Bacteria can be modified, through synthetic biology approaches, to function as biological computers. They can be programmed with DNA sequences, interact naturally with other cells and the environment, power themselves from food sources, and respond to directed evolution. Biological computing is not yet widespread, and this project will develop second-generation bacterial computers capable of addressing mathematical problems in graph theory, probability, theory of computation, and artificial intelligence. The project will identify problems that are scalable and generalizable, develop a system of classifying the bacterial computational complexity of mathematical problems, and construct mathematical models and computer simulations to assess the design feasibility of new bacterial computer programming languages. Proven and novel bacterial computing mechanisms will be used to program bacterial computers to solve the selected mathematical problems.
Broader Impacts. This project will provide multidisciplinary education and research training for undergraduate students (including members of groups under-represented in science) at the interface of mathematics, synthetic biology and computer science. Students will pose research questions, develop testable hypotheses, collect and analyze data, and communicate results through publications and at scientific meetings.
This project is supported jointly by the Networks and Regulation and Mathematical Biology Programs.
Intellectual Merit This project led to the development of a new approach for optimizing production of useful compounds by genetically engineered bacteria. The approach is called Programmed Evolution in order to capture two important concepts. First, the researchers program a population of cells with DNA code to carry out computation of solutions to a chosen optimization problem. The bacteria gather inputs unknown to humans for cellular calculations, including the genetic composition of the cells, the molecular interaction of parts and devices, the energy requirements for metabolism, the effects of metabolic flux, and the changing genomic context of evolving cells. The bacteria process all of this information as living analog computers, using the results to direct the operation of their biochemical hardware. The bacteria are better informed and more capable of making these calculations than people with incomplete information and models who program silicon computers. The second important concept employed is the evolution of a bacterial population toward solutions to the problem of optimizing a metabolic pathway. The researchers engineer genetic variation into the bacterial population and impose selection on it. Fitness is the ability of bacteria to produce the most of a desired product such as a medication. After one generation, the genetic makeup of the population will have changed, or evolved. In successive generations, engineered genetic variation is subjected to further selection and the population continues to evolve. Repeated generations of evolution are expected to lead to increased optimization of the metabolic pathway. The researchers also developed mathematical models and software tools in support of their metabolic optimization experiments. Broader impacts The project increased diversity for STEM education. The researchers continued their successful model of recruiting underrepresented groups. The project contributed to a trained STEM workforce because the students involved are highly valued as a result of their authentic research experiences. Programmed Evolution technology could reduce costs for industrial-scale commercial chemical production and pharmaceuticals, serving to improve American economic competitiveness. In addition to improving the infrastructure for research and education on the two home campuses, the project enabled the researchers to continue to serve as national leaders in undergraduate synthetic biology education and research through the Genome Consortium for Active Teaching (GCAT). The project also provided opportunities for undergraduate research training. Students learned how to pose research questions, develop testable hypotheses, collect and analyze data, and communicate results. These experiences enhanced the undergraduate education of the students, contribute to their development as scientifically literate citizens, and provide them with the background needed to pursue research careers. As the undergraduate students learned how to program the evolution of bacteria, they also learn how to program the course of their own futures as professionals, educators, and research scientists.