1、,MIT-OCW Health Sciences & Technology 508/510 Harvard Biophysics 101 Economics, Public Policy, Business, Health PolicyFor more info see: http:/karma.med.harvard.edu/wiki/Biophysics_101,10 AM Thu 1-Feb 2007 Barker Center, Room 316 12 Quincy St.,Genomics, Computing, Economics,Genomics, Computing, & Ec
2、onomics 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% par
3、ticipation(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 t
4、he 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
5、 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 thro
6、ugh 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 05-07 Complex to Simple,03 5 problem sets then project 07 Project starts on day 1
7、03 one 2 hr ppt lecture + 1.5 hr section per week 07 two 1.5 hr discussion (may include 30 ppt)03 Project teams of 1 or two students 07 Project team of all students & TFs03 Choice of two campuses & streaming video 07 Less choice03 Tools: Perl & Mathematica 07 Wiki (& anything else, especially Python
8、),Previous class projects,Andr Catic, Cal Collins, George Church, Hidde Ploegh, HL (2004) Preferred in vivo ubiquitination sites. Bioinformatics 20: 3302-7. Andrew Tolonen, Dinu Albeanu, Julia Corbett, Heather Handley, Charlotte Henson & Pratap Malik (2002) Optimized in situ construction of oligomer
9、s 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, 29:482-6.,John Aach, Martha Bulyk, George Church, Jason Comander, Adnan Derti, Jay Shendure (2001) Com
10、putational comparison of two draft sequences of the human genome. Nature 409, 856-859.,Potential class projects 2007,Earth/Life 2.0 Analytic: Bioweather map. Collection and use of real-time assays to track virus outbreaks, etc. Synthetic: Bioenergy. Integrated metabolic, genetic, economic, process,
11、environmental modeling. H. sapiens 2.0 Analytic: Personal Genome Project. What could we do with a ten (or a million) full genome (or exome) sequences? Synthetic: Cell therapies for more info see http:/www.synberc.org/testbeds.html#cancerkiller,Computational Approaches,What is there? InformaticsWhat
12、is best? OptimizationHow 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 fin
13、d 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 (
14、daltons),Analysis (bp/$),tRNA,101: 99-03 Simple to Complex 05 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 w
15、e buy iron-lungs or polio-vaccine research?Do we invest in anti-terrorism or anti-malaria?,Hot buttons,Evolution Stem Cells Life extension Eugenics, race Privacy Security, Terrorism Genetically modified organisms - GMOs Intelligent machines Replicating machines Withholding experimental drugs (e.g. H
16、IV & Cancer) Gender Do no harm, abortion Substance abuse Vioxx,General Reading,http:/www.edge.org/ Limits to Growth, The 30-Year Update by Donella H. Meadows, Jorgen Randers & Dennis L. Meadows 2004Freakonomics by Levitt & Dubner 2005As the Future Catches You by Juan Enriquez 2000Genetic Programming
17、 IV: Routine Human-Competitive MachineIntelligence by John R. Koza et al. 2003Consilience by E.O. Wilson 1998Redesigning Humans by Gregory Stock 2002Scientific Conquest of Death 2004 Collapse by Jared Diamond 2005 The Tipping Point by Malcolm Gladwell 2000 The Wisdom of Crowds by James Surowiecki 20
18、04 The Climate Of Man by Elizabeth Kolbert 2005Widescale Biodiesel Production from Algae by Michael Briggs,Synthetic Life by WW Gibbs 2004Personalized medicine by Francis S. Collins 2005,It seemed like a good idea at the time.,Crops River life Grain trade Livestock Hygiene Insecticides Fertilizer Pe
19、ts Tankers Power Plants,http:/ Cholera Yersinia Flu & HIV Polio Silent Spring Anoxic fish Australian herbicide Mussels & sea snakes TMI, Chernobyl,Unintended consequences,Human subject experimentation (not a test) 7 questions. 5 seconds each,1. Write your name, email, school & year.,2. Estimate 8 x
20、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. W
21、rite 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)?
22、,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 experim
23、ents 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 Smit
24、h.“ (see also: Judgement under Uncertainty 1974 Science 185:1124)Cognitive 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.“,High-level decisi
25、ons,Leon Kass, chairs the US Presidents Council on Bioethics, opposed IVF 78 & stem cell research 05 via the wisdom of repugnance http:/ http:/ http:/www.seti.org/site/apps/nl/content2.asp?c=ktJ2J9MMIsE&b=194993&ct=1324903The Wisdom of Crowds by James Surowiecki 2004Interracial-marriage: Perez v. Sh
26、arp, CA 1948, Loving v. Virginia, U.S. Supreme Court 1967,Economics & Global Ecology,“much of the current enthusiasm for market based regulation stems from the results of a pilot program launched in th US in 1990. Congress amended the clean air act that year to set up a market for the permits that p
27、ower plants must have to release SO2 Supply & demand Market overseers need the power, analogous to the Federal Reserves“ -W Gibbs, Sci Am 2005,Quantifying the “unquantitifiable“ Interconversions in modeling,Biofuels,“Wrong assumptions and poor comparisons abound. ethanol subsidies are at $0.51 per g
28、allon the total dollar amount is probably negative. The decrease in corn subsidies is far more than total ethanol subsidies in 2006. While total ethanol subsidies were about $2.5b in 2006, farm subsidies declined by an estimated $6b because of higher biofuels use - a net negative subsidy for ethanol
29、! (-$0.71/gal?)oil subsidies? The absolutely lowest estimate I have seen is about $0.25 per gallon subsidy for gasoline using only the direct subsidies to oil producers. The conservative estimate of subsidy per gallon of gasoline including indirect costs is a few dollars per gallon ($4.00 per gallon
30、 was been mentioned) while aggressive estimates are more than twice that. How is a new fuel to compete?” ($8/gal?)http:/ Vinod Khosla: President Bush, Please Declare a War on Oil!,Vaccines for the 21st Century,http:/books.nap.edu/html/vacc21/ Level I Most favorable: saves money & Quality-Adjusted Li
31、fe Years(QALY)Level II $10,000 Level III $100K per QALY saved Level IV Level I candidate vaccines: Viral: CMV vaccine for 12 year olds, Flu vaccine for 20% of the US per year.Therapeutic vaccines: IDDM diabetes, MS, Rheumatoid arthritis Bacterial: Streptococcus B & pneumoniae vaccine for infants & 6
32、5 year olds.HIV vaccines prominent already within NIH.,“A quantitative model that could be used by decision makers to prioritize the development of vaccines against a number of disparate diseases”,Biomedical Priorities,“The complete conquest of cancer or heart disease would each lead to an increase
33、of a mere 3% in mean life span in humans, i.e. about a tenth of what can be accomplished, today, in laboratory animals of delayed aging. “ - Miller, R.A. (2005) J.Am. Geriatrics Soc. 53:S284.,Leading Causes of Death in 2001,Programming,#!/usr/bin/env python from Bio import GenBank, Seq query = “Arab
34、idopsisORGN AND topoisomeraseTITL“ 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 r
35、aw record ncbi_dict = GenBank.NCBIDictionary(nucleotide, genbank) # Retrieve the first 2 results raw_records = for i in range(2): raw_records.append(ncbi_dictgi_listi) # Here we print the raw record from the first id returned by our query print “nrecord 1:n“, raw_records0 # We can also create an int
36、erface 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_dict2gi_list0 print “nid:“, parsed_record.id print “sequence:“, parsed_record.seq.tostring(),.,
