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A Graph Based Approach to Analyse Metabolic Networks for Strain Engineering

Recently, there has been great interest in the field of metabolic engineering. Engineered microorganisms such as bacteria can be exploited to synthesize value-added bioproducts. Conversely, optimal genetic manipulations to reach to operative engineered cells is very costly; hence, regarding whole-cell functionalities, a number of proficient simulation algorithms have been developed and widely applied for systems-level analyses, and as a consequence, systems metabolic engineering. However, most of the currently-used in silico algorithms are usually time-consuming. In other words, it is requisite that computer scientists gain prior knowledge of molecular biology and biochemistry to be able to develop more effective algorithms in this field. Here, in the current study, we regard the metabolic network of interest as a general graph to present algorithmic perspective of the complex metabolic systems. A cell's metabolism is modeled in the form of a metabolic network. This way, the major problems in the field of metabolic engineering can be tackled using graph theory and graph mining algorithms. For this purpose, we reconstruct the metabolic network as a general graph, redefine the metabolic engineering problems accordingly, and finally, solve one of them as a sample.