Motivated by the ability of living cells to form specific shapes and structures, we present a computational approach using distributed genetic programming to discover cell-cell interaction rules for automated shape composition. The key concept is to evolve local rules that direct virtual cells to produce a self-organizing behavior that leads to the formation of a macroscopic, user-defined shape. The interactions of the virtual cells, called Morphogenic Primitives (MPs), are based on chemotaxis-driven aggregation behaviors exhibited by actual living cells. Cells emit a chemical into their environment. Each cell responds to the stimulus by moving in the direction of the gradient of the cumulative chemical field detected at its surface. MPs, though, do not attempt to completely mimic the behavior of real cells. The chemical fields are explicitly defined as mathematical functions and are not necessarily physically accurate. The functions are derived via a distributed genetic programming process. A fittness measure, based on the shape that emerges from the chemical-field-driven aggregation, determines which functions will be passed along to later generations. This paper describes the cell interactions of MPs and a distributed genetic programming method to discover the chemical fields needed to produce macroscopic shapes from simple aggregating primitives.