An in vitro - agent based modelling approach to optimisation of culture medium for generating muscle cells

Abstract

AbstractMany of the protocols in contemporary tissue engineering remain insufficiently optimised. Methodologies for culturing the complex structures of muscle tissue are particularly lacking, both in terms of quality and quantity of mature cells. Here, we analyse images from in vitro experimentation to quantify the effects of the composition of culture media on mouse-derived myoblast behaviour and myotube cell quality. Computational modelling was then used to predict the optimum range of media compositions for culturing. We define a metric of uniformity of myonuclei distribution as an early indicator of cell quality and difference in myonuclei density over time as an indicator of cell quantity. Analysis of live and static images of muscle cell differentiation revealed that changes in culture media result in significant changes in indicators of cell quantity and quality as well as changes in myoblast migratory behaviour. By describing cell behaviours as a set of functions of media composition we designed a model for predicting cell quality. Cell behaviours were taken directly from experimental images or inferred using Approximate Bayesian Computation and applied as inputs to an agent-based model of cell differentiation which predicted cell quality indicators. Our results suggest that culturing muscle cells in a neural cell differentiation medium reduces the quantity of cell fusion, but does not diminish cell quality. We also show that, while high concentrations of serum are detrimental to cell development, increasing serum concentration raises the total amount of myoblast fusion. This implies a trade-off between the quantity and quality of cells produced when choosing a culture medium. Our model provided a good prediction of experimental results for media with 5% serum provided the background cell proliferation rate was known.