Genomics, Computing, Economics, and Society
Course facts
- Payment: Free, no certificate
- Credits: Non Credit
- Learning style: Self-paced, start anytime
- Resources: Lecture Notes
MIT OpenCourseWare details
- As taught in: Fall 2005
- Department: Health Sciences and Technology
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How exponential technologies affect society: personalized medicine, new energy sources and biocomplexity, through a team project and notes.
Some modern technologies display exponential growth curves, and decisions about them affect quality of life worldwide. This course studies that impact through a weekly updated class project, building practical tools for political and business decisions about bioengineering, personalized medicine, genetically modified organisms and stem cells. Multi-disciplinary teams explore the interplay of economic, ethical, ecological and biophysical modeling, with statistics, datamining and systems biology as skills.
What You Will Achieve
- Explain what personalized medicine involves and how it might be measured
- Assess a new energy source using environmental models and economic incentives
- Apply statistics and modeling to questions about technology development
- Build decision-making tools for technology deployment as part of a team
Program Overview
Projects and Exercises
Setting up the shared work and basic framing.
- A quantitative definition of life
- New energy sources and personalized medicine
- Biosphere facts
- Project literature review
Genomes and Tools
Data and computing behind the projects.
- Metabolic networks and learning Perl
- 101 uses for 1000 genomes
- ENCODE and OMIM
- Aligned genomes and statistically significant mutations
Class Project Topics
What students examined.
- Personalized medicine
- New energy sources
- Biocomplexity and randomness
Course Completion
Job Outlook
The mix of statistics, modeling, datamining, systems biology and technology development applies to work in genomics, biotechnology and technology policy.
Good for: Readers curious how scientific, economic and ethical analysis can be combined for decisions about genomics and new technology.
Keep in mind: This 2005 course was built around a shared class wiki and team project, so you read the topics and notes rather than take part. Genomics has moved on a long way since.
Bottom line: A broad-minded experiment in teaching technology decisions, now most interesting for its framing and project ideas.
Frequently Asked Questions
What did the class project produce?
A class-wide wiki of decision-making tools for global and local technology development and deployment, in the categories of Personalized Medicine, New Energy Sources, and Biocomplexity and Randomness.
What is the biocomplexity problem about?
Finding a metric for complexity and randomness that fits intuition: for example, ecosystems and stock markets should score high, while perfect crystals and white noise should score low.
Are there programming elements?
Yes, in part. Sessions include learning Perl with metabolic networks and a session on the World-Wide Web Library for Perl, alongside readings and project updates.
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