MIT-OCW Health Sciences & Technology 510 Harvard Biophysics 101 For more info see: http://openwetware.org/wiki/Harv ard:Biophysics_101/2009 Tue & Thu 4:00-5:30pm HMS, TMEC L-007 Genomics, Computing, Economics
Mar 21, 2016
MIT-OCW Health Sciences & Technology 510 Harvard Biophysics 101
For more info see: http://openwetware.org/wiki/Harvard:Biophysics_101/2009
Tue & Thu 4:00-5:30pmHMS, TMEC L-007
Genomics, Computing, Economics
Genomics, Computing, & Economics Course planEach 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 planThis 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 + 1.5 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 460:1011-5.
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: 3302-7.
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, 29:482-6.
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, 856-859.
Potential class projects 2009H. 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 dataBioweather map: Collection & use of real-time assays to track outbreaks, etc.
Synthetic: Cell therapies, environmental changes, etc.Resources: Biobricks, IGEM, HSCISynBERC: Tumor Killing Bacteria
Computational ApproachesWhat 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
ActWisdom
KnowledgeInformationIntelligence
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
1E-3
1E-1
1E+1
1E+3
1E+5
1E+7
1E+9
1E+11
1E+13
1830 1850 1870 1890 1910 1930 1950 1970 1990 2010
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
$/genome
20 years ahead of the 1970-2004 exponential
Seq bp/$
11
Moore’s law (= 1.5x/yr)
vs 10x/yr
for2nd-generation
Sequencing & DNA synthesis
0.0000010.00001
0.00010.001
0.010.1
110
1001000
10000100000
100000010000000
1980 1985 1990 1995 2000 2005 2010
dsDNAOligosSeq bp/$
Gene synthesis is still 1st
generation1200kb/$ 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.
CropsRiver life
Grain tradeLivestock
HygieneFertilizer
InsecticidesPets
TankersPower Plants
http://www.primitivism.com/easter-island.htm
It seemed like a good idea at the time.
CropsRiver life
Grain tradeLivestock
HygieneFertilizer
InsecticidesPets
TankersPower Plants
http://www.primitivism.com/easter-island.htm
MalariaCholeraYersiniaFlu & HIVPolioAnoxic fishSilent Spring (> malaria)Australian herbicideMussels & sea snakesChernobyl (> coal)
Unintended consequences
Precautionary PrincipleIf 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 applicationSafety-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, email, 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)
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."
Programming#!/usr/bin/env pythonfrom Bio import GenBank, Seqquery = "Arabidopsis[ORGN] AND topoisomerase[TITL]"print "Query:", query# GenBank.search_for() returns a list of genbank ids in response to the querygi_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 recordncbi_dict = GenBank.NCBIDictionary('nucleotide', 'genbank')# Retrieve the first 2 resultsraw_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 queryprint "\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 sequencesrecord_parser = GenBank.FeatureParser()ncbi_dict2 = GenBank.NCBIDictionary('nucleotide', 'genbank', parser = record_parser)parsed_record = ncbi_dict2[gi_list[0]]print "\nid:", parsed_record.idprint "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.Sign up for an OpenWetWare account HERE and add yourself to the PEOPLE page.Write a brief description about yourself, your interests and what you hope to get out of the class in your own user page.