Jeffrey L. Poet

Genomics Education Partnership

The Genomics Education Partnership offers an inclusive model for undergraduate research experiences, with students pooling their work to contribute to international databases.

Solving a Hamiltonian Path Problem with a bacterial computer

BackgroundThe Hamiltonian Path Problem asks whether there is a route in a directed graph from a beginning node to an ending node, visiting each node exactly once. The Hamiltonian Path Problem is NP complete, achieving surprising computational complexity with modest increases in size. This challenge has inspired researchers to broaden the definition of a computer. DNA computers have been developed that solve NP complete problems. Bacterial computers can be programmed by constructing genetic circuits to execute an algorithm that is responsive to the environment and whose result can be observed. Each bacterium can examine a solution to a mathematical problem and billions of them can explore billions of possible solutions. Bacterial computers can be automated, made responsive to selection, and reproduce themselves so that more processing capacity is applied to problems over time.ResultsWe programmed bacteria with a genetic circuit that enables them to evaluate all possible paths in a directed graph in order to find a Hamiltonian path. We encoded a three node directed graph as DNA segments that were autonomously shuffled randomly inside bacteria by a Hin/hixC recombination system we previously adapted from Salmonella typhimurium for use in Escherichia coli. We represented nodes in the graph as linked halves of two different genes encoding red or green fluorescent proteins. Bacterial populations displayed phenotypes that reflected random ordering of edges in the graph. Individual bacterial clones that found a Hamiltonian path reported their success by fluorescing both red and green, resulting in yellow colonies. We used DNA sequencing to verify that the yellow phenotype resulted from genotypes that represented Hamiltonian path solutions, demonstrating that our bacterial computer functioned as expected.ConclusionWe successfully designed, constructed, and tested a bacterial computer capable of finding a Hamiltonian path in a three node directed graph. This proof-of-concept experiment demonstrates that bacterial computing is a new way to address NP-complete problems using the inherent advantages of genetic systems. The results of our experiments also validate synthetic biology as a valuable approach to biological engineering. We designed and constructed basic parts, devices, and systems using synthetic biology principles of standardization and abstraction.

Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria

Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields – evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy, pharmaceuticals, chemical commodities, biomining, and bioremediation.

Bacterial computing

Undergraduate students find that a genetically engineered machine can solve Hamiltonian Path Problems.

Synthetic Biology: A New Frontier

Abstract Synthetic biology is a new field that combines engineering principles, mathematical modeling, and molecular biology techniques to design and construct novel biological parts, devices, and systems with applications in energy, medicine, environmental science, and technology. We discuss examples of mathematical models aiding in biological investigations, biology aiding in mathematical investigations, and the two fields working together in synthetic biology to attack some of the worlds biggest problems. Opportunities abound for mathematicians to contribute to this budding field.

Synthetic Biology and the International Genetically Engineered Machines Competition

? JM T^e are excited to share an exciting new m/m/ area ?f biological research that will be V V of great interest to many TriBeta stu? dents and faculty mentors. As biology and math? ematics faculty mentors ourselves with a long? standing commitment to undergraduate research, we have advised students working on a variety of research topics, including cell biology, mo? lecular phylogenetics, cancer biology, microarray analysis, graph theory, and computer program? ming. Each of these areas provided interesting projects for our students, enabling them to learn how to conduct research and disseminate their results through presentations and publications. However, about four years ago, our active un? dergraduate research programs transitioned to a new research field called synthetic biology. Syn? thetic biology is exciting, interdisciplinary, rela? tively inexpensive and appropriate for under? graduate research. Synthetic biology has also enabled us to establish multidisciplinary research groups composed of biology and mathematics professors and students and to work together as a collaborative team from our two institutions, Missouri Western State University and Davidson College. Our students have used their research experience to get jobs and enter graduate school at a time when global interest in synthetic biol? ogy is growing rapidly. In this article, we de? scribe the emerging field of synthetic biology, provide examples of the impact it is having on the understanding of biology and the ability to engineer biological systems, and explain how undergraduates are making important contribu? tions to its development. We will also describe the international Genetically Engineered Ma? chines (iGEM) competition as an entry point for undergraduate students to begin synthetic biology research.