The class will be organized around very recent topics in molecular systems biology. We will learn about the biology and the experimental data collection in detail, and then will discuss the development and application of machine learning methods to solve the various modeling and inference problems that arise. Students will spend the semester working individually on a research project with a topic of very recent interest, so it is expected that students who work hard on their project will be able to publish a result after the end of the course. The project will require a write-up to be submitted at the end of the semester, but I will expect each student to maintain a "running" write-up throughout the semester that can be accessed by everyone in the class. The running write-up is a place to do brainstorming, organizing of ideas, listing of resources, ongoing description of the goals and progress to date. The idea is to conceptualize your writing less like a paper that is written the night before it's due, and more like a running experimental notebook. Students will be required to read all assigned papers before the class during which they are discussed, participate actively in the discussions that arise, and "lead off" discussion during two class sessions during the semester, once on a Tuesday and once on a Thursday (typically, we will read two papers before a Tuesday class session and one paper before a Thursday class session). After today, the next 22 class sessions will be devoted to discussion of readings, with the last such on Nov 15; the final three class sessions (Nov 20, 27, and 29) will be devoted to student project presentations. There will be no class sessions in December and no examinations. The current course web location is: http://www.cs.duke.edu/courses/fall07/cps296.3/; if you don't recall the URL, you can find it by browsing http://www.cs.duke.edu/. The course web location may change in the future, but if it does, instructions will be placed at this URL. Outline of Topics to be Covered (sketch): Transcription: DNA -> mRNA gene structure, TSS core promoter specific TFs TF specificities and binding sites models of specificity experimental assays computational prediction (motif discovery) traditional formulations recent developments nucleosome positioning data from DIP-chip and PBM regulation of transcript production descriptive models computational models role of epigenetics nucleosome positioning histone modifications DNA modifications regulation of transcript destruction NMD and other degradation processes high-throughput assays net transcript level measurement recent array technologies recent sequencing technologies Translation: mRNA -> Protein mRNA processing capping, polyadenylation splicing of introns (even in yeast) localization translation codon usage and tRNAs Replication: DNA -> DNA cell cycle origins of replication, ORC models of S phase progression advanced topics multinucleated cells embryonic cells germ cells and meiosis cancer cells terminally differentiated cells telomeres and apoptosis mitochondrial DNA Proteins localization domains and motifs protein-protein interactions experimental assays protein complexes transcription factor complexes Non-coding RNAs silencing microRNAs (not in yeast) anti-sense transcripts (even in yeast)