Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects of each paper, emphasizing the interplay of these two approaches in the field of Biological Computing. This course is appropriate for both biology and non-biology majors. Care will be taken to fill in any knowledge gaps for both scientists and engineers.

Biology is designed for multi-semester biology courses for science majors. It is grounded on an evolutionary basis and includes exciting features that highlight careers in the biological sciences and everyday applications of the concepts at hand. To meet the needs of today’s instructors and students, some content has been strategically condensed while maintaining the overall scope and coverage of traditional texts for this course. Instructors can customize the book, adapting it to the approach that works best in their classroom. Biology also includes an innovative art program that incorporates critical thinking and clicker questions to help students understand—and apply—key concepts.

By the end of this section, you will be able to:Describe how single-celled yeasts use cell signaling to communicate with one anotherRelate the role of quorum sensing to the ability of some bacteria to form biofilms

Introduction to quantitative methods and modeling techniques to address key questions in modern biology. Overview of quantitative modeling techniques in evolutionary biology, molecular biology and genetics, cell biology and developmental biology. Description of key experiments that validate models. Specific topics include: Evolutionary biology: theoretical models for evolution, evolution in test tube, evolution experiments with viruses and bacteria, complexity and evolution; Molecular biology and genetics: protein design, bioinformatics and genomics, constructing and modeling of genetic networks, control theory and genetic networks; Cell biology: forces and motion, cell motility, signal transduction pathways, chemotaxis and pheromone response; Development biology: pattern formation, self-organization, and models of Drosophila development.