Self-organization is the spontaneous formation of ordered patterns and networks from a population of comparatively simple elements or individuals with no prior information on neither the formation process nor the final organization. While the construction of tissues and organs is driven by the collective action of many eukaryotic cells, at a higher scale, social insects govern massive colonies via cooperative group behavior. In biological self-organization, unlike inanimate matter, interaction rules of elements are not constant but generally evolve in time and space in a history-dependent fashion. Recent technological advances in molecular biology, genetics, microscopy, and quantitative analysis have made it possible to follow the development of tissues, organs, and entire embryos at the single-cell level with high temporal resolution. Automated video tracking systems based on identification labels allow tracking of all members in insect colonies to identify individual interactions and patterns of social organization. These studies have shown that individuals must allocate themselves to actions or tasks in a dynamic manner following simple rules that incorporate local stimuli received directly from the environment and from interactions with other individuals. The majority of the available platforms provide observation only and do not allow manipulation of conditions for probing the self-organizing systems. Application of spatiotemporally resolved physical stimuli to individuals will reveal a more complete understanding of biological self-organization.
In this thesis, I have developed robotic manipulation platforms along with computational models to investigate the principles of self-organization in biological systems. I have worked on two complementary experimental models at different scales to prove the generality of my approach. The first robotic manipulation platform explores the transmission and transduction of mechanical signals within a connected fiber network and associated cellular responses. I developed a biocompatible magnetic microactuator and a magnetic control system to apply physiologically relevant traction forces to cells cultured on fibrous matrices. Along with the experimental platform, I have developed a finite element-modeling framework that simulates stresses on a digital copy of the fiber network. The second robotic manipulation platform controls the temperature on the nest floor of ant colonies. The system is integrated with a real-time tracking system that is capable of following interactions among hundreds of individuals. The ant colony is stimulated to transport their brood by changing the temperature of the next floor in a spatially patterned way. We explored how ants processed thermal signals, coordinated the transport of brood, and interacted with one another.