The co-existence of two of phenotypically distinct subpopulations suggests that the phenotypic conversion of individual cells follows a bistable dynamics. A cell can be considered as bistable if under the same conditions it can adopt one of two different and stable phenotypes with the intermediate states being unstable. Bistability may arise from the internal dynamical properties of gene networks that bring about the phenotype. Although gene regulatory networks are usually complicated, in the simplest cases a single regulatory loop is sufficient to allow two stable alternative states, attractors, with different active and silenced genes. The probability of a cell with bistable properties to adopt one or the other phenotypic state is specified by the regulatory parameters of the system, more specifically by the threshold separating the two stable states. However, the transition Paclitaxel between the states is triggered by the noise arising from the stochastic nature of molecular interactions and the frequency of the phenotypic switches is dependent of the noise level. As a result, in a population of bistable cells, the proportion of the two possible phenotypes reflects the regulatory properties of the underlying gene network, while the velocity to reach the phenotype depends on the noise level. The systematically observed high CD56+/ CD562 cell ratio in the myoblasts suggests that the equilibrium between the two possible states is biased and the cells are more prone to become CD56+. However, the observation that CD56+ cells relaxed Regorafenib abmole bioscience faster to the CD562 phenotype than the opposite contradicts this. In addition, the bistability of the individual cells cannot explain their non-random spatial localization within the population. This is only possible if the cells can sense the local cell density and respond to it by changing their phenotype. In order to understand how the generic principles of bistability and the capacity of sensing the local cell density bring together the dynamical properties observed in our muscle derived cell system we performed computer simulations. The aim of the simulations was to produce qualitative rather than quantitative predictions on the behaviour of the system. We focused our attention on the effect the cell density may have on the regulatory parameters of the bistable phenotypic transition, on the noise that triggers the change and the possible impact of the spatial patterns formed by the cells. We first designed an agent-based model that faithfully reproduced the formation of regions with variable cell densities and wave-like alignments observed in myoblast cultures.