It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates. The composition of these MMNs has defined a new functional class of genes termed Network Efficiency Determinants (NEDs). These genes, whilst not essential, are very rarely eliminated in constructing an MMN, suggesting that it is difficult for metabolism to be re-routed to obviate the need for such genes. Moreover, the removal of NED genes from an MMN significantly reduces its global efficiency. Bioinformatic analyses of the NED genes have revealed that not only do these genes have more genetic interactions than the bulk of metabolic genes but their protein products also show more protein-protein interactions. In yeast, NED genes are predominantly single-copy and are highly conserved across evolutionarily distant organisms. These features confirm the importance of the NED genes to the metabolic network, including why they are so rarely excluded during minimisation.
The construction of minimal metabolic networks contributes to our understanding of metabolism and biotechnological processes. Here, Jansen et al. have defined a class of genes that must be retained to ensure cell viability and rapid growth.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details







1 University of Cambridge, Department of Biochemistry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934); University of Catania, Department of Biomedical & Biotechnological Sciences, Catania, Italy (GRID:grid.8158.4) (ISNI:0000 0004 1757 1969)
2 University of Manchester, Manchester Institute of Biotechnology, Manchester, UK (GRID:grid.5379.8) (ISNI:0000 0001 2166 2407)
3 Queen Mary University of London, School of Mathematical Sciences, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); University of Catania, Department of Physics and I.N.F.N., Catania, Italy (GRID:grid.8158.4) (ISNI:0000 0004 1757 1969)
4 University of Thessaly, Bioinformatics Laboratory, Department of Biochemistry & Biotechnology, Thessaly, Greece (GRID:grid.410558.d) (ISNI:0000 0001 0035 6670)
5 University of Cambridge, Department of Biochemistry, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000 0001 2188 5934)