Pattern synthesis in a 3D agent-based model of stem cell differentiation

Embryonic stem cells (ESC) are generally regarded as the smallest functional units necessary to reproduce multicellular systems such as tissues and organs. Recent work showed that agent-based models of the stochastic dynamics of locally interacting stem cell agents accurately capture the time-dependent distributions of a variety of spatial patterns. Starting from a 3-dimensional, local interaction model of ESC proliferation and differentiation, in this paper, we developed a pattern classification and parameter optimization approach to maximize the occurrence of desired morphogenic patterns. Our approach uses Particle Swarm Optimization (PSO) and a pattern classification method that exploits a quantitative characterization of pattern formation. Since patterning likely imprints subsequent choices that stem cell aggregates make in lineage specification (e.g. precursors of neurons, lung cells, or muscle cells), our parameter optimization approach can be used to synthesize global differentiation patterns through local cues.