Producing Propane from E. Coli
In an attempt to develop and produce new renewable biofuels, researchers from the University of Turku and Imperial College have found a way to make Escherichia coli (E. coli) produce propane through modifying its metabolism. These researchers created a synthetic metabolic pathway in E. Coli B21 (DE3) by altering a natural biological process that turns fatty acids into cell membranes within the bacteria and making it produce propane instead. This involved using 3 different enzyme to turn the fatty acids into propane rather than allowing the bacteria to make cell walls. One enzyme targets the fatty acids and separates them from the bacteria’s original metabolic pathway. Another enzyme convert butyric acid in the bacteria into butyraldehyde. The last enzyme, which was discovered by these researchers and was extremely important in producing propane, converts the butyraldehyde and the fatty acids into hydrocarbons that include propane. The amazing thing about this process is that the propane that is produced is completely ready to be used as fuel as soon as it is created, which means that refinement of fuel and other costly processes can be avoided. The ultimate goal for these scientists is to eventually insert this propane production system into photosynthetic bacteria to cheaply create chemical fuel that is engine-ready without any required refining or processing.
Graph theory can be used to model the metabolic pathways of all living organisms. For these researchers, the best application of graph theory would be to model the metabolic network of E. Coli B21 (DE3). As metabolism is governed by a series of interconnected and interdependent sets of chemical reactions and enzymes, it is natural that the nodes in this network would be the individual reactions and enzymes involved in metabolism and the edges would represent the order and interdependence of reactions. To incorporate strength of ties, strong ties can be given to edges that are not easily broken (usually signifying multiple lines of defense in the bacteria’s metabolism) and weak ties can be given to edges that are more easily broken (usual target for researchers). Synthetic pathways, such as the ones made by the researchers in this article, can also be mapped alongside the unaltered network. After finishing a model, researchers can pinpoint exactly which nodes they wish to alter and can begin working toward doing so. This would involve experimentation beyond basic graph theory, but has led to amazing innovations.
This article shows the potential power behind using graph theory in the field of microbiology. The use of graphs and large networks in biology and chemistry are actually quite common. Metabolic network modelling is the first step in performing research, designing experiments, and testing validity of theory behind experimentation. Generally, these networks lead to mathematical modeling of important physical phenomena. This would allow scientists and engineers to perform analysis, such as flux balance analysis, to reach an end goal. (An example of an established, complex metabolic network of E. Coli B21 (DE3) from can be found at this Figure link below.)
Figure (Modeled metabolic pathway for E. Coli B21 (DE3): http://www.genome.jp/kegg-bin/show_pathway?ebl01100
Research Paper: http://www.nature.com/ncomms/2014/140902/ncomms5731/full/ncomms5731.html