MIT-OCW Health Sciences & Technology 508/510 Harvard Biophysics 101 For more info see: Biophysics_101/2009 Biophysics_101/2009.

MIT-OCW Health Sciences & Technology 508/510

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 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 + 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

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

1000000

10000000

1980 1985 1990 1995 2000 2005 2010

dsDNA

Oligos

Seq 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

HygieneInsecticides

FertilizerPets

TankersPower Plants

http://www.primitivism.com/easter-island.htm

MalariaCholeraYersiniaFlu & HIVPolioSilent SpringAnoxic fishAustralian herbicideMussels & sea snakesTMI, Chernobyl

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 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, 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()

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