Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the -shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.

中文翻译：

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使用k-最短路径算法对恶性疟原虫代谢途径的计算鉴定。

疟疾病原体恶性疟原虫已显示出对治疗的抵抗力以及对某些疫苗的不良反应，因此需要采取紧急，整体和广泛的方法来预防这种地方病。了解疟疾寄生虫的生物学已被认为是克服疟疾威胁的重要方法。这项研究旨在使用恶性疟原虫3D7菌株的重建iPfa基因组规模代谢模型（GEM）鉴定疟原虫特有的必需蛋白通过使用从MetaCyc数据库获得的十九（19）种代谢产物和二十三（23）种反应填补模型中的空白。从网络中删除了二十（20）个货币代谢物，因为已确定它们会产生生物学上不可行的捷径。产生的修饰iPfa GEM是使用-最短路径算法的模型，用于识别恶性疟原虫的糖酵解和磷酸戊糖途径中可能的替代代谢途径。引入启发式函数以实现算法的最佳性能。为了验证预测结果，使用中间性中心度评估评估了重构网络中反应的必要性，该中间性测度适用于本研究中考虑的途径内的每个反应。预测了三十二（32）个基本反应，其中我们的方法验证了文献中已经预测的十四（14）个酶。检查了催化这些基本反应的酶蛋白与宿主基因组的同源性，两（2）例相似性不显着，使其成为可能的药物靶标。总之，智能搜索技术在恶性疟原虫代谢网络中的应用 使用基于图论的方法预测潜在的生物学相关替代途径。