Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium. 4. Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; Skeletal Biology and Engineering Research Center, KU Leuven, Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; Institute of Chemical Engineering Science, Foundation of Research and Technology - Hellas (FORTH). 5. Biomechanics Research Unit, GIGA In Silico Medicine, University of Liege, CHU - BAT 34, Quartier Hopital, Liege, Belgium; Prometheus, the Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1 (+8), Leuven, Belgium; Biomechanics Section, KU Leuven, Leuven, Belgium. Electronic address: Liesbet.geris@uliege.be.
Abstract
BACKGROUND: Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics. METHODS: In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics. RESULTS: Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups. DISCUSSION: In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.
BACKGROUND:Human mesenchymal stromal cells (hMSCs) have become attractive candidates for advanced medical cell-based therapies. An in vitro expansion step is routinely used to reach the required clinical quantities. However, this is influenced by many variables including donor characteristics, such as age and gender, and culture conditions, such as cell seeding density and available culture surface area. Computational modeling in general and machine learning in particular could play a significant role in deciphering the relationship between the individual donor characteristics and their growth dynamics. METHODS: In this study, hMSCs obtained from 174 male and female donors, between 3 and 64 years of age with passage numbers ranging from 2 to 27, were studied. We applied a Random Forests (RF) technique to model the cell expansion procedure by predicting the population doubling time (PDT) for each passage, taking into account individual donor-related characteristics. RESULTS: Using the RF model, the mean absolute error between model predictions and experimental results for the PDT in passage 1 to 4 is significantly lower compared with the errors obtained with theoretical estimates or historical data. Moreover, statistical analysis indicate that the PD and PDT in different age categories are significantly different, especially in the youngest group (younger than 10 years of age) compared with the other age groups. DISCUSSION: In summary, we introduce a predictive computational model describing in vitro cell expansion dynamics based on individual donor characteristics, an approach that could greatly assist toward automation of a cell expansion culture process.
Authors: Sara Rolandsson Enes; Anna D Krasnodembskaya; Karen English; Claudia C Dos Santos; Daniel J Weiss Journal: Front Pharmacol Date: 2021-04-19 Impact factor: 5.810
Authors: Simon Hort; Laura Herbst; Niklas Bäckel; Frederik Erkens; Bastian Niessing; Maik Frye; Niels König; Ioannis Papantoniou; Michael Hudecek; John J L Jacobs; Robert H Schmitt Journal: Front Med (Lausanne) Date: 2022-06-06