Course descriptions are below. Click here to view the latest schedule of Systems Biology courses at Harvard.
SysBio200: Dynamic and Stochastic Processes in Cells (typically offered in Fall semester)
Instructors: Jeremy Gunawardena and Johan Paulsson
Rigorous introduction to (i) dynamical systems theory as a tool to understand molecular and cellular biology (ii) stochastic processes in single cells, using tools from statistical physics and information theory.
SysBio201: Principles of Animal Development (typically offered in Spring semester)
Instructors: Angela DePace, Marc Kirschner and Sean Megason
Intensive and critical analysis of systems approaches to circuits and principles controlling pattern formation and morphogenesis in animals. Students develop their own ideas and present them through mentored "chalk talks" and other interactive activities.
SysBio212: Communication of Science (typically offered in Fall semester)
Instructors: Angela DePace, Allon Klein, and Galit Lahav
Communicating effectively is an essential scientific skill but rarely explicitly taught. Scientists must tell people about their work—their colleagues, the broader scientific community, students and the general public. All of these audiences have different levels of expertise and different goals for learning about science. Therefore each audience needs a specific message tailored to them. Not only must scientists tailor their message, they must also deliver it in a variety of different formats—in graphics, in writing, and in talks. Scientists with strong communication skills are better teachers, better colleagues, and more persuasive advocates for science. And yet we do not typically teach scientific communication directly.
To address this gap, we designed a class where students learn scientific communication in the context of problems relevant to their own research. We address three modes of scientific communication: graphics, writing and presentations. Across all of these sections, we emphasize three core principles: teaching a process, finding the essential story and getting critical feedback. Each section consists of hands-on exercises in small peer groups. We explicitly teach students how to lead these groups and how to constructively critique one another.
SysBio204: Synthetic Biology: From Ideation to Commercialization (typically offered in Fall semester)
Instructors: Jeffrey Way, Dubreuil, Catherine
This course provides an introduction to synthetic biology, with an emphasis on medical applications. Topics will include (1) design principles of cells, organisms, and complex proteins, industry case studies, and analysis of the synthetic-biological literature; and (2) commercialization of biotechnology and synthetic biology, including conceptualization of commercializable research, financing mechanisms, intellectual property strategies, licensing, publicity, virtual companies, and the progression through pre-clinical and clinical research and development. Specific topics include design of bacterial and mammalian genetic circuits, CAR-T cells, whole genome recoding, artificial protein design, and gene therapy.
- SB220qc: Analysis foundations for quantitative biologists (part 1)
The bedrock foundation of quantitative biology is controlling assumptions and errors in empirical measurement. This course focuses on developing "street-fighting” capabilities in quantitative analysis: statistical concepts that every biologist needs to ensure that they can interpret their data correctly. The course introduces estimators, the origin and consequences of key distributions in biology, error propagation, hypothesis testing, multiple hypothesis correction and the perils of p-hacking. Concepts are reinforced through problem sets, and team-based analysis of new experimental methods. This course can be taken alone, or as the start of SB221 that extends to cover: high-dimensional data analysis; statistical inference; and how molecular biological systems measure their surroundings in the face of thermal noise and limited energy resources.
- SB221: Analysis foundations for quantitative biologists (parts 1 and 2)
New experimental techniques are changing the nature of data sets in biology. For example, high throughput methods routinely measure expression levels of thousands of genes in individual cells across tens of thousands of cells. Imaging methods record 3-dimensional movies of developmental processes, generating terabytes of data in a single run. How do we make sense of these data sets? This course will begin with "street-fighting" statistics: tools that every biologist needs to ensure that they can interpret their data correctly. We will then study the fascinating world of high-dimensional spaces and build the intuition required for interpreting data that live in these spaces. We will cover linear and non-linear dimensionality reduction, statistical learning and inference in high-dimensional spaces, and relevant machine learning tools such as autoencoders. Finally, we ask how biological systems themselves solve high-dimensional inference problems, subject to severe measurement constraints in the form of thermal noise and limited energy resources. We will cover relevant ideas from statistical physics such as kinetic proof reading. The first five weeks of this course (on "street-fighting” statistics) overlap with SB220.