Industrial or white biotechnology refers to the harnessing of cells and enzymes to make products of interest (that may be the enzymes themselves), usually using microorganisms as the hosts. It is an important and growing sector for BBSRC, both in terms of basic biology and in ‘green’ application areas such as biofuels and biochemicals, since one may anticipate an increasing contribution of biotechnology to the industrial production of chemicals as we move away from petrochemical feedstocks. Creating an improving a bioprocess is a combinatorial optimisation problem at every step of the way, since even from a genomic point of view the number of possible sequences is enormous. I blogged before about this from the points of view of aptamer optimisation and of increasing expression levels of proteins in E. coli.
The sequence spaces of even modestly sized proteins are equally enormous. Thus, the number of sequence variants for M substitutions in a given protein of N amino acids is (19M.N!)/((N-M)!M!). For a protein of 300 amino acids with changes in just 1, 2 and 3 amino acids this is 5700, ca 16 million and ca 30 billion. Even for a tiny protein of N=50 amino acids, the number of variants exceeds 1012 when M=10. Methods of directed evolution are widely used to search these enormous search spaces, in ways that can be modelled, so as to evolve variants with desirable properties (thus this is a multiobjective combinatorial optimisation problem).
Evolving organisms involves optimising networks of reactions, and this too is a combinatorial optimisation problem. Thus, if a particular metabolic flux can be improved by modifying just 3 genes out of a 1000 (a reasonable estimate – see e.g. a nice example from Sang-yup Lee and colleagues), but we do not know which 3, the number of possibilities is ~1.66.108. Clearly this is a hard number to assess experimentally, but a perfectly tractable number in silico given a genome-scale model of the organism’s metabolic network. None of this is rocket science, and simply requires us to exploit known methods of systems biology, part of BBSRC’s 10-year vision. However, the UK is not yet a leader in this field, and we will welcome suggestions as to what to do about that.
According to a recent article in The Economist, the market for white biotechnology is some £147Bn, and the Danish company Novozymes has some 47% of that for industrial enzymes. Processes for molecules as simple as succinic and acrylic acids are now becoming economic, and those for shikimic acid more so since it is a major substrate for the synthesis of oseltamivir (Tamiflu). The article considers that “converting agricultural waste into other chemicals (including fuels) using industrial biotechnology could replace 20-25% of global oil consumption”, and the use of such feedstocks is a major part of BBSRC’s strategy and of the activities of our Sustainable Bioenergy Centre. Another clear niche for the UK, and for BBSRC, is the production of biopharmaceuticals (‘biologicals’), as these are widely anticipated to take a considerably greater share of the drug and biotechnology market than the small molecules currently prevalent in the former. Substances other than proteins, such as nucleic acids, bioactive carbohydrates and whole cells, are likely to be part of the mix. As recognised by the Industrial Biotechnology Innovation and Growth Team (IB-IGT), the UK needs to be gearing up our technological capabilities now.
Finally, while scanning the scientific literature, I was pleased to see that my recent review on iron metabolism in health and disease had become the most accessed publication of all time at the Open Access journal BMC Medical Genomics. There was also a very useful review on swine flu in this week’s Nature that set out with commendable clarity the state of our knowledge of the roles of each of the 8 influenza A virus gene products. Treatment options for any possible pandemic later in the year remain uncertain, and Fedson summarises important and intriguing data that a variety of generic drugs that have anti-inflammatory ‘off-target’ effects might be useful. Nature also made the important announcement that they have altered their license conditions to allow anyone with access to full text articles of manuscripts to use them as they see fit for the academic purposes of data mining, text mining, literature-based knowledge discovery and so forth. “The re-use permissions apply to author manuscripts, of articles published in NPG’s journals, which have been archived in PubMed Central, UK PubMed Central (UKPMC) and other institutional and subject repositories.” This is still some way from full and unfettered Open Access (the ‘Gold Road’), but it is a definite step in the right direction. Other publishers: please take note.
- Fedson, D. S. (2008). Confronting an influenza pandemic with inexpensive generic agents: can it be done? Lancet Infect Dis 8, 571-6
- Handl, J., Kell, D. B. & Knowles, J. (2007). Multiobjective optimization in bioinformatics and computational biology. IEEE Trans Comput Biol Bioinformatics 4, 279-292
- Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P., Liebermeister, W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasić, I., Weichart, D., Brent, R., Broomhead, D. S., Westerhoff, H. V., Kırdar, B., Penttilä, M., Klipp, E., Palsson, B. Ø., Sauer, U., Oliver, S. G., Mendes, P., Nielsen, J. & Kell, D. B. (2008). A consensus yeast metabolic network obtained from a community approach to systems biology. Nature Biotechnol. 26, 1155-1160
- Hull, D., Pettifer, S. R. & Kell, D. B. (2008). Defrosting the digital library: bibliographic tools for the next generation web. PLoS Comput Biol 4, e1000204. doi:10.1371/journal.pcbi.1000204. Free full text
- Kell, D. B. (2009). Iron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseases. BMC Medical Genomics 2, 2
- Kostoff, R. N., Briggs, M. B., Solka, J. L. & Rushenberg, R. L. (2008). Literature-related discovery (LRD): Methodology. Technol. Forecast. Soc. Change, doi:10.1016/j.techfore.2007.11.010
- Moore, J. C., Jin, H. M., Kuchner, O. & Arnold, F. H. (1997). Strategies for the in vitro evolution of protein function: Enzyme evolution by random recombination of improved sequences. J. Mol. Biol. 272, 336-347
- Neumann, G., Noda, T. & Kawaoka, Y. (2009). Emergence and pandemic potential of swine-origin H1N1 influenza virus. Nature 459,931-939
- Park, J. H., Lee, K. H., Kim, T. Y. & Lee, S. Y. (2007). Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc Natl Acad Sci U S A 104, 7797-802
- Wedge, D., Rowe, W., Kell, D. B. & Knowles, J. (2009). In silico modelling of directed evolution: implications for experimental design and stepwise evolution. J Theor Biol 257, 131-141
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