Our framework incorporates features missing from other existing biological neural network models, including the possibility to trace the fate of an individual cell in the system as it develops, register the changes of a large number of different biologically relevant parameters, and describe the cell population behavior as a whole. The Biological Cellular Neural Network Modeling (BCNNM) approach uses a predicate description of sequences of biochemical reactions and it can be used to run complex models of multilayer neural network formation from a limited number of stem cells. A model run recapitulates crucial cellular events in the development of the nervous system. The BCNNM framework is implemented based on the principles of dynamic modeling with the possibility of probabilistic processes description.

Biological mechanisms can be characterized comprehensively and users can choose the desired detail for the processes described (from shifts in intracellular enzyme concentrations to interactions of cell groups forming high-level multicellular structures). We can build structures with sparse and specific connections, create models of signal conduction within such structures, and reveal peculiarities of their work.

The framework can be used for thorough in silico replication of in vitro experiments aimed to obtain exhaustive sets of measurements from all key components (cells, their compartments, synapses etc.), providing new data for neural research. Preliminary computational testing of novel hypotheses is another important application that will reduce the cost of wet lab experiment setups and accelerate the research pipeline.