基因组规模代谢网络模型构建及其应用

微生物制造产业的发展迫切需要进一步提高认识、设计和改造微生物细胞代谢的能力,以推动工业生物技术快速发展。随着微生物全基因组序列等高通量数据的不断积聚和生物信息学策略的持续涌现,使全局性、系统化地解析、设计、调控微生物生理代谢功能成为可能。而基于基因组序列注释和详细生化信息整合的基因组规模代谢网络模型(GSMM)构建为全局理解和理性调控微生物生理代谢功能提供了最佳平台。以下在详述GSMM的应用基础上,描述了如何构建一个高精确度的GSMM,并展望了未来的发展方向。     

基于约束的基因组规模代谢网络模型构建方法研究进展

基因组规模代谢网络模型(Genome-scale metabolic network model,GSMM)正成为细胞代谢特性研究的重要工具,经过多年发展相关理论方法取得了诸多进展.近年来,在基础GSMM模型基础上,通过整合基因组、转录组、蛋白组和热力学数据,实现基于各种约束的GSMM构建,在基因靶点识别、系统代谢工程...     

基于热力学原理约束的代谢网络模型研究进展及其应用

高通量组学技术为人们研究生命系统组件提供了细节数据,通过对基因组、转录组、蛋白质组及代谢组等不同生命层级间相互作用的研究,推动了生化反应网络的重建——基因组规模代谢网络模型(genome scale of metabolic network model,GSMM).GSMM作为系统生物学领域中研究生命系统的基本手段,表...     

工业微生物代谢网络模型的研究进展及应用

基因组规模代谢网络(Genome-scale metabolic network model,GSMM)是工业微生物菌株定向改造过程中一种极为重要的指导性工具,有助于研究者快速获取特定性状的工业微生物,因此越来越受到人们的关注。文中回顾了GSMM的发展历程,总结并评述了GSMM的构建方法,以4种重要工业微生物(枯草芽孢杆菌Bacillus subtilis、大肠杆菌Escherichia coli、谷氨酸棒杆菌Corynebacterium glutamicum和酿酒酵母Saccharomyces cerevisiae)为例,阐述了GSMM在工业微生物中的发展与应用。此外,还对GSMM未来的发展趋势进行了展望。     

应用代谢网络模型解析工业微生物胞内代谢

为了快速、高效地理解工业微生物胞内代谢特征,寻找潜在的代谢工程改造靶点,基因组规模代谢网络模型(GSMM)作为一种系统生物学工具越来越受到人们的关注。文中在回顾GSMM 20年发展历程的基础上,分析了当前GSMM的研究现状,总结了GSMM的构建及分析方法,从预测细胞表型和指导代谢工程两个方面阐述了GSMM在解析工业微生物胞内代谢中的应用,并展望了GSMM未来的发展趋势。     

多约束代谢网络模型的研究进展

基于约束的基因组尺度代谢网络模型(genome-scale metabolic models,GEMs)分析已被广泛应用于代谢表型的预测.而实际细胞中代谢速率除计量学约束外,还受到酶资源可用性和反应热力学可行性等其他因素影响,在GEMs中整合酶资源约束或者热力学约束构建多约束代谢网络模型可以进一步缩小优化解空间,提升细...     

乳酸乳球菌NZ9000基因组规模代谢网络模型的构建与验证

随着后基因组时代的到来,工业微生物的代谢工程改造在工业生产上发挥着越来越重要的作用。而基因组规模代谢网络模型(Genome-scalemetabolicmodel,GSMM)将生物体体内所有已知代谢信息进行整合,为全局理解生物体的代谢状态、理性指导代谢工程改造提供了最佳的平台。乳酸乳球菌NZ9000(Lactococcuslactis NZ9000)作为工业发酵领域的重要菌株之一,由于其遗传背景清晰且几乎不分泌蛋白,是基因工程改造和外源蛋白表达的理想模式菌株。文中基于基因组功能注释和比较基因组学构建了L.lactisNZ9000的首个基因组规模代谢网络模型iWK557,包含557个基因、668个代谢物、840个反应,并进一步在定性和定量两个层次验证了iWK557的准确性,以期为理性指导L. lactis NZ9000代谢工程改造提供良好工具。     

基于酶约束的代谢网络模型研究进展及其应用

基因组尺度代谢网络模型已经成功地应用于指导代谢工程改造,但由于传统通量平衡分析法仅考虑化学计量学和反应方向约束,模拟得到的是理论最优结果,对一些现象如代谢溢流、底物层级利用等无法准确描述。近年来人们通过在代谢网络模型中引入新的蛋白量、热力学等约束发展了新的约束优化计算方法,可以更准确真实地模拟细胞在不同条件下的代谢行为。文中主要对近年来提出的多种酶约束模型进行评述,对酶约束引入的基本思路、酶约束的数学方程表示及优化目标设定、引入酶约束后对代谢通量计算结果的影响及酶约束模型在代谢工程菌种改造中的应用等进行了全面深入的介绍,并提出了已有各种方法存在的主要问题,展望了相关方法的未来发展方向。通过引入新的约束,代谢网络模型能够更精确模拟和预测细胞在环境和基因扰动下的代谢行为,为代谢工程菌种改造提供更准确可靠的指导。     

基于Matlab对解脂耶氏酵母基因组规模代谢网络模型仿真结果的可视化分析

【目的】近十年来,基因组代谢网络模型迅速发展。通过构建基因组代谢网络模型进行计算机仿真模拟已成为研究生物体复杂的生理代谢不可或缺的工具。实现对仿真结果的可视化分析,可以直观地追踪模型中的代谢流向,从而更好地对仿真结果进行分析。【方法】在简要概述目前可视化方法的基础上,提出了一种基于Matlab实现基因组规模代谢网络模型仿真结果可视化的方法：通过CellDesigner预先绘制与模型相匹配的图,通过RAVEN toolbox中的函数于Matlab进行读图、并实现仿真结果的可视化。【结果】以解脂耶氏酵母基因组规模代谢网络模型iYL619_PCP v1.7为对象,实现并阐明其仿真结果的可视化。【结论】通过该方法可以清晰地监测模型中的流量和流向变化,提高仿真结果的分析效率。     

细胞生理代谢网络模型重构和优化的进展

细胞中自发或由酶催化的代谢反应组成了高度复杂的代谢网络,其与细胞生理代谢活动运作密切相关。细胞生理代谢网络模型的重构有助于从系统层面上解析基因型与生长表型之间的关联,为细胞生理代谢活动精准刻画与生物绿色制造等研究提供重要的计算生物学工具。本文系统介绍了全基因组尺度代谢网络模型(genome-scale metabolic models, GEMs)、动力学模型、酶约束代谢模型(enzyme-constrained genome-scale metabolic models, ecGEMs)等不同类型细胞生理代谢网络模型发展与应用的最新研究进展;同时还介绍了GEMs自动化构建研究进展以及条件特异性GEMs建模策略。人工智能技术为高精度细胞生理代谢网络模型构建提供了全新机遇,本文进一步总结了人工智能技术在动力学模型和酶约束模型构建等领域的应用。各类细胞生理代谢网络模型的高质量重构将为今后的定量合成生物学与系统生物学等研究提供强大计算支撑。     

Identification of anticancer drugs for hepatocellular carcinoma through personalized genome‐scale metabolic modeling

Genome‐scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype–phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task‐driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type‐specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty‐two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.     

全基因组范围代谢网络的构建和最小基因组研究

全基因组范围代谢网络（genome-scale metabolic network,GSMN）的构建是合成生物学研究的一个重要研究手段。通过整合各种组学数据和借助计算机进行模拟分析,将基因型与表型的关系进行定量关联,从而为从全局的角度探索和揭示生物代谢机制,进而对生物进行合理的重新设计和工程改造提供了有效的框架。该方法在最小基因组研究中也有着突出的优势,通过计算机辅助的基因组最小化模拟与分析,能够系统鉴定微生物基因组基因的必需性。迄今为止,已有近百个基因组范围的代谢网络发表,覆盖的生物包括原核生物、真核生物和古生生物,并广泛应用于医药、能源、环境、工业和农业等多个领域,展现出了广阔的应用前景。将对全基因组范围代谢网络构建的方法、应用,特别是其在最小基因组研究中的应用作简要的综述。     

Fifteen years of large scale metabolic modeling of yeast: developments and impacts

Since the first large-scale reconstruction of the Saccharomyces cerevisiae metabolic network 15 years ago the development of yeast metabolic models has progressed rapidly, resulting in no less than nine different yeast genome-scale metabolic models. Here we review the historical development of large-scale mathematical modeling of yeast metabolism and the growing scope and impact of applications of these models in four different areas: as guide for metabolic engineering and strain improvement, as a tool for biological interpretation and discovery, applications of novel computational framework and for evolutionary studies.     

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration

Over the last decade, the field of cancer metabolism has mainly focused on studying the role of tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first genome‐scale computational study of the metabolic underpinnings of cancer migration. We build genome‐scale metabolic models of the NCI‐60 cell lines that capture the Warburg effect (aerobic glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is found to be highly positively associated with cancer cell migration. We hence predicted that targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer migration. By testing the anti‐migratory effects of silencing such 17 top predicted genes in four breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly attenuate cell migration either in all or one cell line only, while having almost no effect on cell proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed in the ratio between experimentally measured ECAR and OCR levels following these perturbations. Inhibiting anti‐migratory targets is a promising future avenue in treating cancer since it may decrease cytotoxic‐related side effects that plague current anti‐proliferative treatments. Furthermore, it may reduce cytotoxic‐related clonal selection of more aggressive cancer cells and the likelihood of emerging resistance.     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

Insight into trichomonas vaginalis genome evolution through metabolic pathways comparison

Trichomonas vaginalis causes the trichomoniasis, in women and urethritis and prostate cancer in men. Its genome draft published by TIGR in 2007 presents many unusual genomic and biochemical features like, exceptionally large genome size, the presence of hydrogenosome, gene duplication, lateral gene transfer mechanism and the presence of miRNA. To understand some of genomic features we have performed a comparative analysis of metabolic pathways of the T. vaginalis with other 22 significant common organisms. Enzymes from the biochemical pathways of T. vaginalis and other selected organisms were retrieved from the KEGG metabolic pathway database. The metabolic pathways of T. vaginalis common in other selected organisms were identified. Total 101 enzymes present in different metabolic pathways of T. vaginalis were found to be orthologous by using BLASTP program against the selected organisms. Except two enzymes all identified orthologous enzymes were also identified as paralogous enzymes. Seventy-five of identified enzymes were also identified as essential for the survival of T. vaginalis, while 26 as non-essential. The identified essential enzymes also represent as good candidate for novel drug targets. Interestingly, some of the identified orthologous and paralogous enzymes were found playing significant role in the key metabolic activities while others were found playing active role in the process of pathogenesis. The N-acetylneuraminate lyase was analyzed as the candidate of lateral genes transfer. These findings clearly suggest the active participation of lateral gene transfer and gene duplication during evolution of T. vaginalis from the enteric to the pathogenic urogenital environment.     

植物基因组研究进展(综述)

目前集中在模式植物拟南芥、水稻的基因组研究进展迅速,基因组测序和物理作图极大地便利了基于分子标记图的基因克隆,并增加了对植物基因组的组织、结构和进化的认识。     

Applications of metabolic modeling to drive bioprocess development for the production of value-added chemicals

Increasing numbers of value added chemicals are being produced using microbial fermentation strategies. Computational modeling and simulation of microbial metabolism is rapidly becoming an enabling technology that is driving a new paradigm to accelerate the bioprocess development cycle. In particular, constraint-based modeling and the development of genome-scale models of industrial microbes are finding increasing utility across many phases of the bioprocess development workflow. Herein, we review and discuss the requirements and trends in the industrial application of this technology as we build toward integrated computational/experimental platforms for bioprocess engineering. Specifically we cover the following topics: (1) genome-scale models as genetically and biochemically consistent representations of metabolic networks; (2) the ability of these models to predict, assess, and interpret metabolic physiology and flux states of metabolism; (3) the model-guided integrative analysis of high throughput ‘omics’ data; (4) the reconciliation and analysis of on- and off-line fermentation data as well as flux tracing data; (5) model-aided strain design strategies and the integration of calculated biotransformation routes; and (6) control and optimization of the fermentation processes. Collectively, constraint-based modeling strategies are impacting the iterative characterization of metabolic flux states throughout the bioprocess development cycle, while also driving metabolic engineering strategies and fermentation optimization.