Delta Scan: The Future of Science and Technology, 2005-2055: Promising Applications of Computational Biology

Key Pages

Category:
Science and Technology

Domain:
Biology and Biotechnology, Computer Science

Keywords:
Pharma - biosimulation, computer science, molecular biology, genomics, drug development, pharmacogenomics, computational biology, bioinformatics

Outlook:
The tools of computational biology may be applied at an increasing rate to pharmaceutical innovation in the next 20 to 50 years, resulting in a faster, less costly, and more tailored approach to drug development.

Summary Analysis:
Computer science and molecular biology have made some of the most significant contributions to science over the past 20 years, and computational biology (also known as bioinformatics) seeks to organize the multitude of activities that are emerging from new collaborations between the two fields. Computational biology makes use of advances in computing power, modelling, visualisation, genomics, protein chemistry, and information science, among others, to find relationships among biomarkers, genetics, pharmaceutical responses, normal responses, and diseases. For example, a computational biologist might search the human genome for particular patterns, analyse gene expression data for biologically relevant molecules, or develop models for visualising the interaction of DNA with other molecules. A loosely shared goal of computational biology is to bring the predictive power of mathematics and computer modelling to modern molecular biology and reign in the enormous amount of information produced by genomic sequencing.

Computational biology is showing preliminary signs of successful of applications. The applied subfields generating some of the greatest interest because of their potential impact on biomedicine are biosimulation and pharmacogenomics, and further research progress will come in these areas in the next 3 to 10 years.

Biosimulation is the computer modelling of biological processes and has the character of what some have called a 'laptop lab'. One hope is to use knowledge of the human genome and pharmaceutical chemistry to design new or more effective drugs that could then be 'tested' in computer models before attempting costly clinical trials, although this potential development is still years away.
Pharmacogenomics is the science of inherited variations in drug responses and promises better biomedicine through a personalized approach. The idea is that a patient’s genome could be profiled to predict in advance the effectiveness of a particular drug or treatment. One of the few instances in which this approach has been demonstrated is with the cytochrome P450 (CYP) family of liver enzymes, which are involved in the metabolism of more than 30 different classes of drugs. Genetics tests have been developed to screen for variations and avoid drug overdoses. Another enzyme, thiopurine methyltransferase, has been shown to negatively influence chemotherapy treatments for childhood leukaemia in the rare patient who has a defective variant.

A new industry has developed around applications of computational biology in the last decade. Initial hopes have been tempered, however, and ethical concerns about privacy and property rights to genetic information have arisen. Nonetheless, many new computer applications to aid the drug development process are expected in the next decade. The larger goal of creating a fully predictive biomedicine with tailored treatments is still 20 to 50 years out.

Implications:

Advances in the modeling and visualisation of molecular biology
Reorganization of university research under the categories of computational biology and bioinformatics
Increased university and industry collaborative research centers
Improved dosing and efficacy of drugs for individuals; improved diagnostics and screening, leading to fewer adverse drug reactions
Reduced research and development costs and shortened development schedules for new drugs
Emergence of a new approach to drug discovery, where 'designer drugs' are deduced from the genetic code itself
Emergence of new ethical (privacy, property rights) concerns about databases of individual genetic records

Early Indicators:

Passage by Iceland in 1998 of the Health Sector Database Act, giving the DeCode exclusive rights to databases of genetic and medical information for the country’s 270,00 citizens. However, this data has been little-used because it failed the basic ethical criterion of informed consent by Icelanders.
Consideration by other countries, including the UK, of 'biobanks' or population databases
Issuance in 1999 by the Biomedical Information Science and Technology Initiative (BITSI) of the US National Institutes of Health of a report stating that the NIH should create between 5 and 20 National Programs of Excellence in Biomedical Computing and should develop a national computer infrastructure
Opening of research facilities related to the State of California's new initiative, the California Institute for Quantitative Biomedical Research, beginning in 2005
Choice by the Public Library of Science, a new open-access publisher of scientific and medical research, of Computational Biology to be its third journal and publication of the first issue in June 2005

What to Watch:

New publicly and privately funded centres for biomedical computing open.
Life science research in universities reorganizes under the banner computational biology and bioinformatics.
Debates are waged over the merits of computer models verus clinical trials in providing evidence of toxicity or pharmacological efficacy.
A computer of model of a cell, perhaps a liver cell, is developed.

Parallels/Precedents:

The emergence of molecular biology as a separate field of biology
The close association of physics and mathematics, especially in developing theoretical models
The use of computer science to model and visualise the interaction of molecules

Enablers/drivers:

Training of a new generation of scientists in computer science and biology
Continued investment by governments seeking to remain competitive in scientific research, especially biomedicine
New collaborations between the pharmaceutical industry and the biotechnology industry

Leaders:
Regions:

US, UK, Australia

Institutions:

Roche Pharmaceuticals (collaboration to produce some of the first applications from these approaches)
Other major pharmaceutical companies such as GlaxoSmithKline, Astra Zeneca, Pfizer, Merck, and Johnson & Johnson (investment in new drug development projects)
Many smaller companies such as Gene Network Sciences, Enelos, and Rosetta Biosoftware (various specialisations in aids for drug development and clinical design)
Oak Ridge National Laboratory [link]
Cambridge Computational Biology Institute [link]
IT companies including IBM [link]
Aberystwyth University [link]
University of Leeds [link]
Rice University, W M Keck Centre [link]
National Institute for Advanced Industrial Science and Technology, Japan [link]

Figures:

Sources:

Bourne, Phillip E., Steven E. Brenner, and Michael Eisen. "Plos Computational Biology: A New Community Journal." PloS Computational Biology 1, no. 1 (2005): 0001.
Eddy, Sean R. "'Antedisciplinary' Science." PloS Computational Biology 1, no. 1 (2005): 0003-0004.
Carmichael, Mary. "The Shape of Things to Come." Newsweek Special Edition: The Future of Medicine, Summer 2005, 40-45.
Golan, David. "Building Better Medicines." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 37-39.
"Models That Take Drugs." The Economist, June 11 2005, Suppl. 23-24.
Tyre, Peg. "An Rx for Kids - with warnings." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 74-75.
Underwood, Anne, and Karen Springen. "Medicine Tailored Just for You." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 76-83
Branca, Malorye. "The New New Pharmacogenomics." Bio-IT World, 9 Sept 2002. [link].
"International Society for Computational Biology" International Society for Computational Biology [link]
"California Institute for Quantitative Biomedical Research" California Institute for Quantitative Biomedical Research. www.qb3.org.
"Human Genome Project" Human Genome Project [link]
"National Center for Biotechnology Information" National Center for Biotechnology Information [link]
Kaiser, J. "Biobanks. Population Databases Boom, from Iceland to the U.S." Science 298, no. 5596 (2002): 1158-61.
Interview with Martha Michel, 8 May 2005.
Interview with David Konerding, 10 June 2005.
Nature, Focus on Computational Biology, 14 November 2002 [link]
David A Bader, Computational Biology and High-Performance Computing, Communications of the ACM, 47, 11, 34-41, 2004 [link]
Denis Noble, The Rise of Computational Biology, Nat Rev Mol Cell Biol, 3, 6, 459-463, 2002
A Levchenko, Computational Cell Biology in the Post-Genomic Era, Mol Biol Rep 28, 2, 83-89 [link]

At A Glance:
When:
21–50 years +

Where:
Global

How Fast:
Years

Likelihood:
Medium

Impact:
Medium-Low

Controversy:
Medium

Related Outlooks:

About this outlook: An outlook is an internally consistent, plausible view of the future based on the best expertise available. It is not a prediction of the future. The AT-A-GLANCE ratings suggest the scope, scale, and uncertainty associated with this outlook. Each outlook is also a working document, with contributors adding comments and edits to improve the forecast over time. Please see the revision history for earlier versions.

Category:	Science and Technology
Domain:	Biology and Biotechnology, Computer Science
Keywords:	Pharma - biosimulation, computer science, molecular biology, genomics, drug development, pharmacogenomics, computational biology, bioinformatics
Outlook:	The tools of computational biology may be applied at an increasing rate to pharmaceutical innovation in the next 20 to 50 years, resulting in a faster, less costly, and more tailored approach to drug development.
Summary Analysis:	Computer science and molecular biology have made some of the most significant contributions to science over the past 20 years, and computational biology (also known as bioinformatics) seeks to organize the multitude of activities that are emerging from new collaborations between the two fields. Computational biology makes use of advances in computing power, modelling, visualisation, genomics, protein chemistry, and information science, among others, to find relationships among biomarkers, genetics, pharmaceutical responses, normal responses, and diseases. For example, a computational biologist might search the human genome for particular patterns, analyse gene expression data for biologically relevant molecules, or develop models for visualising the interaction of DNA with other molecules. A loosely shared goal of computational biology is to bring the predictive power of mathematics and computer modelling to modern molecular biology and reign in the enormous amount of information produced by genomic sequencing. Computational biology is showing preliminary signs of successful of applications. The applied subfields generating some of the greatest interest because of their potential impact on biomedicine are biosimulation and pharmacogenomics, and further research progress will come in these areas in the next 3 to 10 years. Biosimulation is the computer modelling of biological processes and has the character of what some have called a 'laptop lab'. One hope is to use knowledge of the human genome and pharmaceutical chemistry to design new or more effective drugs that could then be 'tested' in computer models before attempting costly clinical trials, although this potential development is still years away. Pharmacogenomics is the science of inherited variations in drug responses and promises better biomedicine through a personalized approach. The idea is that a patient’s genome could be profiled to predict in advance the effectiveness of a particular drug or treatment. One of the few instances in which this approach has been demonstrated is with the cytochrome P450 (CYP) family of liver enzymes, which are involved in the metabolism of more than 30 different classes of drugs. Genetics tests have been developed to screen for variations and avoid drug overdoses. Another enzyme, thiopurine methyltransferase, has been shown to negatively influence chemotherapy treatments for childhood leukaemia in the rare patient who has a defective variant. A new industry has developed around applications of computational biology in the last decade. Initial hopes have been tempered, however, and ethical concerns about privacy and property rights to genetic information have arisen. Nonetheless, many new computer applications to aid the drug development process are expected in the next decade. The larger goal of creating a fully predictive biomedicine with tailored treatments is still 20 to 50 years out.

	Implications:	Advances in the modeling and visualisation of molecular biology Reorganization of university research under the categories of computational biology and bioinformatics Increased university and industry collaborative research centers Improved dosing and efficacy of drugs for individuals; improved diagnostics and screening, leading to fewer adverse drug reactions Reduced research and development costs and shortened development schedules for new drugs Emergence of a new approach to drug discovery, where 'designer drugs' are deduced from the genetic code itself Emergence of new ethical (privacy, property rights) concerns about databases of individual genetic records
	Early Indicators:	Passage by Iceland in 1998 of the Health Sector Database Act, giving the DeCode exclusive rights to databases of genetic and medical information for the country’s 270,00 citizens. However, this data has been little-used because it failed the basic ethical criterion of informed consent by Icelanders. Consideration by other countries, including the UK, of 'biobanks' or population databases Issuance in 1999 by the Biomedical Information Science and Technology Initiative (BITSI) of the US National Institutes of Health of a report stating that the NIH should create between 5 and 20 National Programs of Excellence in Biomedical Computing and should develop a national computer infrastructure Opening of research facilities related to the State of California's new initiative, the California Institute for Quantitative Biomedical Research, beginning in 2005 Choice by the Public Library of Science, a new open-access publisher of scientific and medical research, of Computational Biology to be its third journal and publication of the first issue in June 2005
	What to Watch:	New publicly and privately funded centres for biomedical computing open. Life science research in universities reorganizes under the banner computational biology and bioinformatics. Debates are waged over the merits of computer models verus clinical trials in providing evidence of toxicity or pharmacological efficacy. A computer of model of a cell, perhaps a liver cell, is developed.
	Parallels/Precedents:	The emergence of molecular biology as a separate field of biology The close association of physics and mathematics, especially in developing theoretical models The use of computer science to model and visualise the interaction of molecules
	Enablers/drivers:	Training of a new generation of scientists in computer science and biology Continued investment by governments seeking to remain competitive in scientific research, especially biomedicine New collaborations between the pharmaceutical industry and the biotechnology industry
	Leaders:	Regions: US, UK, Australia Institutions: Roche Pharmaceuticals (collaboration to produce some of the first applications from these approaches) Other major pharmaceutical companies such as GlaxoSmithKline, Astra Zeneca, Pfizer, Merck, and Johnson & Johnson (investment in new drug development projects) Many smaller companies such as Gene Network Sciences, Enelos, and Rosetta Biosoftware (various specialisations in aids for drug development and clinical design) Oak Ridge National Laboratory [link] Cambridge Computational Biology Institute [link] IT companies including IBM [link] Aberystwyth University [link] University of Leeds [link] Rice University, W M Keck Centre [link] National Institute for Advanced Industrial Science and Technology, Japan [link]
	Figures:
	Sources:	Bourne, Phillip E., Steven E. Brenner, and Michael Eisen. "Plos Computational Biology: A New Community Journal." PloS Computational Biology 1, no. 1 (2005): 0001. Eddy, Sean R. "'Antedisciplinary' Science." PloS Computational Biology 1, no. 1 (2005): 0003-0004. Carmichael, Mary. "The Shape of Things to Come." Newsweek Special Edition: The Future of Medicine, Summer 2005, 40-45. Golan, David. "Building Better Medicines." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 37-39. "Models That Take Drugs." The Economist, June 11 2005, Suppl. 23-24. Tyre, Peg. "An Rx for Kids - with warnings." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 74-75. Underwood, Anne, and Karen Springen. "Medicine Tailored Just for You." Newsweek. Special Edition: The Future of Medicine, Summer 2005, 76-83 Branca, Malorye. "The New New Pharmacogenomics." Bio-IT World, 9 Sept 2002. [link]. "International Society for Computational Biology" International Society for Computational Biology [link] "California Institute for Quantitative Biomedical Research" California Institute for Quantitative Biomedical Research. www.qb3.org. "Human Genome Project" Human Genome Project [link] "National Center for Biotechnology Information" National Center for Biotechnology Information [link] Kaiser, J. "Biobanks. Population Databases Boom, from Iceland to the U.S." Science 298, no. 5596 (2002): 1158-61. Interview with Martha Michel, 8 May 2005. Interview with David Konerding, 10 June 2005. Nature, Focus on Computational Biology, 14 November 2002 [link] David A Bader, Computational Biology and High-Performance Computing, Communications of the ACM, 47, 11, 34-41, 2004 [link] Denis Noble, The Rise of Computational Biology, Nat Rev Mol Cell Biol, 3, 6, 459-463, 2002 A Levchenko, Computational Cell Biology in the Post-Genomic Era, Mol Biol Rep 28, 2, 83-89 [link]

At A Glance:	When:	21–50 years +
	Where:	Global
	How Fast:	Years
	Likelihood:	Medium
	Impact:	Medium-Low
	Controversy:	Medium