This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).

An overview of research topics and methods in quantitative biology through reading and discussion of primary literature. Students read two papers weekly, each showcasing how modern experimental and analytical techniques are applied to address basic questions in biology (e.g. What shape is the endoplasmic reticulum? What controls gene expression?) with a strong focus on big data. Students examine the achievements and impact of each study, present context and background, dissect experimental and analytical approaches, and highlight remaining challenges. Topics range from gene regulation and organellar dynamics to virology and cancer genomics.

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).

Close reading of published papers illustrating the principles, achievements, and difficulties that lie at the interface of theory and experiment in biology. Two important papers, read in advance by all students, will be considered each week; the emphasis will be on discussion with students as opposed to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, and maximum likelihood methods. A general tutorial on Matlab and specific tutorials for the four homework assignments will be available.

This Special Topics Quantitative and Computational Biology Course comprises five units, each presenting a different level of biological organization. Unit 1 focuses on the regulation of single genes and gene networks. Unit 2 discusses enzyme networks in metabolism and protein-protein interaction networks that control intracellular processes. Unit 3 focuses on cell-cell communication within adult and developing tissues. Unit 4 is on control systems that coordinate tissues in growing and aging organisms. Unit 5 is on networks of organisms, connecting with ideas from genetics, biochemistry, and physiology.