Biotechnology - Genomic and Proteomics

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Field definition

Basic Definition

Simply defined, biotechnology is any technology that relies on living organisms or biological systems. By this definition, human beings have been using biotechnology for thousands of years to produce food products, textiles and other necessary items. Several familiar items -- including yeast-rising bread, yogurt, cheese, wine, beer and vinegar -- are all produced with the help of cultured microorganisms.

In recent years, however, the term "biotechnology" has come to mean the use of genetic engineering and its associated techniques. This more common definition is found in a variety of applications, from medicine to agriculture.

Human genomics is of critical importance to health and welfare, used by firms as the foundation for innovation in many applications, from enviromental and medical to industrial and agriculture products.

The US biotechnology industry includes about 1,000 companies, with combined annual revenue close to $50 billion. Large companies include Amgen, Monsanto, Genentech, and Biogen. The biotechnology and the pharmaceutical industries overlap considerably, since many drugs are now developed using biotechnology. The industry consists of a few very large companies and many very small ones, and is fragmented by type of product. Most companies have annual sales under $50 million.

Industry overview here: Biotechnology Industry Organization

Key Industry Statistics

Key Industry Figures 2008

  • Industry Revenue: 85,695.4$Mil
  • Revenue Growth: 12.9%
  • Industry Gross Product: 58,539.2 $Mil
  • Number of Establishments: 6,815 Units
  • Number of Enterprises: 6,480 Units
  • Employment: 341,000 Units
  • Exports: 7,251$Mil
  • Imports: 2,954.7$Mil
  • Total Wages: *20,630.9$Mil

Source: Ibis World

Focus Market Segments

Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena and other interactions between loci and alleles within the genome and the techniques of sequencing, genome mapping, data storage, and bioinformatic analyses. The wide range of genomics outputs means that the entire range of intellectual property rights come into play at some point in the cycle. Roughly speaking we can divide the knowledge products into narratives, data, and inventions, and use these as a tool to divide up the field for our case study.

From initial R&D to the moment when a target product is developed for licensing to Pharmaceutical or Agricultural companies, we will study the following:

  • Data production: Genomic or proteomic sequences
  • Narrative production: Journals focused on biotechnology and related disciplines; publications of data; descriptions of tools or patents, or commentaries on them
  • Tool production: processes for producing data or physical products


The science of genomics is focused on the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine‐scale genetic mapping efforts to determine the activity and function of genes. Genomic scientists produce genetic, pathway, and functional information analysis about the genome in attempts to place the DNA code in context inside living beings, typically in order to improve human health or advance agricultural technology.
The first techniques that can be related to genomics were the development of sequencing, genome mapping, data storage, and bioinformatics analyses in the 1970‐1980s. This period also marks the birth of the first biotechnology industries in the USA. Amgen and Genentech are the oldest examples still in the market and are models for other startups in terms of business models. The high uncertainty and asymmetry of information characteristic of the biotechnology industry transformed the field, from a commercial perspective into an archipelago of high‐specialized islands of knowledge. Collaborations inside the industry have since that time been characterized by the “big deal” involving a complex transfer of IP, future royalties, data, investment, and employee sharing. Such collaborations are rarely open to outside parties and are themselves asymmetrical in their treatment of property. GenomicsIP.jpg
PD=Public Domains; C = Copyright; P = Patents; TS = Trade Secret; Con = Contracts, including Material Transfer Agreements; N = Community construed norms.


In terms of data and narrative outputs, proteomics is very similar. There is fundamental and observational data, though there is no “human proteome project” like the HGP to serve as an aggregating actor for commons based efforts. There are many smaller efforts that we can study including databases in structural genomics and protein data.
For protein tools, antibodies are the biggest category. We can classify all sorts of antibodies for specific study like cytokines, neurotrophins, etc. There is also a growing system for protein expression like gene expression that depends on antibodies, but also now can use all sorts of genomic tools. So the genomic tools are now becoming proteomic tools as well. Also, access to the same stem cells and mice is essential if the research is going to translate to cures. It will be interesting to look and see if the same desire for treating the outputs of research as inputs to new research we saw in the fundamental genomic data space apply here.
Some other kinds of protein tech would include high throughput screening array technology (the robots that test drugs against proteins) and software tools: structure prediction, identification, properties, alignment. Proteomics research is very intensive in terms of computation and software (much more complex than genomics – more similar in some ways to climate change and weather modeling in terms of complexity).
We should probably expect to discuss the impact of patents as biomarker / diagnostic marker. Gene patents haven't had the expected impact of anticommons, but protein patents are extremely valuable and frequently enforced.
PD=Public Domains; C = Copyright; P = Patents; TS = Trade Secret; Con = Contracts, including Material Transfer Agreements; N = Community construed norms.

Deprioritized Market Segments

Tools such as software for biotechnology (bio-informatics) and other biological tools not genetically modified.

Excluded From Field Definition

We will not study data, process, or tool production once a target product is licensed to pharmaceutical or agricultural products

Study of the field

Analysis of the field with basis on ICP Main Questions

  1. Overview of Economics of Intellectual Property in BGP
  2. Give an overall picture of the BGP field
  3. Outputs and Products of the field: data, narratives and tools produced by the BGP field
  4. Legal tools available for and in use by the actors of BGP field: IP in BGP
  5. competitive advantages in BGP
  6. IP Profile of Biggest for-profit companies in BGP
  7. IP Profile of non-profit companies in BGP
  8. IP Profile of Universities working in BGP
  9. IP Profile of Associations in BGP
  10. Commons based cases in BGP
  11. Peer-Production Models in BGP
  12. Open Business models in BGP

Special Cases in BGP

Under this section we will explore possible special case studies that will be later trasformed in papers under BGP Field Intellectual Property Profile.


Diagnostic Kits


Biotech and Energy


Bibliography by Research Question



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