MIT-OCW Health Sciences & Technology 510 Harvard Biophysics 101 For more info see: Tue & Thu 4:00-5:30pm HMS, TMEC L-007 Genomics, Computing, Economics

Genomics, Computing, & Economics Course plan Each student will participate in a class-wide project to provide decision-making tools for global/local technology development and deployment. Each will have a web page or wiki describing and updating their part of project going by the second class. Grades will be based on 25% participation(round robin), 25% personal wiki page (weekly) 25% contribution to group project/ article, 25% peer evaluations No prerequisites. It is assumed that each of you brings some expertise to be integrated with the goals and talents of other team members. Each student should make this clear at the start of the project and update it as the course proceeds.

Genomics, Computing, Economics & Society Course plan This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports. Specific (standard) skills to be developed: statistics, modeling, datamining, systems biology, technology development.

101: '99-'03 Simple to Complex '05-’09 Complex to Simple '03 5 problem sets then project '09 Project starts on day 1 '03 one 2 hr ppt lecture hr section per week '09 two 1.5 hr discussion (may include 30' ppt) '03 Project teams of 1 or two students '09 Project team of all students & TFs '03 Choice of two campuses & streaming video '09 Less choice '03 Tools: Perl & Mathematica '09 Wiki (& anything else, especially Python)

Previous class projects Kim JI,.. Wu X,.. Seo JS (2009) A highly annotated whole genome sequence of a Korean Individual. Nature 460: Drmanac R,.. Wu X,.. Reid CA (2009) Human Genome Sequencing Using Unchained Base Reads on Self-assembling DNA Nanoarrays. Science submitted. André Catic, Cal Collins, George Church, Hidde Ploegh, HL (2004) Preferred in vivo ubiquitination sites. Bioinformatics 20: Andrew Tolonen, Dinu Albeanu, Julia Corbett, Heather Handley, Charlotte Henson & Pratap Malik (2002) Optimized in situ construction of oligomers on an array surface. Nucleic Acids Research, 30: e107 Hui Ge, George Church, Marc Vidal (2001) Correlation between transcriptome and interactome data obtained from S. cerevisiae. Nature Genetics, 29: John Aach, Martha Bulyk, George Church, Jason Comander, Adnan Derti, Jay Shendure (2001) Computational comparison of two draft sequences of the human genome. Nature 409,

Potential class projects 2009 H. sapiens 2.0 Analytic: Intra-species resources: Trait-o-matic: What could we do with 100,000 full genome sequences? Inter-species resources: comparative genome & phenome data Bioweather map: Collection & use of real-time assays to track outbreaks, etc. Synthetic: Cell therapies, environmental changes, etc. Resources: Biobricks, IGEM, HSCI SynBERC: Tumor Killing BacteriaTumor Killing Bacteria

Computational Approaches What is there? Informatics What is best? Optimization How do we get there? Simulation Hypothesis/opinion: DNA computers are poor at mathematics. Electronic computers are poor at predicting phenotype from DNA.

The Maslow pyramid, 1943 Act Wisdom Knowledge Information Intelligence Memory Capacity Transcendence : need to help others find fulfillment Thirst for knowledge & aesthetical order

3 Exponential technologies (synergistic) Shendure J, Mitra R, Varma C, Church GM, 2004 Nature Reviews of Genetics. Carlson 2003 ; Kurzweil 2002; Moore 1965 urea E.coli B12 tRNA operons telegraph Computation & Communication (bits/sec) Synthesis (daltons) Analysis (bp/$) tRNA

10 (Moore’s law) 1.5x/yr for electronics vs 10x/yr for DNA Sequencing 4 logs in 4 years 2009:Lig:$5K 2005:capil:$50M 1995:gel: $3G Pol:$50K $/genome 20 years ahead of the exponential Seq bp/$

11 Moore’s law (= 1.5x/yr) vs 10x/yr for 2nd-generation Sequencing & DNA synthesis Gene synthesis is still 1 st generation 1200kb/$ 30kb/$ 2 b/$

101: '99-'03 Simple to Complex '09 Complex to Simple Common ground; de-polarization. What is life? Should we construct from scratch? Did life evolve using intelligent design? When does human life begin? Stem cells & therapeutic cloning? Can we compare Apples & oranges? Should we buy iron-lungs or polio-vaccine research? Do we invest in anti-terrorism or anti-malaria?

It seemed like a good idea at the time. Crops River life Grain trade Livestock Hygiene Fertilizer Insecticides Pets Tankers Power Plants

It seemed like a good idea at the time. Crops River life Grain trade Livestock Hygiene Fertilizer Insecticides Pets Tankers Power Plants Malaria Cholera Yersinia Flu & HIV Polio Anoxic fish Silent Spring (> malaria) Australian herbicide Mussels & sea snakes Chernobyl (> coal) Unintended consequences

Precautionary Principle If an action might cause severe or irreversible harm to the public, in the absence of a scientific consensus, the burden of proof falls on those who would advocate taking the action. Downsides: In a changing world inaction can be the radical “action” Clean Air Act : incentive to use less well studied agents Drugs & vaccines: more people can die than are saved Thresholds & selective politically-motivated application Safety-by-Design Inclusion of diverse community input (including out-of-the-box negative and safety scenarios), simulation, controlled incremental tests, extensive cost- effective monitoring

Human subject experimentation ( not a test) 7 questions. 5 seconds each 1. Write your name, , school & year. 2. Estimate 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1 3. From a group "of 70 engineers and 30 lawyers: Dick is a 30 year old man. He is married with no children. A man of high motivation, he promises to be quite successful in his field. He is well liked by his colleagues." What is the probability that Dick is an engineer? 4. Write down a string of 10 random H & T characters. 5. From 10 people, how many different committees of 2 members? and of 8 members? 6. One individual has drawn 4 red balls and 1 white. Another 12 red and 8 white. What odds should each individual give that the source is 2/3 red (rather than 2/3 white)? 7. Estimate 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8

Economics Nobel 2002 "Economics has been regarded as a non-experimental science, where researchers – as in astronomy or meteorology – have had to rely exclusively on field data,.. however, these views have undergone a transformation. Controlled laboratory experiments have emerged as a vital component.. & have shown that basic postulates in economic theory should be modified... cognitive psychologists who have studied human judgment and decision-making, and experimental economists who have tested economic models in the laboratory... Daniel Kahneman and Vernon Smith." (see also: Judgement under Uncertainty 1974 Science 185:1124)1974 Science 185:1124 Cognitive biasCognitive bias.. includes "very basic statistical and memory errors that are common to all human beings and drastically skew the reliability of anecdotal and legal evidence & significantly affect the scientific method."

Programming #!/usr/bin/env python from Bio import GenBank, Seq query = "Arabidopsis[ORGN] AND topoisomerase[TITL]" print "Query:", query # GenBank.search_for() returns a list of genbank ids in response to the query gi_list = GenBank.search_for(query) print "GenBank ids returned:", gi_list # NCBIDictionary is an interface to Genbank # If you pass it an id, it will download the raw record ncbi_dict = GenBank.NCBIDictionary('nucleotide', 'genbank') # Retrieve the first 2 results raw_records = [] for i in range(2): raw_records.append(ncbi_dict[gi_list[i]]) # Here we print the raw record from the first id returned by our query print "\nrecord 1:\n", raw_records[0] # We can also create an interface that will parse the raw record # This facilitates extracting specific information from the sequences record_parser = GenBank.FeatureParser() ncbi_dict2 = GenBank.NCBIDictionary('nucleotide', 'genbank', parser = record_parser) parsed_record = ncbi_dict2[gi_list[0]] print "\nid:", parsed_record.id print "sequence:", parsed_record.seq.tostring()

Sept 3, 2009 Assignment Read the HELP page to get acclimated to the wiki environment if you are not already familiar with it. You will be documenting the majority of your class contributions on this wiki.HELP Sign up for an OpenWetWare account HERE and add yourself to the PEOPLE page.HEREPEOPLE Write a brief description about yourself, your interests and what you hope to get out of the class in your own user page.