From individuals to emergence

Computational modelling of individual cells is an emerging field in computational science with great potential. Until recently many biological models were quite abstract compared to the cells which compose the real system. At the highest level of abstraction, individual cells are not represented at all but are modelled instead as a continuous mass which may flow and diffuse. These models are applied to larger systems such as tissues and tumours where the macroscopic behaviour can be well described analytically.

These models are useful but their abstractness can pose a problem. It is quite difficult to incorporate all but the most simple features of cells into analytical models and this is due to the fact that it is not always clear how individual cell behaviours translate into macroscopic effects. So even if the genetic regulation of a cell is understood there is no general means for translating this knowledge into behaviour at scales larger than a single cell. To build this bridge we opt to model individual cells whose characteristics are well under stood and. if possible, linked to a genetic control. By simulating a system of virtual cells we can observe how a change at the cellular level may effect the emergent patterns of the system. And since cells interact locally, these simulations are computationally simple with linear computation time in the number of cells.

We have adopted two biological systems to model. The first is a blastula (a primitive aggregate of cells resulting from the division of a fertilized egg) and the process of gastrulation under which blastocells under go major shape changes and migrations resulting in the more complex gastrula. We are modelling this system at the cell level using a cortical model for the individual cells, i.e. a model in which the cell membrane is the essential feature. Chemotaxis and adhesion are other features of the model.