information technology: the foundation for 21st century research · 2018-04-16 · information...

Information Technology:The Foundation for 21st Century Research

Robert J. RobbinsFred Hutchinson Cancer Research Center

1100 Fairview Avenue North, LV-101Seattle, Washington 98109

[email protected](206) 667 2920

Abstract

Over the next few years, the relentless exponential effect of Moore'sLaw will profoundly affect nearly all areas of science and technology.By 2005, analytical power previously available only at supercomputercenters will exist on every desktop and the volume of electronic datawill be enormous. Even now, a standard Intel computer delivers morecomputational power than the first supercomputer and GenBankacquires more data every ten weeks than it did in its first ten years.

Science of the 21st Century will require an adequate informationinfrastructure. Those with access will participate in the transformationof science; those without may become irrelevant. If public support forinformation infrastructure is inadequate, some types of research mayonly be possible in the private sector.

Abstract

Over the next few years, the relentless exponential effect of Moore'sLaw will profoundly affect nearly all areas of science and technology.By 2005, analytical power previously available only at supercomputercenters will exist on every desktop and the volume of electronic datawill be enormous. Even now, a standard Intel computer delivers morecomputational power than the first supercomputer and GenBankacquires more data every ten weeks than it did in its first ten years.

Science of the 21st Century will require an adequate informationinfrastructure. Those with access will participate in the transformationof science; those without may become irrelevant. If public support forinformation infrastructure is inadequate, some types of research mayonly be possible in the private sector.

3

Topics

• Biotechnology and information technology will be the“magic” technologies of the 21st Century.


4

Topics


• Moore’s Law constantly transforms IT (and everythingelse).



5

Topics



• Information Technology (IT) has a special relationshipwith biology.




6

Topics




• 21st-Century biology will be based on bioinformatics.




• 21st-Century biology will be based on bioinformatics.

7

Topics




• 21st-Century biology will be based on bioinformatics.• Bioinformatics is emerging as an independent

discipline.





discipline.

8

Topics





discipline.• A connected, federated information infrastructure for

biology is needed.






biology is needed.

9

Topics






biology is needed.• Current support for public bio-information

infrastructure seems inadequate.






biology is needed.• Current support for public bio-information

infrastructure seems inadequate.

Introduction

MagicalTechnology

11

Magic

To a person from 1897, much currenttechnology would seem like magic.

12

Magic


What technology of 2097 would seemmagical to a person from 1997?

13

Magic



Candidate: Biotechnology so advanced that the distinctionbetween living and non-living is blurred.

Information technology so advanced that accessto information is immediate and universal.



14

Magic







Moore’s Law

Transforms InfoTech(and everything else)

16

Moore’s Law: The Statement

Every eighteen months, thenumber of transistors that canbe placed on a chip doubles.

Gordon Moore, co-founder of Intel...Gordon Moore, co-founder of Intel...

17

Moore’s Law: The EffectP

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Moore’s Law: The Effect

Three Phases of Novel IT Applications

• It’s Impossible



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• It’s Impractical




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• It’s Overdue




• It’s Overdue

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Relevance for biology?Relevance for biology?

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Cost (constant performance)

1975 1980 1985 1990 1995 2000 2005

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1975 1980 1985 1990 1995 2000 2005

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1975 1980 1985 1990 1995 2000 2005

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UniversityPurchase

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1975 1980 1985 1990 1995 2000 2005

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RO1 GrantPurchase

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1975 1980 1985 1990 1995 2000 2005

1,000

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UnplannedPurchases

IT-BiologySynergismIT-BiologySynergism

38

IT is Special

Information Technology:

• affects the performance and themanagement of tasks



39

IT is Special



• allows the manipulation of hugeamounts of highly complex data




40

IT is Special




• is incredibly plastic




• is incredibly plastic(programming and poetry are both exercises in pure thought)

41

IT is Special




• is incredibly plastic




• is incredibly plastic(programming and poetry are both exercises in pure thought)

• improves exponentially(Moore’s Law)

42

Biology is Special

Life is Characterized by:

• individuality


• individuality

43

Biology is Special


• individuality

• historicity


• individuality

• historicity

44

Biology is Special


• individuality

• historicity

• contingency


• individuality

• historicity

• contingency

45

Biology is Special


• individuality

• historicity

• contingency

• high (digital) information content


• individuality

• historicity

• contingency


46

Biology is Special


• individuality

• historicity

• contingency



• individuality

• historicity

• contingency


No law of large numbers...No law of large numbers...

47

Biology is Special


• individuality

• historicity

• contingency



• individuality

• historicity

• contingency


No law of large numbers, since everyliving thing is genuinely unique.No law of large numbers, since everyliving thing is genuinely unique.

48

IT-Biology Synergism

• Physics needs calculus, the method formanipulating information aboutstatistically large numbers of vanishinglysmall, independent, equivalent things.


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IT-Biology Synergism


• Biology needs information technology, themethod for manipulating informationabout large numbers of dependent,historically contingent, individual things.


• Biology needs information technology, themethod for manipulating informationabout large numbers of dependent,historically contingent, individual things.

50

Biology is Special

For it is in relation to the statistical point of viewthat the structure of the vital parts of livingorganisms differs so entirely from that of anypiece of matter that we physicists and chemistshave ever handled in our laboratories ormentally at our writing desks.

For it is in relation to the statistical point of viewthat the structure of the vital parts of livingorganisms differs so entirely from that of anypiece of matter that we physicists and chemistshave ever handled in our laboratories ormentally at our writing desks.

Erwin Schrödinger. 1944. What is Life.Erwin Schrödinger. 1944. What is Life.

51

Genetics as Code

[The] chromosomes ... contain in some kind of code-script the entire pattern of the individual's futuredevelopment and of its functioning in the mature state.... [By] code-script we mean that the all-penetratingmind, once conceived by Laplace, to which everycausal connection lay immediately open, could tellfrom their structure whether [an egg carrying them]would develop, under suitable conditions, into a blackcock or into a speckled hen, into a fly or a maize plant,a rhodo-dendron, a beetle, a mouse, or a woman.

[The] chromosomes ... contain in some kind of code-script the entire pattern of the individual's futuredevelopment and of its functioning in the mature state.... [By] code-script we mean that the all-penetratingmind, once conceived by Laplace, to which everycausal connection lay immediately open, could tellfrom their structure whether [an egg carrying them]would develop, under suitable conditions, into a blackcock or into a speckled hen, into a fly or a maize plant,a rhodo-dendron, a beetle, a mouse, or a woman.

Erwin Schrödinger. 1944. What is Life.Erwin Schrödinger. 1944. What is Life.

52

One Human SequenceWe now know thatSchrödinger’s mysterioushuman “code-script”consists of 3.3 billionbase pairs of DNA.

53

One Human Sequence

Typed in 10-pitch font, one human sequence would stretch for morethan 5,000 miles. Digitally formatted, it could be stored on one CD-ROM. Biologically encoded, it fits easily within a single cell.

We now know thatSchrödinger’s mysterioushuman “code-script”consists of 3.3 billionbase pairs of DNA.

54

Bio-digital Information

DNA is a highly efficient digital storage device:

• There is more mass-storage capacity in theDNA of a side of beef than in all the hard drivesof all the world’s computers.



55

Bio-digital Information



• Storing all of the (redundant) information in allof the world’s DNA on computer hard diskswould require that the entire surface of the Earthbe covered to a depth of three miles in Conner1.0 gB drives.



• Storing all of the (redundant) information in allof the world’s DNA on computer hard diskswould require that the entire surface of the Earthbe covered to a depth of three miles in Conner1.0 gB drives.

Genomics:An ExampleGenomics:

An Example

57

Human Genome Project - Goals– construction of a high-resolution genetic map of the human

genome;– construction of a high-resolution genetic map of the human

genome;

USDOE. 1990. Understanding Our Genetic Inheritance.The U.S. Human Genome Project: The First Five Years.


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genome;

– production of a variety of physical maps of all humanchromosomes and of the DNA of selected modelorganisms;

– construction of a high-resolution genetic map of the humangenome;




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genome;


– determination of the complete sequence of human DNA andof the DNA of selected model organisms;






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genome;



– development of capabilities for collecting, storing,distributing, and analyzing the data produced;







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genome;




– creation of appropriate technologies necessary to achievethese objectives.





– creation of appropriate technologies necessary to achievethese objectives.



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Infrastructure and the HGP

Progress towards all of the [Genome Project]goals will require the establishment of well-funded centralized facilities, including a stockcenter for the cloned DNA fragmentsgenerated in the mapping and sequencingeffort and a data center for the computer-basedcollection and distribution of large amounts ofDNA sequence information.

Progress towards all of the [Genome Project]goals will require the establishment of well-funded centralized facilities, including a stockcenter for the cloned DNA fragmentsgenerated in the mapping and sequencingeffort and a data center for the computer-basedcollection and distribution of large amounts ofDNA sequence information.

National Research Council. 1988. Mapping and Sequencing theHuman Genome. Washington, DC: National Academy Press. p. 3

National Research Council. 1988. Mapping and Sequencing theHuman Genome. Washington, DC: National Academy Press. p. 3

63

GenBank Totals (Release 103)

DIVISION

Phage Sequences (PHG)Viral Sequences (VRL)

Bacteria (BCT)

Plant, Fungal, and Algal Sequences (PLN)

Invertebrate Sequences (INV)

Rodent Sequences (ROD)Primate Sequences (PRI1–2)

Other Mammals (MAM)Other Vertebrate Sequences (VRT)

High-Throughput Genome Sequences (HTG)

Genome Survey Sequences (GSS)Structural RNA Sequences (RNA)

Sequence Tagged Sites Sequences (STS)Patent Sequences (PAT)

Synthetic Sequences (SYN)Unannotated Sequences (UNA)

EST1-17

TOTALS

Entries

1,313 45,355 38,023

44,553

29,657

36,967 75,587 12,744 17,713

1,120

42,628 4,802 52,824 87,767 2,577 2,480

1,269,737

1,765,847

Base Pairs

2,138,810 44,484,848 88,576,641

92,259,434

105,703,550

45,437,309 134,944,314 12,358,310 17,040,159

72,064,395

22,783,326 2,487,397 18,161,532 27,593,724 5,698,945 1,933,676

466,634,317

1,160,300,687

Per Cent

0.184%3.834%7.634%

7.951%

9.110%

3.916%11.630%

1.065%1.469%

6.211%

1.964%0.214%1.565%2.378%0.491%0.167%

40.217%

100.000%

Per Cent

0.074%2.568%2.153%

2.523%

1.679%

2.093%4.280%0.722%1.003%

0.063%

2.414%0.272%2.991%4.970%0.146%0.140%

71.905%

100.000%

64

Base Pairs in GenBank

0

2 0 0 ,0 0 0 ,0 0 0

4 0 0 ,0 0 0 ,0 0 0

6 0 0 ,0 0 0 ,0 0 0

8 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0

1 ,2 0 0 ,0 0 0 ,0 0 0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105

GenBank Release Numbers

9493929190898887 95 96 97

65


0

2 0 0 ,0 0 0 ,0 0 0

4 0 0 ,0 0 0 ,0 0 0

6 0 0 ,0 0 0 ,0 0 0

8 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0

1 ,2 0 0 ,0 0 0 ,0 0 0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105


9493929190898887 95 96 97

Growth in GenBank is exponential.More data were added in the last tenweeks than were added in the first tenyears of the project.


66


0

2 0 0 ,0 0 0 ,0 0 0

4 0 0 ,0 0 0 ,0 0 0

6 0 0 ,0 0 0 ,0 0 0

8 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0

1 ,2 0 0 ,0 0 0 ,0 0 0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105


9493929190898887 95 96 97



At this rate, what’s next...At this rate, what’s next...

67

ABI Bass-o-Matic Sequencer

In with the sample, out with the sequence...

TGCGCATCGCGTATCGATAG

speed

gB/sec

EnterDefrost

7 8 9

4 5 6

1 2 3

0

+

-

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What’s Really Next

The post-genome era in biologicalresearch will take for granted readyaccess to huge amounts of genomicdata.

The challenge will be understandingthose data and using the understandingto solve real-world problems...

69


0

2 0 ,0 0 0 ,0 0 0

4 0 ,0 0 0 ,0 0 0

6 0 ,0 0 0 ,0 0 0

8 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 0

1 2 0 ,0 0 0 ,0 0 0

1 4 0 ,0 0 0 ,0 0 0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105


9493929190898887 95 96 97

Net ChangesNet Changes

70

Base Pairs in GenBank (Percent Increase)

0 .0 0 %

1 0 .0 0 %

2 0 .0 0 %

3 0 .0 0 %

4 0 .0 0 %

5 0 .0 0 %

6 0 .0 0 %

7 0 .0 0 %

8 0 .0 0 %

9 0 .0 0 %

1 0 0 .0 0 %

85 86 87 88 89 90 91 92 93 94 95 96

Year

Percent Increaseaverage = 56%

Percent Increaseaverage = 56%

71

Projected Base Pairs

11 0

1 0 01 ,0 0 0

1 0 ,0 0 01 0 0 ,0 0 0

1 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 01 ,0 0 0 ,0 0 0 ,0 0 0

1 0 ,0 0 0 ,0 0 0 ,0 0 01 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

90 95 0 5 10 15 20 25

Year

Assumed annual growth rate: 50%(less than current rate)

72


11 0

1 0 01 ,0 0 0

1 0 ,0 0 01 0 0 ,0 0 0

1 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 01 ,0 0 0 ,0 0 0 ,0 0 0

1 0 ,0 0 0 ,0 0 0 ,0 0 01 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

90 95 0 5 10 15 20 25

Year

Assumed annual growth rate: 50%(less than current rate)

Is this crazy?One trillion bp by 2015100 trillion by 2025


73


11 0

1 0 01 ,0 0 0

1 0 ,0 0 01 0 0 ,0 0 0

1 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 01 ,0 0 0 ,0 0 0 ,0 0 0

1 0 ,0 0 0 ,0 0 0 ,0 0 01 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 01 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

1 0 0 ,0 0 0 ,0 0 0 ,0 0 0 ,0 0 0

90 95 0 5 10 15 20 25

Year



500,00050,0005,000

Maybe not...Maybe not...

Projected database size,indicated as the numberof base pairs perindividual medicalrecord in the US.

21st CenturyBiology

Post-Genome Era

75

The Post-Genome Era

Post-genome research involves:

• applying genomic tools and knowledge to moregeneral problems

• asking new questions, tractable only to genomicor post-genomic analysis

• moving beyond the structural genomics of thehuman genome project and into the functionalgenomics of the post-genome era

Post-genome research involves:

• applying genomic tools and knowledge to moregeneral problems

• asking new questions, tractable only to genomicor post-genomic analysis

• moving beyond the structural genomics of thehuman genome project and into the functionalgenomics of the post-genome era

76

The Post-Genome Era

Suggested definition:

• functional genomics = biology

Suggested definition:

• functional genomics = biology

77

The Post-Genome Era

An early analysis:An early analysis:

Walter Gilbert. 1991. Towards a paradigmshift in biology. Nature, 349:99.

78

Paradigm Shift in Biology

To use [the] flood of knowledge, which will pouracross the computer networks of the world,biologists not only must become computerliterate, but also change their approach to theproblem of understanding life.

To use [the] flood of knowledge, which will pouracross the computer networks of the world,biologists not only must become computerliterate, but also change their approach to theproblem of understanding life.

Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.

79


The new paradigm, now emerging, is that all the‘genes’ will be known (in the sense of beingresident in databases available electronically),and that the starting point of a biologicalinvestigation will be theoretical. An individualscientist will begin with a theoretical conjecture,only then turning to experiment to follow or testthat hypothesis.

The new paradigm, now emerging, is that all the‘genes’ will be known (in the sense of beingresident in databases available electronically),and that the starting point of a biologicalinvestigation will be theoretical. An individualscientist will begin with a theoretical conjecture,only then turning to experiment to follow or testthat hypothesis.

Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.

80


Case of Microbiology

< 5,000 known and described bacteria

5,000,000 base pairs per genome

25,000,000,000 TOTAL base pairs

If a full, annotated sequence were available for all known bacteria, the practiceof microbiology would match Gilbert’s prediction.

If a full, annotated sequence were available for all known bacteria, the practiceof microbiology would match Gilbert’s prediction.

21st CenturyBiology

The Science

82

Fundamental Dogma

The fundamental dogma of molecular biologyis that genes act to create phenotypes througha flow of information from DNA to RNA toproteins, to interactions among proteins(regulatory circuits and metabolic pathways),and ultimately to phenotypes.

Collections of individual phenotypes, ofcourse, constitute a population.

DNA

RNA

Proteins

Circuits

Phenotypes

Populations

83

Fundamental DogmaDNA

RNA

Proteins

Circuits

Phenotypes

Populations

GenBankEMBLDDBJ

MapDatabases

SwissPROTPIR

PDB

Although a few databases already existto distribute molecular information,


84

Fundamental DogmaDNA

RNA

Proteins

Circuits

Phenotypes

Populations

GenBankEMBLDDBJ

MapDatabases

SwissPROTPIR

PDB

Gene Expression?

Clinical Data ?

Regulatory Pathways?Metabolism?

Biodiversity?

Neuroanatomy?

Development ?

Molecular Epidemiology?

Comparative Genomics?

the post-genomic era will need manymore to collect, manage, and publishthe coming flood of new findings.

the post-genomic era will need manymore to collect, manage, and publishthe coming flood of new findings.



21st CenturyBiology

The People

86

Human Resources Issues

• Reduction in need for non-IT staff• Reduction in need for non-IT staff

87


• Reduction in need for non-IT staff

• Increase in need for IT staff, especially“information engineers”



88






In modern biology, a general trend is toconvert expert work into staff work andfinally into computation. New expertise isrequired to design, carry out, and interpretcontinuing work.

In modern biology, a general trend is toconvert expert work into staff work andfinally into computation. New expertise isrequired to design, carry out, and interpretcontinuing work.

89


Elbert Branscomb: “You must recognize thatsome day you may need as many computerscientists as biologists in your labs.”


90



Craig Venter: “At TIGR, we already havetwice as many computer scientists on ourstaff.”


Craig Venter: “At TIGR, we already havetwice as many computer scientists on ourstaff.”

Exchange at DOE workshop on high-throughput sequencing.Exchange at DOE workshop on high-throughput sequencing.

New Disciplineof Informatics

New Disciplineof Informatics

92

What is Informatics?

Informatics

ComputerScienceResearch

BiologicalApplicationPrograms

93


Informatics combines expertise from:

• domain science (e.g., biology)

• computer science

• library science

• management science

Informatics combines expertise from:

• domain science (e.g., biology)

• computer science

• library science

• management science

All tempered with an engineering mindset...All tempered with an engineering mindset...

94


ISIS

MedicalInformatics

MedicalInformatics

BioInformatics

BioInformatics

OtherInformatics

OtherInformatics

LibraryScience

LibraryScience

ComputerScience

ComputerScience

MgtScienceMgt

Science

DomainKnowledge

EngineeringPrinciples

95

Engineering Mindset

Engineering is often defined as the use ofscientific knowledge and principles for practicalpurposes. While the original usage restrictedthe word to the building of roads, bridges, andobjects of military use, today's usage is moregeneral and includes chemical, electronic, andeven mathematical engineering.


Parnas, David Lorge. 1990. Computer, 23(1):17-22.Parnas, David Lorge. 1990. Computer, 23(1):17-22.

96

Engineering Mindset




... or even information engineering.

97

Engineering Mindset

Engineering education ... stresses findinggood, as contrasted with workable, designs.Where a scientist may be happy with a devicethat validates his theory, an engineer is taughtto make sure that the device is efficient,reliable, safe, easy to use, and robust.



98

Engineering Mindset




The assembly of working, robust systems, on time and onbudget, is the key requirement for a federated informationinfrastructure for biology.

FederatedInformation

Infrastructure

FederatedInformation

Infrastructure

100

National Information Infrastructure

analog

commercialuses

non-commercialuses

digital

Edu Libother ResETC

�

101

FIIST & NII

FIIST(science & technology)

FIIE(engineering)

FIIS(science)

FII(climatology)

FII(chemistry)

FII(geology)

FII(physics)

FII(biology)

FII(physiology)

FII(ecology)

FII(structural biology)

FII(• • •)

FII(geography)

FII(human)

FII(mouse)

FII(• • •)

FII(Arabidopsis)

FII(genomics)

FII(E. coli)

FII(zoology)

FII(botany)

FII(• • •)

FII(systematics)

FII(• • •)

analog

commercialuses

non-commercialuses

digital

Edu Libother ResETC

�

The research component ofthe NII contains a FederatedInformation Infrastructurefor Science and Technology..

The research component ofthe NII contains a FederatedInformation Infrastructurefor Science and Technology..

102

FIIST

FIIST(science & technology)

FIIE(engineering)

FIIS(science)

FII(climatology)

FII(chemistry)

FII(geology)

FII(physics)

FII(biology)

FII(• • •)

FII(geography)

103

FIIB

FII(biology)

FII(physiology)

FII(ecology)

FII(structural biology)

FII(human)

FII(mouse)

FII(• • •)

FII(Arabidopsis)

FII(genomics)

FII(E. coli)

FII(zoology)

FII(botany)

FII(• • •)

FII(systematics)

FII(• • •)

104

Public Funding of Databases

Stand-alone Criteria:

• Is there a need?

• Will this meet the need?

• Can they do it?

• Is it worth it?

105

Public Funding of Databases

Global Criteria:

• Does it adhere to standards?

• Will it interoperate?

• Is there commitment to federation?

• Is it worth it?

106

Information Resources and the GII

Guiding Principles:

• Global value explosion

• Componentry

• Anonymous interoperability

• Technical scalability

• Social scalability

• Value additivity

Funding forBio-InformationInfrastructure

Funding forBio-InformationInfrastructure

108

Call for Change

Among the many new tools that are or will be needed (for 21st-century biology), some of those having the highest priority are:

• bioinformatics

• computational biology

• functional imaging tools using biosensors and biomarkers

• transformation and transient expression technologies

• nanotechnologies

Among the many new tools that are or will be needed (for 21st-century biology), some of those having the highest priority are:

• bioinformatics

• computational biology

• functional imaging tools using biosensors and biomarkers

• transformation and transient expression technologies

• nanotechnologies

Impact of Emerging Technologies on the Biological Sciences: Report of aWorkshop. NSF-supported workshop, held 26-27 June 1995, Washington, DC.

Impact of Emerging Technologies on the Biological Sciences: Report of aWorkshop. NSF-supported workshop, held 26-27 June 1995, Washington, DC.

109

The Problem

• IT moves at “Internet Speed” and respondsrapidly to market forces.


110

The Problem


• IT will play a central role in 21st Centurybiology.



111

The Problem



• Current levels of support for public bio-information infrastructure are too low.




112

The Problem




• Reallocation of federal funding is difficult,and subject to political pressures.





113

The Problem





• Federal-funding decision processes areponderously slow and inefficient.





• Federal-funding decision processes areponderously slow and inefficient.

114

Federal Funding of Bio-Databases

The challenges:The challenges:

115


The challenges:

• providing adequate funding levels

The challenges:


116


The challenges:


• making timely, efficient decisions

The challenges:


• making timely, efficient decisions

IT Budgets

A Reality Check

118

Rhetorical Question

Which is likely to be more complex:

• identifying, documenting, and tracking thewhereabouts of all parcels in transit in the US atone time



119

Rhetorical Question



• identifying, documenting, and analyzing thestructure and function of all individual genes inall economically significant organisms; thenanalyzing all significant gene-gene and gene-environment interactions in those organismsand their environments



• identifying, documenting, and analyzing thestructure and function of all individual genes inall economically significant organisms; thenanalyzing all significant gene-gene and gene-environment interactions in those organismsand their environments

120

Business Factoids

United Parcel Service:

• uses two redundant 3 Terabyte (yes, 3000 GB)databases to track all packages in transit.

• has 4,000 full-time employees dedicated to IT

• spends one billion dollars per year on IT

• has an income of 1.1 billion dollars, againstrevenues of 22.4 billion dollars

United Parcel Service:

• uses two redundant 3 Terabyte (yes, 3000 GB)databases to track all packages in transit.

• has 4,000 full-time employees dedicated to IT

• spends one billion dollars per year on IT

• has an income of 1.1 billion dollars, againstrevenues of 22.4 billion dollars

121

Business ComparisonsCompany Revenues IT Budget Pct

Bristol-Myers Squibb 15,065,000,000 440,000,000 2.92 %

Pfizer 11,306,000,000 300,000,000 2.65 %

Pacific Gas & Electric 10,000,000,000 250,000,000 2.50 %

K-Mart 31,437,000,000 130,000,000 0.41 %

Wal-Mart 104,859,000,000 550,000,000 0.52 %

Sprint 14,235,000,000 873,000,000 6.13 %

MCI 18,500,000,000 1,000,000,000 5.41 %

United Parcel 22,400,000,000 1,000,000,000 4.46 %

AMR Corporation 17,753,000,000 1,368,000,000 7.71 %

IBM 75,947,000,000 4,400,000,000 5.79 %

Microsoft 11,360,000,000 510,000,000 4.49 %

Chase-Manhattan 16,431,000,000 1,800,000,000 10.95 %

Nation’s Bank 17,509,000,000 1,130,000,000 6.45 %

122

Federal Funding of Biomedical-IT

Appropriate funding level:

• approx. 5-10% of research funding

• i.e., 1 - 2 billion dollars per year




123

Federal Funding of Biomedical-IT







Source of estimate:

- Experience of IT-transformed industries.

- Current support for IT-rich biological research.

Source of estimate:

- Experience of IT-transformed industries.

- Current support for IT-rich biological research.

Basics

Business 101

125

Market Forces

Vendors

productsservices

Buyers

$

purchases

In a simple market economy, vendors try to anticipatethe needs of buyers and offer products and services tomeet those needs.

Real users decide whether or not to buy a product orservice, depending upon whether or not it meets a realneed at a reasonable price.

In a simple market economy, vendors try to anticipatethe needs of buyers and offer products and services tomeet those needs.

Real users decide whether or not to buy a product orservice, depending upon whether or not it meets a realneed at a reasonable price.

Business 101 Insight:

Successful vendors target aniche and excel at meeting theneeds of that niche.

Business 101 Insight:

Successful vendors target aniche and excel at meeting theneeds of that niche.

126

Market Forces

VentureCapital

Vendors$

Buyers

$Stock

Offerings

Funding to initiate the developmentof products and services come frominvestors, not from buyers.

Investors decide whether or not toprovide start-up funding based uponthe estimated ability of the vendor tocreate products and services that willmeet real needs at competitive prices.

Funding to initiate the developmentof products and services come frominvestors, not from buyers.

Investors decide whether or not toprovide start-up funding based uponthe estimated ability of the vendor tocreate products and services that willmeet real needs at competitive prices.

$

VendorInvestment

productsservices

$

purchases

127

Federal Funding

Investors

Database

$

Users

productsservices

$

purchases

If biological databases were drivenby market forces, individual userswould choose what services theyneed and individual databaseproviders would choose whatservices to make available.

Investors would provide start-upmoney on the likelihood ofsuccessful products and servicesbeing developed.

Ultimate success would depend onmeeting the needs of real users.Decisions could be made rapidly, inresponse to changing needs andemerging opportunities.

If biological databases were drivenby market forces, individual userswould choose what services theyneed and individual databaseproviders would choose whatservices to make available.

Investors would provide start-upmoney on the likelihood ofsuccessful products and servicesbeing developed.

Ultimate success would depend onmeeting the needs of real users.Decisions could be made rapidly, inresponse to changing needs andemerging opportunities.

128

Federal Funding

Agency

Database

Reviewers

OtherAgencies

AgencyAdvisors

Congress

productsservices

OMB $

$ DatabaseAdvisors

Users

Instead, funding decisions for grant-supported biological databases canfollow a ponderously slow course,with almost no opportunity for real-time input from real users.

Even with the best of intentions at alllevels, this process is slow,inefficient, risk-averse, and non-responsive to the real and changingneeds of users.

Instead, funding decisions for grant-supported biological databases canfollow a ponderously slow course,with almost no opportunity for real-time input from real users.

Even with the best of intentions at alllevels, this process is slow,inefficient, risk-averse, and non-responsive to the real and changingneeds of users.

129


Possible solutions:

• increase the direct support of federal serviceorganizations providing informationinfrastructure (e.g., NCBI).

• reduce support for investigator-initiated, grant-funded public database projects.

• create market forces, initially throughsubsidization, later simply through direct supportfor affected science (e.g., NSFnet into internet).

Possible solutions:

• increase the direct support of federal serviceorganizations providing informationinfrastructure (e.g., NCBI).

• reduce support for investigator-initiated, grant-funded public database projects.

• create market forces, initially throughsubsidization, later simply through direct supportfor affected science (e.g., NSFnet into internet).

130


Creating market forces:

• stop supporting the supply side of biodatabasesthrough slow, inefficient processes.



131




• start supporting the demand side through fast,efficient processes.




132





• provide guaranteed supplementary funding,redeemable only for access to bio-databases.





133






• data stamps





• data stamps

134






• data stamps, AKA food (for-thought) stamps ?!





• data stamps, AKA food (for-thought) stamps ?!

135

Food (for thought) Stamps

Funding Agencies could:

• provide a 10% supplement to every researchgrant in the form of “stamps” redeemable only atdatabase providers.

• allow the “stamps” to be transferable amongscientists, so that a market for them couldemerge.

• provide funding only after the stamps have beenredeemed at a database provider.

Funding Agencies could:

• provide a 10% supplement to every researchgrant in the form of “stamps” redeemable only atdatabase providers.

• allow the “stamps” to be transferable amongscientists, so that a market for them couldemerge.

• provide funding only after the stamps have beenredeemed at a database provider.

136


Problems:

• how to estimate the amount of FFT stamps thatwould actually be redeemed (and thus therequired budget set-aside).

• how to identify “approved” database providers.

• how to initiate the FFT system.

• etc etc

Problems:

• how to estimate the amount of FFT stamps thatwould actually be redeemed (and thus therequired budget set-aside).

• how to identify “approved” database providers.

• how to initiate the FFT system.

• etc etc

137


Alternatives (if no solution emerges):• increasingly inefficient research activities (abject

failure will occur when it becomes simpler torepeat research than to obtain prior results).

• loss of access to bio-databases for public-sectorresearch.

• movement of majority of “important” biologicalresearch into the private sector.

• loss of American pre-eminence (if othercountries solve the problems first).

Alternatives (if no solution emerges):• increasingly inefficient research activities (abject

failure will occur when it becomes simpler torepeat research than to obtain prior results).

• loss of access to bio-databases for public-sectorresearch.

• movement of majority of “important” biologicalresearch into the private sector.

• loss of American pre-eminence (if othercountries solve the problems first).

138

Slides:

http://www.esp.org/rjr/self.pdfhttp://www.esp.org/rjr/self.pdf

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