Professor Desmond Lun, Mr Kuhn Ip
The overall aim of this research is to develop and extend the mathematical and computational techniques available for modelling, analysing and engineering biological organisms. The extensive data sets for characterising biological entities generated by modern technologies require such tools to enhance our understanding of their capabilities and our ability to harness their potential for novel applications.
J. J. Kelley, A. Lane, X. Li, B. Mutthoju, S. Maor, D. Egen, D. S. Lun (2015) MOST: A software environment for constraint-based metabolic modeling and strain design. Bioinformatics, 31(4):610-611.
O. Levitan, J. Dinamarca, E. Zelzion, D. S. Lun, L. Tiago Guerra, M. Kyung Kim, J. Kim, B.A.S. Van Mooy, D. Bhattacharya, P.G. Falkowski (2015) Remodeling of intermediate metabolism in the diatom Phaeodactylum tricornutum under nitrogen stress, PNAS, 112 (2) 412-417.
Making Fuel-Producing Microbes
The production of cheap, clean, renewable energy is one of the world's most pressing problems. And microbes are a potential solution.
Microbes, such as blue-green algae, are capable of taking solar energy and storing it as a chemical fuel, thus allowing us to make use of the solar energy that continually bathes our planet in 10,000-fold abundance to our consumption. In contrast to the traditional solution of photovoltaic cells, solar microbial biofuel does not require expensive batteries for energy storage and, since microbes self-replicate, the capturing apparatus itself is potentially cheaper. Unfortunately, naturally-occurring microbes are not optimised for biofuel production from solar energy and must be engineered for this purpose.
We aim to engineer microbes that are useful for biofuel production using an approach that is grounded in mathematical modelling and computational design. We aim to engineer, in particular, a blue-green algae that converts solar energy and carbon dioxide into petroleum. Our activities include using and developing flux-balance metabolic models and their extensions, developing efficient algorithms for computational design and optimisation, and constructing engineered strains.
- George Church, Harvard Medical School
- Caroline Colijn, University of Bristol
- Nicholas Guido, Harvard Medical School
- Graham Rockwell, Harvard Medical School