economic impact of bioinformatics - esp · bioinformatics is the application of information...

Economic Impact of Bioinformatics

Robert J. RobbinsFred Hutchinson Cancer Research Center

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

[email protected](206) 667 2920

http://www.esp.org/rjr/atcc.pdf

Abstract

Biotechnology, the "magic technology" of the 21st Century, depends on genomic research and genomics inturn depends upon information technology. Computers are the instruments that allow us to "see" genomes,just as electron microscopes allow us to see viruses.

When information technology becomes critical for any activity, the economics of that field changessubstantially. In the last 25 years, field after field of human endeavor has been transformed when therelentless effects of Moore's Law, delivering exponentially better computers at exponentially lower prices,eventually provide sufficient computational power at affordable prices.

Bioinformatics is the application of information technology to the problems of biology. With $2500 desktopPCs now delivering more raw computing power than the first Cray, bioinformatics is rapidly becoming thecritical technology for 21st Century biology.

DNA is legitimately seen as a biological mass-storage device, making bioinformatics a sine qua non foreffective genomic research. Others areas of biological investigation are also information rich -- for example,an exhaustive tabulation of the Earth's biodiversity would involve a cross-index of the millions of knownspecies against the approximately 500,000,000,000,000 square meters of the Earth's surface.

Access to bioinformatics support and logistics skills are emerging as rate limiting steps for much biomedicalresearch. As 21st Century biology becomes increasing dependent upon bioinformatics and logistics,competitive dynamics will change, possibly leading to the movement of substantial areas of biologicalresearch from the public to the private sector.

Abstract

Biotechnology, the "magic technology" of the 21st Century, depends on genomic research and genomics inturn depends upon information technology. Computers are the instruments that allow us to "see" genomes,just as electron microscopes allow us to see viruses.

When information technology becomes critical for any activity, the economics of that field changessubstantially. In the last 25 years, field after field of human endeavor has been transformed when therelentless effects of Moore's Law, delivering exponentially better computers at exponentially lower prices,eventually provide sufficient computational power at affordable prices.

Bioinformatics is the application of information technology to the problems of biology. With $2500 desktopPCs now delivering more raw computing power than the first Cray, bioinformatics is rapidly becoming thecritical technology for 21st Century biology.

DNA is legitimately seen as a biological mass-storage device, making bioinformatics a sine qua non foreffective genomic research. Others areas of biological investigation are also information rich -- for example,an exhaustive tabulation of the Earth's biodiversity would involve a cross-index of the millions of knownspecies against the approximately 500,000,000,000,000 square meters of the Earth's surface.

Access to bioinformatics support and logistics skills are emerging as rate limiting steps for much biomedicalresearch. As 21st Century biology becomes increasing dependent upon bioinformatics and logistics,competitive dynamics will change, possibly leading to the movement of substantial areas of biologicalresearch from the public to the private sector.

3

Topics

• Biotechnology will be the magic technology of the 21stCentury.


4

Topics


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



5

Topics



• Information Technology (IT) has a special relationshipwith biology, especially genetics and genomics.




6

Topics




• 21st-Century biology will be based on bioinformaticsand powered by logistics skills.





7

Topics





• Currently, support for public bio-informationinfrastructure seems inadequate.






8

Topics






• In the future, much current public research may moveinto the private sector.






• In the future, much current public research may moveinto the private sector.

Biotechnology

21st Century Magic

10

Magic

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

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Magic


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

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Magic


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

Candidate: Biotechnology so advancedthat the distinction between living andnon-living is blurred.

Candidate: Biotechnology so advancedthat the distinction between living andnon-living is blurred.

Moore’s Law

Transforms InfoTech(and everything else)

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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...

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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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• The first to understand and deploy new ITcapabilities often seize great competitiveadvantage.


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• As improved uses of technology aredeveloped, “business” processes change.



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• Ultimately, access to appropriate ITbecomes essential for simple existence.




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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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1,000,000

10,000,000 UniversityPurchase

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1975 1980 1985 1990 1995 2000 2005

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RO1 GrantPurchase

UniversityPurchase

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1975 1980 1985 1990 1995 2000 2005

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UnplannedPurchases

IT-BiologySynergismIT-BiologySynergism

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IT is Special

Information Technology:

• affects the performance and themanagement of tasks



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IT is Special



• allows the manipulation of hugeamounts of highly complex data




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IT is Special




• is incredibly plastic




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

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IT is Special




• is incredibly plastic




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

• improves exponentially(Moore’s Law)

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Biology is Special

Life is Characterized by:

• individuality


• individuality

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Biology is Special


• individuality

• historicity


• individuality

• historicity

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Biology is Special


• individuality

• historicity

• contingency


• individuality

• historicity

• contingency

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Biology is Special


• individuality

• historicity

• contingency

• high (digital) information content


• individuality

• historicity

• contingency


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Biology is Special


• individuality

• historicity

• contingency



• individuality

• historicity

• contingency


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

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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.

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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.

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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.

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

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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.

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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.



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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

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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

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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...

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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%

70

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)

71


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


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



500,00050,0005,000

Maybe not...Maybe not...

Projected database size,indicated as the number ofbase pairs per individualmedical record in the US.

21st CenturyBiology

Post-Genome Era

74

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

75

The Post-Genome Era

Suggested definition:

• functional genomics = biology

Suggested definition:

• functional genomics = biology

76

The Post-Genome Era

An early analysis:An early analysis:

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

77

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.

78


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.

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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

81

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

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Fundamental DogmaDNA

RNA

Proteins

Circuits

Phenotypes

Populations

GenBankEMBLDDBJ

MapDatabases

SwissPROTPIR

PDB

Although a few databases already existto distribute molecular information,


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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

85

Human Resources Issues

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

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• Reduction in need for non-IT staff

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



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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.

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


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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.

Funding forBio-InformationInfrastructure

Funding forBio-InformationInfrastructure

91

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.

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The Problem

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


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The Problem


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



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The Problem



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




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The Problem




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





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The Problem





• Federal-funding decision processes areponderously slow and inefficient.





• Federal-funding decision processes areponderously slow and inefficient.

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Federal Funding of Bio-Databases

The challenges:The challenges:

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The challenges:

• providing adequate funding levels

The challenges:


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The challenges:


• making timely, efficient decisions

The challenges:


• making timely, efficient decisions

IT Budgets

A Reality Check

101

Rhetorical Question

Which is likely to be more complex:

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



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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

103

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

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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 %

105

Federal Funding of Biomedical-IT

Appropriate funding level:

• approx. 5-10% of research funding

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




106

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.

Private Sector

Future Reality

Who is this man?

Who is this man?

And why should you care?

112

Celera

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options.

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options.

Tony White, chief executive officer of Perkin-Elmer,Teleconference, May 11, 1998Tony White, chief executive officer of Perkin-Elmer,Teleconference, May 11, 1998

113

Celera

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options. We believe that this company combinescompelling technology with unique sequencing strategies,resulting in a genomics sequencing facility with an expectedcapacity ...

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options. We believe that this company combinescompelling technology with unique sequencing strategies,resulting in a genomics sequencing facility with an expectedcapacity ...


114

Celera

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options. We believe that this company combinescompelling technology with unique sequencing strategies,resulting in a genomics sequencing facility with an expectedcapacity greater than that of the current combined world output.

We want this new company to be the definitive source ofgenomic and related medical information, which scientists canuse to better understand human biology and to deliver improvedhealthcare options. We believe that this company combinescompelling technology with unique sequencing strategies,resulting in a genomics sequencing facility with an expectedcapacity greater than that of the current combined world output.


115

Celera

We are not a philanthropic organisation, wehave a revenue model for this. We are surepeople will want to buy the information.

We are not a philanthropic organisation, wehave a revenue model for this. We are surepeople will want to buy the information.

Tony White, chief executive officer of Perkin-Elmer,quoted in The Guardian, 13 May 1998Tony White, chief executive officer of Perkin-Elmer,quoted in The Guardian, 13 May 1998

117

Millenium Pharmaceuticals

Millenium’s bugs

Mark Levin is an engineer. That makes him ideally qualifiedto be a successful biotechnology entrepreneur

Millenium’s bugs

Mark Levin is an engineer. That makes him ideally qualifiedto be a successful biotechnology entrepreneur

TheEconomist

TheEconomist

SEPTEMBER 26TH - OCTOBER 2ND 1998

118

Millenium PharmaceuticalsMany other biotech firms, increasingly desperate for cash asinvestors have shied away from their shares, have queued up todo deals with the pharmaceutical industry at almost any price. MrLevin, though, has been able to dictate his own terms. Uniquelyamong biotechnologists, he seems to have mastered the art ofhaving his cake and eating it.

Except that Mr Levin is not really a biotechnologist at all:he is a chemical engineer. He has worked in process control forcompanies as varied as Miller Brewing and Genentech, a firm ofbiotech pioneers. Whereas biologists tend to see biotech as thesearch for a compound, Mr Levin thinks of it as a complexproduction process. While they concentrate on the bio, he alsothinks hard about the technology.

Many other biotech firms, increasingly desperate for cash asinvestors have shied away from their shares, have queued up todo deals with the pharmaceutical industry at almost any price. MrLevin, though, has been able to dictate his own terms. Uniquelyamong biotechnologists, he seems to have mastered the art ofhaving his cake and eating it.

Except that Mr Levin is not really a biotechnologist at all:he is a chemical engineer. He has worked in process control forcompanies as varied as Miller Brewing and Genentech, a firm ofbiotech pioneers. Whereas biologists tend to see biotech as thesearch for a compound, Mr Levin thinks of it as a complexproduction process. While they concentrate on the bio, he alsothinks hard about the technology.

TheEconomist

TheEconomist


119

Millenium PharmaceuticalsMr Levin focuses on trying to make each link in the discoverychain as efficient as possible. He has assembled an impressivearray of technologies -- including robotics and informationsystems as well as molecular biology. He then enhances themand links them together in novel ways to create what the engineerin him likes to call "technology platforms". The idea is that theseplatforms should help drug searchers to travel rapidly on theirlong and tortuous journey from gene to treatment. Mr Levin'sgoal is to boost the productivity of drug discovery by 50%,which would lead to many more new drugs coming on to themarket each year.

Mr Levin focuses on trying to make each link in the discoverychain as efficient as possible. He has assembled an impressivearray of technologies -- including robotics and informationsystems as well as molecular biology. He then enhances themand links them together in novel ways to create what the engineerin him likes to call "technology platforms". The idea is that theseplatforms should help drug searchers to travel rapidly on theirlong and tortuous journey from gene to treatment. Mr Levin'sgoal is to boost the productivity of drug discovery by 50%,which would lead to many more new drugs coming on to themarket each year.

TheEconomist

TheEconomist


120

Slides:

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

economic impact of bioinformatics - esp · bioinformatics is the application of information...

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