Identification of point of passage for cell culture

A problem presented at the UK MMSG Nottingham 2006.

Presented by:
Dr John Crowe (School of Electronic and Electrical Engineering, University of Nottingham)
Dr Melissa Mather (School of Electronic and Electrical Engineering, University of Nottingham)
Dr Rob Thomas (School of Mechanical and Manufacturing Engineering, University of Loughborough)
CJW Breward, J Crowe, S Eastburn, A Fletcher, J King, M Mather, R Thomas, A Walter

Problem Description

Over the past few years there has been much publicity about the possibilities of tissue engineering and regenerative medicine producing new and exciting treatments for a whole range of diseases and injuries. One difficulty is scaling up laboratory based cell and tissue culture into high throughput systems that could meet the demands of regenerative therapies.

One of the main purposes of cell culture is multiplication of cell numbers to produce appropriate quantities for therapy. In practice, there are only a certain number of cells that can be effectively grown in a given flask before they are no longer functional. Thus, once a flask has reached its capacity its cell population is split into multiple flaks and sub-cultured. This process is called passaging and it is important that passaging occurs when the cells are in their most proliferative state. Currently the point of passage is determined very subjectively. On a laboratory scale culture flasks are visually inspected to assess the area of the plate covered, cell connectivity and cell distribution.

The problem for the study group is development of a mathematical model to predict cell multiplication during culture with the aim of identification of the point of passage.

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Study Group Report

In this Study Group report a number of models of growing cell populations are considered. We first used a logistic growth model for a single cell type. We found that for small growth rates, k, it is possible to passage at lower passaging confluences, as the delay time, ts, is small in comparison to the growth time. For larger growth rates it is necessary to passage as few times as possible, because the delay time is large in comparison to the growth time. Therefore, it is optimal to passage at higher confluence.

We then considered presented a logistic growth model for multiple cell types. Here we noted that we should minimise the number of passages, np, as well as the time taken, T, to obtain the required number of cells, Nmax. For the simulation run we found good agreement with current tissue engineering protocol. Further work could be carried out on this model, with consideration given to how the results shown vary for dierent growth rates, k.

Finally we introduced a spatial model which we were unable to investigate within the scope of the Study Group. Further work on this model obviously exists and would be of interest to investigate.

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