From neural networks to cell signalling: Chemical communications among cell populations

Artificial Neural Networks abstract the chemical events at synapses into simple transfer Junctions, usually sigmoid in shape. Through a variable topological connectedness they are capable of learning, optimizing recognizing patterns and other pseudo-cognitive functions. Most cells in the organism communicate by chemical signals of a variety of types, neurons being special because of their highly specialized anatomy. Other cell networks have been recognized in the immune system, for example. The ability of cells to communicate by a variety of chemical signals suggests that basic neural networks can be modified and extended to encompass other types of cell signalling. This work will illustrate this with an example, namely a model of chemical communication between stereotypical cells in a solid tumor as a systems model of cancer. The model shows that populations dynamics observed in real tumors can be generated with a minimum of molecular detail and specificity about the chemical signals between cells.