A hybrid of ant colony optimization and flux variability analysis to improve the production of l-phenylalanine and biohydrogen

In silico metabolic engineering has shown many successful results in genome - scale model reconstruction and modification of metabolic network by implementing reaction deletion strategies to improve microbial strain such as production yield and growth rate. While improving the metabolites production, optimization algorithm has been implemented gradually in previous studies to identify the near - optimal sets of reaction knockout to obtain the best results. However, previous works implemented other algorithms that differ than this study which faced with several issues such as premature convergence and able to only produce low production yield because of ineffective algorithm and existence of complex metabolic data. The lack of effective genome models is because of the presence thousands of reactions in the metabolic network caused complex and high dimensional data size that contains competing pathway of non - desirable product. Indeed, the suitable population size and knockout number for this new algorithm have been tested previously. This study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux variability analysis (ACOFVA) to predict near - optimal sets of reactions knockout in an effort to improve the growth rates and the production rate of L - phenylalanine and biohydrogen in Saccharomyces cerevisiae and cyanobacteria Synechocystis sp PCC6803 respectively

In silico guided metabolic engineering of Aspergillus niger for sustainable organic acid production

The filamentous fungus Aspergillus niger is a common mould, and has risen to worldwide impact through industrial fermentation that is the chief source of the world’s citric acid. As well as the high market value chemical citric acid, A. niger fermentation supplies an array of enzymes of biotechnological importance. Industrial fermentation processes continue to be dependent on sucrose-based feed-stocks, and rising energy costs and tightening resources are prompting a shift to more sustainable methods. A. niger fermentation is also a promising platform for the sustainable production of many chemicals that could help solve coming world challenges. A. niger has within it the metabolic potential to achieve these goals, but these can only be realised with more efficient strain development techniques. This project built on the recent systems biology studies of A. niger to create in silico tools that guide rational engineering strategies. A dynamic metabolic model, relevant to the industrial setting of batch fermentation, of A. niger organic acid production was developed and shown to accurately capture physiological characteristics. An empirical characterisation of organic acid fermentation by the wild-type ATCC1015 strain was used to inform the model, and revealed new findings. The onset of citric acid production coincided with a shift to phosphate-limited growth, caused by a rapid phosphate uptake and storage of phosphate as polyphosphate. The role of polyphosphate in organic acid fermentation has not previously been described. The dynamic model was probed by the engineering of seven targets. To better inform further engineering, the genome-scale metabolic network of A. niger was updated and made specific to the ATCC1015 strain, the parent of citric acid producing strains. Finally, a genetic algorithm was developed for in silico evolution of A. niger organic acid production, and applied to suggest strategies for optimising the production of different organic acids