Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyte

Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyte

This model is described in the article:
Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism.

Bazzani S, Hoppe A, Holzhütter HG.
BMC Syst Biol.
2012 Aug 31;6(1):118. PMID: 2937810

Abstract:
ABSTRACT:

BACKGROUND: The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite's essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential.
RESULTS:

Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations.
CONCLUSIONS:

Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug.

This model is hosted on BioModels Database
and identified by: MODEL1206070000
.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024
.

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Bazzani2012 —— 恶性疟原虫（Plasmodium falciparum）与人类肝细胞（human hepatocyte）的基因组规模网络

本模型载于以下研究论文：
《基于网络评估恶性疟原虫代谢药物靶点相对于人类肝脏代谢的选择性》
作者：Bazzani S、Hoppe A、Holzhütter HG
发表于《BMC系统生物学》（BMC Syst Biol），2012年8月31日；6(1):118。PubMed识别号（PMID）：2937810

摘要：

研究背景：恶性疟原虫是引发人类死亡的主要病原体之一，随着多重耐药菌株的快速扩散，寻找新型抗疟抗生素药物靶点已成为亟待解决的科学议题。基于代谢网络的分析方法，可助力筛选出寄生虫必需，但人类宿主同源蛋白缺失、非必需或相对低必需的酶类靶点。

研究结果：
本研究利用经过精心注释与审核的恶性疟原虫代谢网络PlasmoNet与人类肝细胞代谢网络HepatoNet1，对48个实验性抗疟药物靶点的选择性展开分析。通过虚拟基因敲除实验，本研究发现其中24个靶点具备完全选择性：即对寄生虫必需，但对人类细胞非必需。对于在两个模型中均为必需的酶类子集，本研究采用适应度降低概念对其选择性进行评估，进而可量化相同酶活性被逐步抑制时，两个网络的功能适应度下降幅度。总体而言，本项虚拟分析得到的选择性排名与大量体内、体外实验结果相符。

研究结论：
基因组规模模型可用于描述并量化酶抑制对生物质组分合成受损的影响。从宿主-病原体代谢互作的视角出发，评估药物靶点引发的效应，有助于开发具有选择性的抗寄生虫药物。

本模型存储于BioModels数据库（BioModels Database），其标识符为MODEL1206070000。
引用BioModels数据库请使用：BioModels数据库：面向已发表定量动力学模型的增强型、经人工审核并注释的资源。PubMed识别号（PMID）：20587024。

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