Laurie J. Heyer

Evaluation of Three Automated Genome Annotations for Halorhabdus utahensis

Genome annotations are accumulating rapidly and depend heavily on automated annotation systems. Many genome centers offer annotation systems but no one has compared their output in a systematic way to determine accuracy and inherent errors. Errors in the annotations are routinely deposited in databases such as NCBI and used to validate subsequent annotation errors. We submitted the genome sequence of halophilic archaeon Halorhabdus utahensis to be analyzed by three genome annotation services. We have examined the output from each service in a variety of ways in order to compare the methodology and effectiveness of the annotations, as well as to explore the genes, pathways, and physiology of the previously unannotated genome. The annotation services differ considerably in gene calls, features, and ease of use. We had to manually identify the origin of replication and the species-specific consensus ribosome-binding site. Additionally, we conducted laboratory experiments to test H. utahensis growth and enzyme activity. Current annotation practices need to improve in order to more accurately reflect a genomes biological potential. We make specific recommendations that could improve the quality of microbial annotation projects.

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.

On representing graphs by touching cuboids

We consider contact representations of graphs where vertices are represented by cuboids, i.e. interior-disjoint axis-aligned boxes in 3D space. Edges are represented by a proper contact between the cuboids representing their endvertices. Two cuboids make a proper contact if they intersect and their intersection is a non-zero area rectangle contained in the boundary of both. We study representations where all cuboids are unit cubes, where they are cubes of different sizes, and where they are axis-aligned 3D boxes. We prove that it is NP-complete to decide whether a graph admits a proper contact representation by unit cubes. We also describe algorithms that compute proper contact representations of varying size cubes for relevant graph families. Finally, we give two new simple proofs of a theorem by Thomassen stating that all planar graphs have a proper contact representation by touching cuboids.

Implementing Recommendations for Introductory Biology by Writing a New Textbook

A new introductory biology textbook responds to national calls for reform and promotes critical learning gains in scientific, quantitative, and metacognitive ability.

Bacterial computing

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

Illuminating the Black Box of Genome Sequence Assembly: a Free Online Tool to Introduce Students to Bioinformatics

ABSTRACT Next-generation sequencing technologies have greatly reduced the cost of sequencing genomes. With the current sequencing technology, a genome is broken into fragments and sequenced, producing millions of “reads.” A computer algorithm pieces these reads together in the genome assembly process. PHAST is a set of online modules (http://gcat.davidson.edu/phast) designed to teach advanced high school and college students the genome assembly process. PHAST allows users to assemble phage genomes in real time and includes tutorials detailing the complexities of genome assembly. With PHAST, students learn concepts behind genome assembly and understand how mathematics solves biological problems such as genome assembly.

Microarray analysis of the in vivo sequence preferences of a minor groove binding drug

BackgroundMinor groove binding drugs (MGBDs) interact with DNA in a sequence-specific manner and can cause changes in gene expression at the level of transcription. They serve as valuable models for protein interactions with DNA and form an important class of antitumor, antiviral, antitrypanosomal and antibacterial drugs. There is a need to extend knowledge of the sequence requirements for MGBDs from in vitro DNA binding studies to living cells.ResultsHere we describe the use of microarray analysis to discover yeast genes that are affected by treatment with the MGBD berenil, thereby allowing the investigation of its sequence requirements for binding in vivo. A novel approach to sequence analysis allowed us to address hypotheses about genes that were directly or indirectly affected by drug binding. The results show that the sequence features of A/T richness and heteropolymeric character discovered by in vitro berenil binding studies are found upstream of genes hypothesized to be directly affected by berenil but not upstream of those hypothesized to be indirectly affected or those shown to be unaffected.ConclusionThe data support the conclusion that effects of berenil on gene expression in yeast cells can be explained by sequence patterns discovered by in vitro binding experiments. The results shed light on the sequence and structural rules by which berenil binds to DNA and affects the transcriptional regulation of genes and contribute generally to the development of MGBDs as tools for basic and applied research.

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.