• Credits: 3

Topics covered include Darwinism and neo-Darwinian theory, including adaptation, natural selection, sexual selection, speciation, and techniques used to understand evolution of living and extinct organisms.

• Credits: 3
• Syllabus:

The completion of the Human Genome Project in 2003 began a revolution in the treatment of human disease. More than 10 years later, the promise of personalized genome-guided medical treatment is becoming reality. This course will explore how genomic information has enhanced our understanding of human genetic variation and disease susceptibility. Students will develop familiarity with main areas in genomic medicine through lectures from intra- and extramural experts, and they will be involved in classroom discussions.

• Credits: 3
• Syllabus:

Modern evolutionary theory offers a conceptual framework for understanding human health and disease. In this course we will examine human disease in evolutionary contexts with a focus on modern techniques and genome-scale datasets. We ask: What can evolution teach us about human populations? How can we understand disease from molecular evolutionary perspectives? What are the relative roles of negative and positive selection in disease? How do we apply evolutionary principles in diagnosing diseases and developing better treatments? Students will conduct case studies of a variety of diseases and phenotypes in a group setting.

• Credits: 3

Events such as the emergence of avian flu have increased public awareness about the need for incorporating ecology and evolution in decision-making processes that involve infectious diseases. It is evident for the public health community that molecular information, together with concepts from ecology and evolutionary biology, allows for testing of hypotheses and exploration of scenarios that otherwise could not be investigated by traditional epidemiological approaches. Understanding the ecological and evolutionary dynamics of infectious diseases requires the integration of information across organizational levels at various temporal and/or spatial scales. This requirement, together with novel molecular evolution, genomics, and mathematical modeling approaches, has positioned research on Genomics and Infectious Diseases Dynamics at the forefront of Public Health Genomics. The goal of this class is to discuss some of the biological processes leading to the emergence and re-emergence of infectious diseases stressing on evolutionary concepts within an epidemiological context. Basic concepts will be provided by the instructor as part of formal lectures. Our general objective (integrating evolutionary biology into epidemiology) will be fulfilled by discussing research articles. Such discussions will take place during the second half of the semester. "Emerging" perspectives such as One Health and Public Health Genomics will be integrated into the lectures and discussions.

• Credits: 3

Since we last shared a common ancestor with chimpanzees, over 6 million years ago, the human species experienced a series of unusual adaptations so that today humans dominate planet earth and are masters of arts and letters, science and technology. Humans are both highly intelligent and highly social, so that when we work together extraordinary and unpredictable things can happen. This course will cover the evolutionary history of humans, with an emphasis on the genetic aspects of the process.

• Credits: 3

Introduction to Bioinformatics and Computational Biology presents students without a computational background with an initial presentation of the biological questions that can be addressed computationally using mostly online tools. Beginning with an introduction to the scientific hypothesis testing and computational biology, students will subsequently be introduced to searching the scientific literature and biological datasets and databases, concepts in the organization of genes and genomes, sequence searching (BLAST), pairwise and multiple sequence alignment, phylogenetic tree reconstruction, protein structure and homology modeling, and finally modeling function in metabloic pathways. This course is designed as an applied course and as a prerequisite for more advanced conceptual and technological courses in the department.

• Credits: 3

All known multicellular organisms harbor diverse assemblages of dependent species, many of which are considered parasites or pathogens. Yet, in spite of a growing awareness of the importance of dependent species in biodiversity and medicine, many studies are limited to assessing the consequences to their hosts. The goal of this seminar is to discuss some of the biological processes leading to the diversity of dependent species and their functional/evolutionary relationships with their hosts. This general objective will be fulfilled by discussing research articles on the genomics and evolution of dependent species, many of them considered parasites or pathogens. Students are also expected to gain proficiency in writing scientific review papers.

• Credits: 3

The completion of the Human Genome Project in 2003 began a revolution in the diagnostics, treatment, and prevention of human disease. As a part of this revolution, many areas of biology has become data-driven and quantitative. Modern genomic biology, biomedicine, and evolutionary genomics, and vitally dependent on key bioinformatic tools and algorithms. This course is design to introduce students to key informatics and algorithmic concepts widely used in bioinformatics and computational biology, and to equip this with operational knowledge of the 'must-know' tools used by scientists and practitioners today. Students will complete an independent project using the tools and techniques learned in the course, integrating literature review, new analyses of published data using software tools and pipelines, data visualization and interpretation, and formal report writing. This course takes the approach of discovery-based learning. Each lecture will be structured to cover one discrete topic, with a brief background, introduction of key concepts, tutorials, and guided software exercises.

• Credits: 3

This course will cover the process of gene inheritance and descriptions of genome structure, as well as a discussion of gene content and function across lineages. Students will learn about genome-related technologies, including genome sequencing. They will also learn about how genomes vary across species, as well as the forces driving these evolutionary changes. A significant part of the course will cover genome-level data analyses, and students will complete assignments and exams to demonstrate understanding of the information present in genomes and how we know it. Note: Prior to fall 2016, the course title was "Genomics." Prior to fall 2015, the course title was "Genomics and Proteomics."

• Credits: 3

Computational Genomics covers topics in genome assembly, and related topics such as transcriptome assembly and gene expression analysis. The course also covers genomic databases and genome annotation. Students work on their own computers, using python to develop scripts, and complete major assignments each week that are representative of bioinformatics analyses that occur in industry and research centers.

• Credits: 3

High-throughput sequencing technology is now an essential tool in many domains of biology. In addition to enabling large scale population genomics (e.g. 1000 genomes project), the digital, quantitative readout of sequencing reads allows genome-wide insights into chromatin biology, gene expression, translation, and other molecular features. This course will focus on introducing students to uses of high throughput sequencing, including but not limited to population resequencing of genomic data, RNA sequencing (RNAseq), chromatin immunoprecipitation followed by sequening (ChIPseq), chromatin conformation analysis (Hi-C), and many other types of data. Students will be taught basic scripting skills in Python, as well the use of freely available software packages for data analysis.

• Credits: 3

Events such as the emergence of avian flu have increased public awareness about the need for incorporating ecology and evolution in decision-making processes that involve infectious diseases. It is evident for the public health community that molecular information, together with concepts from ecology and evolutionary biology, allows for testing of hypotheses and exploration of scenarios that otherwise could not be investigated by traditional epidemiological approaches. Understanding the ecological and evolutionary dynamics of infectious diseases requires the integration of information across organizational levels at various temporal and/or spatial scales. This requirement, together with novel molecular evolution, genomics, and mathematical modeling approaches, has positioned research on Genomics and Infectious Diseases Dynamics at the forefront of Public Health Genomics. The goal of this class is to discuss some of the biological processes leading to the emergence and re-emergence of infectious diseases stressing on evolutionary concepts within an epidemiological context. Basic concepts will be provided by the instructor as part of formal lectures. Our general objective (integrating evolutionary biology into epidemiology) will be fulfilled by discussing research articles. Such discussions will take place during the second half of the semester. "Emerging" perspectives such as One Health and Public Health Genomics will be integrated into the lectures and discussions.

• Credits: 3

The completion of the Human Genome Project in 2003 began a revolution in the diagnostics, treatment, and prevention of human disease. As a part of this revolution, many areas of biology has become data-driven and quantitative. Modern genomic biology, biomedicine, and evolutionary genomics, and vitally dependent on key bioinformatic tools and algorithms. This course is design to introduce students to key informatics and algorithmic concepts widely used in bioinformatics and computational biology, and to equip this with operational knowledge of the 'must-know' tools used by scientists and practitioners today. Students will complete an independent project using the tools and techniques learned in the course, integrating literature review, new analyses of published data using software tools and pipelines, data visualization and interpretation, and formal report writing. This course takes the approach of discovery-based learning. Each lecture will be structured to cover one discrete topic, with a brief background, introduction of key concepts, tutorials, and guided software exercises.

• Credits: 3

• Credits: 3

• Credits: 3

• Credits: 3

• Credits: 3

• Credits: 3