ecologies, economies, & exponentials in modeling technological goals & human impact

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The Future of Human Nature: Promises and Challenges of Revolutions in Genomics and Computer Science Boston University 12-Apr- 2003 Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact George Church

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Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact. George Church. The Future of Human Nature : Promises and Challenges of Revolutions in Genomics and Computer Science Boston University 12-Apr-2003. In possibly less than 250 years ?. - PowerPoint PPT Presentation

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Page 1: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

The Future of Human Nature:Promises and Challenges of Revolutions in Genomics and Computer Science

Boston University 12-Apr-2003

Ecologies, Economies, & Exponentials in Modeling Technological Goals &

Human Impact

George Church

Page 2: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

In possibly less than 250 years ?

(1) Our doctor’s actions depend on knowing whether we & our viruses/bacteria are drug sensisitive or resistant.

(2) Cars, bikes & pedestrians have their own spaces.

(3) Hackers have less access to our medical records & doctors more access.

Page 3: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Future of Human Nature

Systems Biology & ModelingSpeculative SpecificationsPotential timelinesLimits to MassLimits to Cost (energy & environment)How? Potential pathways to nanotechnology Human biology keeping up: stem cells

Page 4: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

gggatttagctcagttgggagagcgccagactgaa gatttg gaggtcctgtgttcgatccacagaattcgcacca

Models: Share, Search, Merge, Check,

Design

Page 5: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

DNA RNA Proteins

Metabolites

Replication rate

Environment

Systems Biology Models

Ecosystems Cancer & stem cells Darwinian optimaMolecular machines

RNAiInsertionsSNPs

interactions

Page 6: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Link et al. 1997 Electrophoresis 18:1259-313 (Pub)

Compare predicted with

observed protein properties

(abundance, localization, postsynthetic modifications)

E.coli

Page 7: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

0 50 100 150 2000

20

40

60

80

100

120

140

160

180

200

1

2

3

456

78

9

10

11121314

15

16

17 18

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3456

78

910

11121314

1516

17

18

Experimental Fluxes

Pre

dic

ted

Flu

xes

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3

456

78

910

111213

14

15

16

1718

pyk (LP)

WT (LP)

Experimental Fluxes

Pre

dic

ted

Flu

xes

Experimental Fluxes

Pre

dic

ted

Flu

xes

pyk (QP)

=0.91p=8e-8

=-0.06p=6e-1

=0.56p=7e-3

Compare Flux data with predicted optimaSegre et al

PNAS (2002) 99:15112-7

Page 8: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Genetically modified organisms

Page 9: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Limits to diversity?

Page 10: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Humans past & future?

Past FutureLocomotion 50 26720 km/hOcean depth 75m 4500mVisible .4-.7 pm-MmCold 0oC 3oKMemory 20 yr 2000 yr

Page 11: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Human Nature?

7 deadly sins: Pride, Envy, Greed, Lust, Gluttony, Sloth, Anger

+ 7 heavenly virtues: Faith, Hope, Charity, Fortitude, Justice, Temperance, Prudence

+1 Darwinian breakthrough: Hyperadaptability via anticipatory modeling & experimentation

Page 12: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Inheritance is not just DNA

Page 13: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Is germline engineering the fastest threat/promise?

Watson-Crick base pair (Nature April 25, 1953)

Page 14: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Somatic vs germline engineering

• Days from design to phenotype vs 20 years

• Ethics of adults choosing for children

• Phenotype is more “predictable” from adult than embryo

• Histocompatible adult stem cells may be more accessible

• Interfaces with inorganic engineering

Page 15: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Steeper than exponential growth

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

-4000 -3500 -3000 -2500 -2000 -1500 -1000 -500 0 500 1000 1500 2000

log(#people)

log(#computers)

If population growth decreases with increasing GDP,then #CPUs could overtake #people.

Page 16: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Steeper than exponential growth

http://www.faughnan.com/poverty.html

GDP$/person (W. Europe)

2

2.5

3

3.5

4

4.5

5

5.5

6

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200

trend

log($GDP/person)

Page 17: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Vertebratebrain size evolution

Harry J. Jerison, Paleoneurology & the Evolution of Mind, Scientific American, Jan 1976 http://serendip.brynmawr.edu/bb/brainevolution/brainevol3.html

Page 18: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

The human neural net

“The retina's 10 million detections per second [.02 g] ... extrapolation ... 1014 instructions per second to emulate the 1,500 gram human brain. ... thirty more years at the present pace would close the millionfold gap.” (Morovec99)

fig

Edge & motion detection (examples)

Page 19: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Steeper than exponential growth

http://www.kurzweilai.net/meme/frame.html?main=/articles/art0184.html

log(IPS/$K)

-100

102030405060708090

100110120130

1900 1950 2000 2050 2100 2150 2200 2250

Kurzweil/Moore's law (Instructions per second/ $K)

Page 20: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Computing limits

Current: 1010 IPS/kg on 1010 bits

Limit: 1051 IPS/kg on 1031 bits

Lloyd Nat 406:1047

Page 21: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Computing costs

• High parallelism & density:

DNA: 1 nm3 /bit ; CD: 1013 nm3 /bit

• DNA copying near thermodynamic limit– 2 x1019 op/J for DNA copying– 34 x1019 op/J thermodynamic limit– 109 op/J for conventional computers– 1011 op/J for human brain @ 1kW

Page 22: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Global Economies

Launching a satellite: $0.3 billionhttp://electronics.howstuffworks.com/satellite8.htm

Eradication of polio by 2006 by vaccine $0.3 billionhttp://www.abpi.org.uk/publications/publication_details/prevention/section9.asp

Sequencing a human genome: $0.3 billionhttp://www.nih.gov/news/pr/mar2003/nhgri-04.htm

Costs of NOT eradicating polio : $3 billionBloom: www.ellison-med-fn.org/files/WoodsHole.ppt

Government change for 22M people: $80 billionwww.cnn.com

Hackers & e-viruses $1.6 trillion (5% global GDP)http://www.vnunet.com/News/1106282

Page 23: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

CO2 100 ppmv increase

http://jan.ucc.nau.edu/~doetqp-p/courses/env470/Lectures/lec41/Lec41.htm

Page 24: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Global Energy & CO2 Fluxes40-170 x1015 W of sunlight hits earth.We consume 1-2 x 1013 W per 6x109 people. (2kW each).

CO2 >370 ppm = 730 x1015 g globally, increase ~3 x1015 /yr.Ocean productivity = ~100 x1015 g/yr.

Autotrophs: 1025 Prochlorococcus cells globally (108 per liter)

Undone by Cyanophages & Heterotrophs: 2x1028 SAR11 cells in the oceans

http://www.gsfc.nasa.gov/gsfc/service/gallery/fact_sheets/earthsci/terra/earths_energy_balance.htmhttp://www.poemsinc.org/factsenergy.htmlMorris et al. Nature 2002 Dec 19-26;420(6917):806-10. http://hosting.uaa.alaska.edu/mhines/biol468/pages/carbon.html

Page 25: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Limits to mass

Lithosphere (0.2% C, 75% SiO2) 110 C at 4 km Diameter = 1.3e6 m = 5e22 g (5000 species / g soil)Microbial hydrosphere 1.4e21g = 1e27 cells = 4e33 bp Biosphere 3e15 g (dry wt. marine); 2e18 g (land)Anthrosphere (23% C) 6e14g = 6e23 cells = 4e32 bp.

fig

Page 26: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Potential economically viable and humane pathways

Avoiding “Colossus” and “grey goo”

To maintain diversity some of us may need to escape

Mars 3 years 1 Sv (0.4 Sv ISS, 2 mSv earth, 0.02 mSv/Xray, Deinococcus 20kSv)

Page 27: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Steeper than exponential growth

http://www.kurzweilai.net/meme/frame.html?main=/articles/art0184.html

0.001

0.01

0.1

1

10

100

1000

10000

1970 1980 1990 2000 2010

bp/$

bp/$

Page 28: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

The issue is not speed, but integration.Cost per 99.99% bp : Including Reagents, Personnel, Equipment/5yr, Overhead/sq.m• Sub-mm scale : 1m = femtoliter (10-15)• Instruments $2-50K per CPU

Why improve measurements?

Human genomes (6 billion)2 = 1019 bpImmune & cancer genome changes >1010 bp per time pointRNA ends & splicing: in situ 1012 bits/mm3

Biodiversity: Environmental & lab evolution Compact storage 105 now to 1017 bits/ mm3 eventually

& How? ($1K per genome, 108-1013 bits/$ )

Page 29: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Projected costs determine when biosystems data overdetermination is feasible.

In 1984, pre-HGP (X, pBR322, etc.) 0.1bp/$, would have been $30B per human

genome.

In 2002, (de novo full vs. resequencing ) ABI/Perlegen/Lynx: $300M vs. $3M

103 bp/$ (4 log improvement)

Other data I/O (e.g. video) 1013 bits/$

Page 30: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Polymerase colony fluorescent in situ sequencing (FISSEQ)

Mitra & Shendure

Single pixel sequences

Page 31: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Inorganic nanofabrication

Diatoms, metalloenzymes, Photoreactions, etc.http://www.urbanrivers.org/diatoms.htmlhttp://norrisgroup.uchicago.edu/research/xrayposter/prc.jpg

Page 32: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

David Goodsell

Page 33: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Synthetic Mini-genomes• Digital input > complex atomically precise output• 90kbp genome? All 3D structures known. • 100X faster replication (10 sec doubling) & selection to evolve widgets & systems?• Utility of mirror-image & other unnatural polymers.• Chassis & power supply

Page 34: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

A 90 kbp mini-genomeSP (3D) StochimetryMge# Bp Min access# Gene L.end R.endorientationlen2 SequenceTotal 144 107 89,498 74,310 285316S 1 y 1418 1418 3968 rrsB 4164238 4165779 > 124 aaattgaagagtttgatcatggctcagattgaacgctggcggcaggcctaacacatgcaagtcgaacggtaacaggaagaagcttgcttctttgctgacgagtggcggacgggtgagtaatgtctgggaaactgcctgatggagggggataactactggaaacggtagctaataccgcataacgtcgcaagaccaaagagggggaccttcgggcctcttgccatcggatgtgcccagatgggattagctagtagg23S 1 y 2903 2903 3970 rrlB 4166220 4169123 > 1 ggttaagcgactaagcgtacacggtggatgccctggcagtcagaggcgatgaaggacgtgctaatctgcgataagcgtcggtaaggtgatatgaaccgttataaccggcgatttccgaatggggaaacccagtgtgtttcgacacactatcattaactgaatccataggttaatgaggcgaaccgggggaactgaaacatctaagtaccccgaggaaaagaaatcaaccgagattcccccagtagcggcgagcga5S 1 120 120 3971 rrfB 4169216 4169335 > 0 tgcctggcggcagtagcgcggtggtcccacctgaccccatgccgaactcagaagtgaaacgccgtagcgccgatggtagtgtggggtctccccatgcgagagtagggaactgccaggcat10sb (RNaseP) 375 375 3123 rnpB 3268233 3267857 < 2 gaagctgaccagacagtcgccgcttcgtcgtcgtcctcttcgggggagacgggcggaggggaggaaagtccgggctccatagggcagggtgccaggtaacgcctgggggggaaacccacgaccagtgcaacagagagcaaaccgccgatggcccgcgcaagcgggatcaggtaagggtgaaagggtgcggtaagagcgcaccgcgcggctggtaacagtccgtggcacggtaaactccacccggagcaaggccaatRNAs 20-46 y 3136 1364 3939 eg. gltT 4165951 4166026 > gtccccttcgtctagaggcccaggacaccgccctttcacggcggtaacaggggttcgaatcccctaggggacgccaCca (no) ? 1236 3056 cca 3199532 3200770 > 3 gtgaagatttatctggtcggtggtgctgttcgggatgcattgttagggctaccggtcaaagacagagattgggtggtggtcggcagtacgccacaggagatgctcgacgcgggctaccagcaggtaggccgcgattttcctgtgtttctgcatccgcaaacgcatgaagagtatgcgctggcacgtaccgaacggaaatccggttccggttacaccggttttacttgctatgccgcaccggatgtcacgctggaaTrmA (22?) ? 1098 3965 trmA 4159749 4160849 < 3 atgacccccgaacaccttccaacagaacagtatgaagcgcagttagccgaaaaagtggtacgtttgcaaagtatgatggcaccgttttctgacctggttccggaagtgtttcgctcgccggtcagtcattaccggatgcgcgcggagttccgcatctggcacgatggcgatgacctgtatcacatcattttcgatcaacaaaccaaaagccgcatccgcgtggatagcttccccgccgccagtgaacttatcaacBstNBI (no) 1815 AF329098 1 1815 > 0 atggctaaaaaagttaattggtatgtttcttgttcacctagaagtccagaaaaaattcagcctgagttaaaagtactagcaaattttgagggaagttattggaaaggggtaaaagggtataaagcacaagaggcatttgctaaagaacttgctgctttaccacaattcttaggtactacttataaaaaagaagctgcattttctactcgagacagagtggcaccaatgaaaacttatggtttcgtatttgtagatTri1 ? AP001918 traI 92673 97943 > atgatgagtattgcgcaggtcagatcggccggaagtgccgggaactattataccgacaaggataattactatgtgctgggcagcatgggagaacgctgggccggcaggggggctgaacagctggggctgcagggcagtgtcgataaggatgtttttacccgtcttctggagggcaggctgccggacggagcggatctaagccgcatgcaggatggcagtaacaggcatcgtcccggctacgatctgaccttctccFlp no 1272 NC_001398 5573 523 > 0 atgccacaatttggtatattatgtaaaacaccacctaaggtgcttgttcgtcagtttgtggaaaggtttgaaagaccttcaggtgagaaaatagcattatgtgctgctgaactaacctatttatgttggatgattacacataacggaacagcaatcaagagagccacattcatgagctataatactatcataagcaattcgctgagtttcgatattgtcaataaatcactccagtttaaatacaagacgcaaaaaGFP no 717 AF302837 27 743 > 0 atgagtaaaggagaagaacttttcactggagttgtcccaattcttgttgaattagatggcgatgttaatgggcaaaaattctctgtcagtggagagggtgaaggtgatgcaacatacggaaaacttacccttaaatttatttgcactactgggaagctacctgttccatggccaacacttgtcactactttcgcgtatggtcttcaatgctttgcgagatacccagatcatatgaaacagcatgactttttcaagRnpa (36%) 357 357 3704 rnpA 3882122 3882481 > 3 gtggttaagctcgcatttcccagggagttacgcttgttaactcccagtcaattcacattcgtcttccagcagccacaacgggctggcacgccgcaaattaccattctcggccgcctgaattcgctggggcatccccgtatcggtcttacagtcgccaagaaaaacgttcgacgcgcccatgaacgcaatcggattaaacgtctgacgcgtgaaagcttccgtctgcgccaacatgaactcccggctatggatttcBstPol multiprot 2631 2631 U93028 95 2728 > 3 atgagattgaagaaaaaactcgtcttaattgatggcaacagtgtggcataccgcgccttttttgccttgccacttttgcataacgacaaaggcattcatacgaatgcggtttacgggtttacgatgatgttgaacaaaattttggcggaagaacaaccgacccatttacttgtagcgtttgacgccggaaaaacgacgttccggcatgaaacgtttcaagagtataaaggcggacggcaacaaacgcccccggaaRpol_Bpt7 multiprot 2649 2649 NC_001604 3171 5822 > 2 atgaacacgattaacatcgctaagaacgacttctctgacatcgaactggctgctatcccgttcaacactctggctgaccattacggtgagcgtttagctcgcgaacagttggcccttgagcatgagtcttacgagatgggtgaagcacgcttccgcaagatgtttgagcgtcaacttaaagctggtgaggttgcggataacgctgccgccaagcctctcatcactaccctactccctaagatgattgcacgcatcEFTu 451 1179 1179 3339 tufA 3467782 3468966 < 6 gtgtctaaagaaaaatttgaacgtacaaaaccgcacgttaacgttggtactatcggccacgttgaccacggtaaaactactctgaccgctgcaatcaccaccgtactggctaaaacctacggcggtgctgctcgtgcattcgaccagatcgataacgcgccggaagaaaaagctcgtggtatcaccatcaacacttctcacgttgaatacgacaccccgacccgtcactacgcacacgtagactgcccggggcacEFG (59%) 89 2109 2109 3340 fusA 3469037 3471151 < 6 atggctcgtacaacacccatcgcacgctaccgtaacatcggtatcagtgcgcacatcgacgccggtaaaaccactactaccgaacgtattctgttctacaccggtgtaaaccataaaatcggtgaagttcatgacggcgctgcaaccatggactggatggagcaggagcaggaacgtggtattaccatcacttccgctgcgactactgcattctggtctggtatggctaagcagtatgagccgcatcgcatcaacEFTs 433 846 846 170 tsf 190857 191708 > 6 atggctgaaattaccgcatccctggtaaaagagctgcgtgagcgtactggcgcaggcatgatggattgcaaaaaagcactgactgaagctaacggcgacatcgagctggcaatcgaaaacatgcgtaagtccggtgctattaaagcagcgaaaaaagcaggcaacgttgctgctgacggcgtgatcaaaaccaaaatcgacggcaactacggcatcattctggaagttaactgccagactgacttcgttgcaaaaEFP (no) 26 561 561 4147 efp 4373277 4373843 > 6 atggcaacgtactatagcaacgattttcgtgctggtcttaaaatcatgttagacggcgaaccttacgcggttgaagcgagtgaattcgtaaaaccgggtaaaggccaggcatttgctcgcgttaaactgcgtcgtctgctgaccggtactcgcgtagaaaaaaccttcaaatctactgattccgctgaaggcgctgatgttgtcgatatgaacctgacttacctgtacaacgacggtgagttctggcacttcatgIF1 173 213 213 884 infA 925448 925666 < 6 atggccaaagaagacaatattgaaatgcaaggtaccgttcttgaaacgttgcctaataccatgttccgcgtagagttagaaaacggtcacgtggttactgcacacatctccggtaaaatgcgcaaaaactacatccgcatcctgacgggcgacaaagtgactgttgaactgaccccgtacgacctgagcaaaggccgcattgtcttccgtagtcgctgaIF2 (25%) 142 2682 2682 3168 infB 3310983 3313655 < -9 atgacagatgtaacgattaaaacgctggccgcagagcgacagacctccgtggaacgcctggtacagcaatttgctgatgcaggtatccggaagtctgctgacgactctgtgtctgcacaagagaaacagactttgattgaccacctgaatcagaaaaattcaggcccggacaaattgacgctgcaacgtaaaacacgcagcacccttaacattcctggtaccggtggaaaaagcaaatcggtacaaatcgaagtcIF3 (~50%) 196 540 540 1718 infC 1798120 1798662 < 3 attaaaggcggaaaacgagttcaaacggcgcgccctaaccgtatcaatggcgaaattcgcgcccaggaagttcgcttaacaggtctggaaggcgagcagcttggtattgtgagtctgagagaagctctggagaaagcagaagaagccggagtagacttagtcgagatcagccctaacgccgagccgccggtttgtcgtataatggattacggcaaattcctctatgaaaagagcaagtcttctaaggaacagaagRF1 (no) 258 1080 1211 prfA 1264235 1265317 > 3 atgaagccttctatcgttgccaaactggaagccctgcatgaacgccatgaagaagttcaggcgttgctgggtgacgcgcaaactatcgccgaccaggaacgttttcgcgcattatcacgcgaatatgcgcagttaagtgatgtttcgcgctgttttaccgactggcaacaggttcaggaagatatcgaaaccgcacagatgatgctcgatgatcctgaaatgcgtgagatggcgcaggatgaactgcgcgaagctRRF 435 555 555 172 frr 192872 193429 > 3 gtgattagcgatatcagaaaagatgctgaagtacgcatggacaaatgcgtagaagcgttcaaaacccaaatcagcaaaatacgcacgggtcgtgcttctcccagcctgctggatggcattgtcgtggaatattacggcacgccgacgccgctgcgtcagctggcaagcgtaacggtagaagattcccgtacactgaaaatcaacgtgtttgatcgttcaatgtctccggccgttgaaaaagcgattatggcgtccRL1 (~50%) 1 82 699 699 3984 rplA 4176457 4177161 > 6 atggctaaactgaccaagcgcatgcgtgttatccgcgagaaagttgatgcaaccaaacagtacgacatcaacgaagctatcgcactgctgaaagagctggcgactgctaaattcgtagaaagcgtggacgtagctgttaacctcggcatcgacgctcgtaaatctgaccagaacgtacgtggtgcaactgtactgccgcacggtactggccgttccgttcgcgtagccgtatttacccaaggtgcaaacgctgaaRL2 1 154 816 816 3317 rplB 3448180 3449001 < 6 atggcagttgttaaatgtaaaccgacatctccgggtcgtcgccacgtagttaaagtggttaaccctgagctgcacaagggcaaaccttttgctccgttgctggaaaaaaacagcaaatccggtggtcgtaacaacaatggccgtatcaccactcgtcatatcggtggtggccacaagcaggcttaccgtattgttgacttcaaacgcaacaaagacggtatcccggcagttgttgaacgtcttgagtacgatccg

Page 35: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Future of Human Nature

Systems Biology & ModelingSpeculative SpecificationsPotential timelinesLimits to MassLimits to Cost (energy & environment)How? Potential pathways to nanotechnology Human biology keeping up: stem cells

Page 36: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact
Page 37: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact
Page 38: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Faster than exponential

Page 40: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Modeling bio-effects on global warming

<1% of photosynthetic biomass, phytoplankton ~50% carbon fixation.

Chisholm et al. (2001) Science 294(5541):309-1. Dis-crediting ocean fertilization.

models

The equatorial Pacific, sub arctic pacific and Southern Ocean are high-nutrient low-chlorophyll (HNLC) areas which may support higher plant biomass if micro-nutrients such as Fe were added... No ocean fertilization study has been long lived enough to follow the effects of iron fertilization through the food web, and hence determine the potential for long term carbon sequestration.

Page 41: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

HIV-1 resequencing

New nucleotide sequences processed in GenBank per month (above)

Today's total is:

Page 42: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

HIV-AIDS models

HIV economic modeling

D. Kirschner & G WebbResistance, Remission, & Qualitative Differences in HIV Chemotherapy (Pub)

Therapy T(0),Vs(0) in mm-3 =306, 21 (5.8 years), 217, 31/mm3 (7.7 years), 100, 69/mm3 (8.4 years), 43, 156/mm3 (8.6 years). The rates of exponential increase in Fig. 2a (.03, .02, .01, .005) are inversely correlated to starting CD4+ T-cell counts; decay rates in Fig 2b (all -.2) are not correlated to starting viral levels (different viral set-points would give different values for the parallel slopes) (1,2). The lack of correlation of viral decay rates is an indication of slower clearance of wild-type virus in the external lymphoid compartment. The time to the downward spike in Figure 2b is correlated to starting viral levels (1). The treatment parameters c1=2.0, c2=.17, c3=.15 and the resistance mutation parameter q=10-6 are the same in all four simulations.

Page 43: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

HIV treatement model parameters

Page 44: Ecologies, Economies, & Exponentials in Modeling Technological Goals & Human Impact

Vaccines for the 21st Centuryhttp://books.nap.edu/html/vacc21/

Level I Most favorable: saves money & Quality-Adjusted Life 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 & 65 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” 1985 & 1999.

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Role of genomics & computational biology in vaccine R&D?

DNA vaccines , Intracellular vaccines, RNAi, multiplexed…

Gaschen et al. (2002) Science 296(5577):2354-60 Diversity considerations in HIV-1 vaccine selection.

Malaria & Mosquito genomes

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Human Genome Project Ethical, Legal & Social Issues (ELSI)

Fairness - Genetic non-discriminationPrivacyReproductive rights - cloningPsychological stigmatizationClinical quality-controlSafety and environmental issues - GMO & biowarfareUncertainties - testing minorsConceptual & philosophical implications - diversity?Commercialization of products - Who owns?

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Human Genome Project Ethical, Legal & Social Issues (ELSI)

Clinton, June 26, 2000. The Genetic Nondiscrimination in Health Insurance and Employment Act of 1999, introduced by Senator Daschle and Congresswoman Slaughter, that will extend these employment protections to the private sector and finish the job of helping to extend protections to individuals purchasing health insurance, begun with the Health Insurance Portability and Accountability Act.

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ELSI: Do Races Differ? Not Really, DNA Shows

Hb variants "evolved to help the ancestors of these groups resist malaria infection, but both prove lethal when inherited in a double dose. As with differences in skin pigmentation, the pressure of the environment to develop a group-wide trait was powerful, and the means to do so simple and straightforward, through the alteration of a single gene.

A founder effect explains the high incidence of Huntington's neurodegenerative disease in the Lake Maracaibo region of Venezuela, and of Tay-Sachs disease among Ashkenazi Jews.

But Dr. Naggert emphasized that medical geneticists had a much better chance of unearthing these founder effects by scrutinizing small, isolated and well-defined populations, like the northern Finns, the Basques of Spain, or the Amish of Pennsylvania, than they did by going after "races."

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Dangers of model-free science

Lysenko: inheritance of acquired characteristics."His habit was to report only successes. His results were based on extremely small samples, inaccurate records, and the almost total absence of control groups. An early mistake in calculation, which caused comment among other specialists, made him extremely negative toward the use of mathematics in science. "

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Dangers of ethics-free science

The 1979 release of Anthrax-836 spores in Sverdlovsk."In 1953 a leak…In 1956, Sizov found that one of the rodents captured in the Kirov sewers had developed a new strain more virulent than the original. The army immediately ordered him to cultivate the new strain…to install in the SS-18s targeted on western cities."

Alibek & Handelman "Biohazard" 1999 (Davis)

How can we improve our genome engineering tools preferentiallytoward defense and away from terrorism?

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Genetically modified organismsDeveloping world needs:Agri-vaccines, salt & drought tolerance

Terminators: Allergen dispersal vs reseeding

“Organic”: no inorganic fertilizers means high animal load.

Many natural pesticides are carcinogens including estragole (basil), safrole (natural root beer), symphytine (comfrey tea), hydrazine (mushrooms) & allyl isothiocyanate (brown mustard); psoralen (celery) & aflatoxin(nuts & cereals).

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Cloning & stem cells

Why do clones exhibit developmental defects?Study epigenetic reprograming with expression profiles?

Can we increase fraction of stem cells without going through cloning?“Radial glial cells that lacked a functional form of a transcription factor called Pax6 could not generate neurons. But when Pax6 was introduced into glial precursor cells, these cells started to produce neurons.” (ref)

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Models for education & decision-making

Improve our ability to deal with:

UncertaintyComplexityQuantitationExceptions (collect and cherish)Comparisons of diverse entitiesTranslation & integrationContinuity over time

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Automate Data Model Similarity quality quality search

X-ray 1960 resolution |o-c|/o DALIdiffraction < 0.2nm R < 0.2

Sequence 1988 discrepancy conserved BLAST bp <0.01% proteins

Function 1999 cc, t-test AlignACE Correlation Map & specificity

Measures of quality of structural & functional genomic data

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Biophilia & Consilience

Kellert & Wilson 1993 The Biophilia Hypothesis.E. O. Wilson 1999 - Consilience: The Unity of Knowledge

Consilience - Long-separated fields come together and create new insights; e.g. chemistry & genetics created the powerful new science of molecular biology. Is all human endeavor, from religious feeling to financial markets to fine arts, ripe for explaining by hard science?

Biophilia -- the connections that human beings subconsciously make with other living beings. (Cute animals, snake dreams, therapeutic greenery & natural sounds …)

How might genomics & computational biology contribute?

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PET & MRI

Positron emission tomography

555MBq of 15O butanol , scan for 60s; effective image resolution of 9mm (FWHM) .

Significant activations for the contrast religious-recite vs. rest in religious subjects, rendered onto canonical T1-weighted image of SPM97d (P <0.001, uncorrected for multiple comparisons) For task comparisons, an ancova (analysis of covariance) model was fitted to the data for each voxel.

Azari et al. Eur J Neurosci 2001 13(8):1649-52. Neural correlates of religious experience.

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MRI & gene expression

Louie et al. (2000) Nat Biotechnol 18(3):321-5 In vivo visualization of gene expression using magnetic resonance imaging.

Suitable for intact, opaque organisms in 3D at cellular resolution (10 )

MRI (live)

Whole-mount(fixed)