Section: Research Program

Agent-based models

Agent-based systems consider each component individually. For example, in agent-based systems of multi-cellular systems the basic modeling unit is the cell, and each cell is considered [54] . This approach has advantages if the population of cells reveals inhomogeneities on small spatial scales as it occurs if organ architecture is represented [57] , or if the number of cells in a particular state is small. Different approaches have been used to model cellular agents in multi-cellular systems in space, roughly distinguished by lattice models (e.g. [62] ) and lattice-free (or off-lattice) models, in which the position of the cell can change gradually (e.g. [54] , [56] ). The dynamics of cells in lattice-based models is usually described by rules chosen to mimic the behavior of a cell including its physical behavior. The advantage of this approach is that it is simpler and simulation times for a given number of cells are shorter than in lattice-free models. In contrast, most lattice-free models attempt to parameterize cells by measurable values with a direct physical or biological meaning hence permitting identification of physiologically meaningful parameter ranges. This improves model simulation feasibility, since simulated parameter sensitivity analyses shows significant improvements when a high dimensional parameter space can be reduced. It also facilitates the development of systematic systems biology and systems medicine strategies to identify mechanisms underlying complex tissue organization processes ([7] ). Moreover, it is straightforward to include relevant signal transduction and metabolic pathways in each cell within the framework of agent-based models, which is a key advantage in the present times where the interplay of components at many levels is more and more precisely studied [19] .