Course Meeting Times
Lectures: 1 session / week, 2 hours / session
No advanced training in mathematics or computational biology is required, though students should have a basic understanding of cell and molecular biology (7.03, 7.05, 7.06, or 7.28). Previous experience with computational methods or differential equations would be beneficial but is not required.
Recommended prerequisites are:
7.05 General Biochemistry
7.06 Cell Biology
7.28 Molecular Biology
20.390J Foundations of Computational and Systems Biology
18.03 Differential Equations
Complex human diseases—like cancer, diabetes, or autism—involve dysregulation of numerous genes, proteins, and cellular behaviors. Creating effective therapies for these diseases will require a comprehensive understanding of how cells integrate enormous amounts of genomic, proteomic, and environmental information to produce specific cellular functions, and furthermore, how such functions have been perturbed in the disease state. To meet this challenge, many have turned to systems biology, an interdisciplinary field that integrates biology with principles borrowed from mathematics, physics, and engineering to gain a comprehensive understanding of biological complexity. In this course, we will survey the primary systems biology literature, particularly as it pertains to understanding and treating various forms of cancer. We will consider various computational and experimental techniques being used in the field of systems biology, focusing on how systems principles have helped advance biological understanding. Topics will include: various methods of quantitative high-throughput data acquisition, genomic analysis, signaling network biology, and statistical/computational modeling. We will also discuss the application of the principles of systems biology and network biology to drug development, an emerging discipline called "network medicine." We will take a field trip to Merrimack Pharmaceuticals, a Cambridge-area pharmaceutical company that is using systems biology to create new anti-cancer agents.
The main goal of this course is to introduce students to the fields of systems biology and network medicine, and to familiarize students with the process of reading, analyzing, and critically evaluating the primary scientific literature.
The course will meet weekly to discuss two original research papers focusing on a broad range of topics in the emerging field of network medicine. Each week, students should thoroughly read the assigned papers and be prepared to discuss them in detail. At the end of each session, the instructor will briefly introduce the papers for the next week.
This course is graded pass/fail, and grading will depend on student attendance, participation in discussions, and completion of short weekly assignments, a midterm writing assignment, and an oral presentation.
|WEEK # ||TOPICS ||KEY DATES |
|1 ||Introduction || |
|2 ||Targeted Therapy in the Pre-Systems Biology Era || |
|3 ||High-Throughput Data Acquisition I: Gene Sequencing and Functional Genomics || |
|4 ||High-Throughput Data Acquisition II: Proteomics || |
|5 ||Gene Expression Analysis || |
|6 ||Network Biology || |
|7 ||Clustering || |
|8 ||Regression Modeling || |
|9 ||Logic Modeling ||Midterm writing assignment due |
|10 ||Kinetic Modeling || |
|11 ||Structural Biology || |
|12 ||Synthetic Biology ||Finalize choice of paper for oral presentation |
|13 ||Field Trip: Visit to Merrimack Pharmaceuticals || |
|14 ||Systems Pharmacology and Network Medicine || |
|15 ||Final Class ||Oral Presentations |