This course is designed for students in graduate level degree programs with an interest in developing a strong understanding of core concepts in genetics and omics technologies. It is assumed that students either have familiarity with undergraduate level biology topics, or can quickly catch up to keep pace with the course. We will focus on genetics, genomics, and bioinformatics topics ranging from transcriptomics to transgenic organisms to epigenetics. This fast-paced course will cover both foundational principles, as well as modern applications and developments, offering hands-on active learning opportunities where possible. Students will be expected to engage deeply with the material, and will have the opportunity to develop scientific skills in critical thinking, reading, and communication, culminating in a final group presentation at the end of the semester. Upon completion of the course, students should feel empowered to enroll in any advanced genetics/genomics-based biology course at Penn.

Integrative seminar on current biological research for first-year PhD students.

This course will cover various mathematical models and tools that are used to study modern biological problems. Mathematical models may be drawn from cell biology, physiology, population genetics, or ecology. Tools in dynamical systems or stochastic processes will be introduced as necessary. No prior knowledge of biology is needed to take this course, but some familiarity with differential equations and probability will be assumed.

Intensive laboratory class where open-ended, interesting biological problems are explored using modern lab techniques. Topics may include protein structure/function studies; genetic screens, genomics and gene expression studies; proteomics and protein purification techniques; and molecular cloning and DNA manipulation. The course emphasizes developing scientific communication and independent research skills. Course topics reflect the interests of individual Biology faculty members. This course is recommended for students considering independent research.

Designed for graduate students with an interest in exploring important discoveries in the biological sciences through the lens of literature discussion. We will emphasize work that has won the Nobel Prize in Physiology or Medicine, and its relevance to modern research. It is assumed that students have familiarity with undergraduate biology topics, or can catch up, to keep pace with discussions. In addition to instructor-guided exploration of course material, students will be expected to lead journal club-style discussions on papers of their choosing. Upon successful completion of this course, students will have both a greater appreciation for major discoveries in the biological sciences, and a greater ability to discuss and apply these discoveries to their own scientific questions.

This course provides a thorough interdisciplinary overview of the modern drug discovery and development process. Building on foundational life sciences and molecular science courses, this course demonstrates how basic scientific concepts are applied to real-world challenges in drug discovery and development.
The course begins with a history of medicines in society and the evolution of the modern pharmaceutical and biotech industry. It then covers a wide range of topics, including the identification of novel therapeutic targets, the molecular design of safe and effective drugs, considerations related to the final clinical formulations, clinical trial design and execution, regulatory pathways for drug approval, and post-market safety and efficacy monitoring.
The curriculum is led by experienced researchers, biotech innovators, and professionals from both academia and the biopharmaceutical industry. It covers essential disciplines that are vital to drug discovery and development. In addition to core subjects such as physiology, cell biology, molecular biology, and biochemistry, the course also explores related fields, including organic chemistry, medicinal chemistry, pharmacology, pharmacokinetics, toxicology, materials science, and biotechnology. Students will also investigate in silico methodologies, explore applications of AI in drug discovery and development, and learn about advanced biomanufacturing processes. By the course's end, students will understand the various challenges, opportunities, and career paths available within drug discovery, the pharmaceutical sector, and the broader biotechnology industry.
Prerequisite: Undergraduate courses in biology, biochemistry, and organic chemistry.

Urban environments present unique challenges and opportunities for plant species. After a review of plant taxonomy and anatomy, this course will examine the ecological impacts of plants in urban settings. We will explore landscapes in and around Penn’s campus to understand how plant communities contribute to ecosystem services in these environments. The applied uses of plants in agriculture, medicine, bioremediation, and other aspects of community health will also be explored.

Introductory computational biology course designed for both biology students and computer science, engineering students. The course will cover fundamentals of algorithms, statistics, and mathematics as applied to biological problems. In particular, emphasis will be given to biological problem modeling and understanding the algorithms and mathematical procedures at the "pencil and paper" level. That is, practical implementation of the algorithms is not taught but principles of the algorithms are covered using small sized examples. Topics to be covered are: genome annotation and string algorithms, pattern search and statistical learning, molecular evolution and phylogenetics, functional genomics and systems level analysis.

The goal of this course is to develop a deeper understanding of techniques and concepts used in Computational Biology. Both theoretical and practical aspects of a range of methods will be covered. Theoretical aspects will include statistical analysis, modeling, and algorithm design. This course cannot provide a comprehensive survey of the field but focuses on a select core set of topics and data types. We will discuss the genome browser, alignment algorithms, classical and non-parametric statistics, pathway analysis, dimensionality reduction, GWAS, multiple testing and machine learning, with primary focus on biomedical data. UNIX, R and Python will be utilized to learn to execute big data analysis pipelines, including RNA-Seq and DNA-Seq. UNIX and R will be taught from first principles but prior experience in Python will be assumed. You will be provided with a computational (cloud based) platform on which to do all programming and assignments.
Prerequisite: Programming experience in Python required.

Introduction to basic theoretical tools to study the evolutionary and ecological dynamics of populations. Topics to be discussed include: basic population dynamics and population genetics theory, evolutionary game theory/adaptive dynamics, social evolution (kin selection/multilevel selection), life-history evolution, and stochastic models. Other topics may be added based on the specific interests of students in the class.