Course Meeting Times
Lectures: 1 session / week, 2 hours / session
The prerequisite for this course is Introduction to Biology (7.012, 7.013, or 7.014). Also helpful would be: Differential Equations (18.03), Genetics (7.03), General Biochemistry (7.05), Cell Biology (7.06), or Molecular Biology (7.28). Depending on the interests of the students, more or less of the mathematical aspects of this subject may be emphasized.
Molecular biology has been extremely successful in deciphering the details of specific cellular biochemical interactions, such as those that control inter- and intracellular signaling and gene expression. However, a full understanding of cellular function will require an understanding of how all of these interactions work together in a network to perform particular tasks. Such an understanding is the goal of the new field of systems biology.
In this seminar, we will discuss some of the main themes that have arisen in this field, including the concepts of robustness, stochastic cell-to-cell variability and the evolution of molecular interactions within complex networks. Robustness is a property of many natural biological networks whereby the behavior of the network is insensitive to variations in the numbers of inputs and the strengths of interactions. One classic example is bacterial chemotaxis, in which the bacterial food sensing mechanism is insensitive to perturbations in the levels of key proteins. This insensitivity to variations is particularly important given recent work demonstrating that gene expression has a strongly random component, leading to large variations from cell to cell even in genetically identical populations. In certain networks, this "gene expression noise" can lead to intrinsically random divergence in developmental fates.
We will also discuss networks in a more global context, considering the structure and evolutionary dynamics of networks in whole organisms.
Finally, we will study how researchers in the field of synthetic biology are using such new knowledge about biological networks to create artificial gene networks capable of performing new functions. Examples range from simple genetic switches and oscillators to the transplantation of entire networks capable of producing drugs, biofuels, and synthetic materials.
The main objectives of this course are to introduce students to the primary scientific literature and the process of finding/reading research papers and to expose students to the new field of systems biology. In the process of reading the assigned publications, you will learn how to analyze papers to extract key points and to examine scientific papers critically. Our focus will be on papers that have made significant conceptual contributions to systems biology. We will discuss experimental methodology and the principles of experimental design, control experiments and the interpretation of experimental data.
We also aim to introduce students to the theoretical aspects of biology. While some mathematical background will be helpful in this regard, it is not required. Part of our goal is to expose those with little theoretical background to some of the interesting theories that have helped to make systems biology a remarkably interdisciplinary field.
This course is pass/fail. Grading will be based on participation during weekly class discussions, and on written assignments.
|SES #||TOPICS||KEY DATES|
|1||Introduction to the class and topic|
|2||Simple synthetic networks|
|3||Noise in gene expression I|
|4||Noise in gene expression II|
|5||Noise in gene expression III|
|6||Structure of biological networks|
|7||Network evolution and adaptation||Paper 1 due|
|12||Noise in development||Paper 2 due|