Recent developments in nanotechnology and synthetic biology have enabled a new direction in biological engineering: synthesis of collective behaviors and spatio-temporal patterns in multi-cellular bacterial and mammalian systems. This will have a dramatic impact in such areas as amorphous computing, nano-fabrication, and, in particular, tissue engineering, where patterns can be used to differentiate stem cells into tissues and organs. While recent technologies such as tissue- and organoid on-a-chip have the potential to produce a paradigm shift in tissue engineering and drug development, the synthesis of user-specified, emergent behaviors in cell populations is a key step to unlock this potential and remains a challenging, unsolved problem. This project brings together synthetic biology and micron-scale mobile robotics to define the basis of a next-generation cyber-physical system (CPS) called biological CPS (bioCPS). Synthetic gene circuits for decision making and local communication among the cells are automatically synthesized using a Bio-Design Automation (BDA) workflow. A Robot Assistant for Communication, Sensing, and Control in Cellular Networks (RA), which is designed and built as part of this project, is used to generate desired patterns in networks of engineered cells. In RA, the engineered cells interact with a set of micro-robots that implement control, sensing, and long-range communication strategies needed to achieve the desired global behavior. The micro-robots include both living and non-living matter (engineered cells attached to inorganic substrates that can be controlled using externally applied fields). This technology is applied to test the formation of various patterns in living cells. The project has a rich education and outreach plan, which includes nationwide activities for CPS education of high-school students, lab tours and competitions for high-school and undergraduate students, workshops, seminars, and courses for graduate students, as well as specific initiatives for under-represented groups. Central to the project is the development of theory and computational tools that will significantly advance that state of the art in CPS at large. A novel, formal methods approach is proposed for synthesis of emergent, global behaviors in large collections of locally interacting agents. In particular, a new logic whose formulas can be efficiently learned from quad-tree representations of partitioned images is developed. The quantitative semantics of the logic maps the synthesis of local control and communication protocols to an optimization problem. The project contributes to the nascent area of temporal logic inference by developing a machine learning method to learn temporal logic classifiers from large amounts of data. Novel abstraction and verification techniques for stochastic dynamical systems are defined and used to verify the correctness of the gene circuits in the BDA workflow.