02-450/02-750 - Automation of Biological Research: Robotics and Machine Learning
Robert F. Murphy, Jaime Carbonell, Jeff Schneider
Fall 2011
02-450: 9 units
02-750: 12 units
MW 1:30 pm to 2:50 pm, 1217 Doherty Hall
Biology has been revolutionized by automated methods for generating large amounts of data on diverse biological processes. This, in addition to the finding that many more components are involved in each process than had earlier been thought, has led to a transition from a “reductionist” paradigm of biological research involving detailed study of single molecules or events to a “systems biology” paradigm involving comprehensive, systematic studies combined with computational data analysis. Integration of data from many types of experiments will be required to construct detailed, predictive models of cell, tissue or organism behaviors, and the complexity of the systems suggests that need for these models to be constructed automatically. This will require iterative cycles of acquisition, analysis, modeling, and experimental design, since it is not feasible to do all possible biological experiments. This course will cover a range of automated biological research methods (especially high-throughput, robotic methods for protein structure determination, gene sequencing, cell-based drug screening, and nanoassays), and a range of relevant computational methods (especially active learning, proactive learning, compressed sensing and model structure learning). It assumes a basic knowledge of machine learning. Class sessions will consist of a combination of lectures and discussions of important research papers. Grading will be based on class participation, homeworks, and a final project.
Prerequisites:
02-450: 15-122 or instructor permission.
02-750: 10-601 or 10-701 or instructor permission.