Towards Kinetic Modeling of Global Metabolic Networks： Methylobacterium extorquens AM1 Growth as Validation

Here we report a systematic method for constructing a large scale kinetic metabolic model and its initial application to the modeling of central metabolism of Methylobacterium extorquens AM1, a methylotrophic and environmental important bacterium. Its central metabolic network includes formaldehyde metabolism, serine cycle, citric acid cycle, pentose phosphate pathway, gluconeogensis, PHB synthesis and acetyl-CoA conversion pathway, respiration and energy metabolism. Through a systematic and consistent procedure of finding a set of parameters in the physiological range we overcome an outstanding difficulty in large scale kinetic modeling: the requirement for a massive number of enzymatic reaction parameters. We are able to construct the kinetic model based on general biological considerations and incomplete experimental kinetic parameters. Our method consists of the following major steps: 1) using a generic enzymatic rate equation to reduce the number of enzymatic parameters to a minimum set while still preserving their characteristics; 2) using a set of steady state fluxes and metabolite concentrations in the physiological range as the expected output steady state fluxes and metabolite concentrations for the kinetic model to restrict the parametric space of enzymatic reactions; 3) choosing enzyme constants K's and K'eqs optimized for reactions under physiological concentrations, if their experimental values are unknown; 4) for models which do not cover the entire metabolic network of the organisms, designing a dynamical exchange for the coupling between the metabolism represented in the model and the rest not included.

Mathematical Modeling of a Metabolic Network to Study the Impact of Food Contaminants on Genomic Methylation and DNA Instability

Environmental contamination of food is a worldwide public health problem. Folate mediated one- carbon metabolism plays an important role in epigenetic regulation of gene expression and mutagenesis. Many contaminants in food cause cancer through epigenetic mechanisms and/or DNA instability i.e. default methylation of uracil to thymine, subsequent to the decrease of 5-methylte- trahydrofolate (5 mTHF) pool in the one-carbon metabolism network. Evaluating consequences of an exposure to food contaminants based on systems biology approaches is a promising alternative field of investigation. This report presents a dynamic mathematical modeling for the study of the alteration in the one-carbon metabolism network by environmental factors. It provides a model for predicting “the impact of arbitrary contaminants that can induce the 5 mTHF deficiency. The model allows for a given experimental condition, the analysis of DNA methylation activity and dumping methylation in the de novo pathway of DNA synthesis.

Large-Scale Kinetic Parameter Identification of Metabolic Network Model of <i>E. coli</i>Using PSO

In metabolic network modelling, the accuracy of kinetic parameters has become more important over the last two decades. Even a small perturbation in kinetic parameters may cause major changes in a model’s response. The focus of this study is to identify the kinetic parameters, using two distinct approaches: firstly, a One-at-a-Time Sensitivity Measure, performed on 185 kinetic parameters, which represent glycolysis, pentose phosphate, TCA cycle, gluconeogenesis, glycoxylate pathways, and acetate formation. Time profiles for sensitivity indices were calculated for each parameter. Seven kinetic parameters were found to be highly affected in the model response;secondly, particle swarm optimization was applied for kinetic parameter identification of a metabolic network model. The simulation results proved the effectiveness of the proposed method.

Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities

Background:Synthetic microbial communities,with different strains brought together by balancing their nutrition and promoting their interactions,demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications.The potential of such microbial communities has not been explored,due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.Results:Genome-scale metabolic models(GEM)have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities,since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbehabitats and microbe-microbe interactions.In this work,we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities:predicting multi-species interactions,exploring environmental impacts on microbial phenotypes,and optimizing community-level performance.Conclusions:Although at the infancy stage,GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities.Compared to other methods,especially the use of laboratory cultures,GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality,such as identifying community members,determining media composition,evaluating microbial interaction potential or selecting the best community configuration.Future efforts should be made to overcome the limitations of the approaches,ranging from quality control of GEM reconstructions to community-level modeling algorithms,so that more applications of GEMs in studying phenotypes of microbial communities can be expected.

Genome-scale metabolic models applied for human health and biopharmaceutical engineering

Over the last 15 years,genome-scale metabolic models(GEMs)have been reconstructed for human and model animals,such as mouse and rat,to systematically understand metabolism,simulate multicellular or multi-tissue interplay,understand human diseases,and guide cell factory design for biopharmaceutical protein production.Here,we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation.Then,we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals,together with their applications.We describe how model develops from H.sapiens to other animals and from generic purpose to precise context-specific simulation.The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals.We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation.We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.

Construction and application of high-quality genome-scale metabolic model of Zymomonas mobilis to guide rational design of microbial cell factories

High-quality genome-scale metabolic models(GEMs)could play critical roles on rational design of microbial cell factories in the classical Design-Build-Test-Learn cycle of synthetic biology studies.Despite of the constant establishment and update of GEMs for model microorganisms such as Escherichia coli and Saccharomyces cerevisiae,high-quality GEMs for non-model industrial microorganisms are still scarce.Zymomonas mobilis subsp.mobilis ZM4 is a non-model ethanologenic microorganism with many excellent industrial characteristics that has been developing as microbial cell factories for biochemical production.Although five GEMs of Z.mobilis have been constructed,these models are either generating ATP incorrectly,or lacking information of plasmid genes,or not providing standard format file.In this study,a high-quality GEM iZM516 of Z.mobilis ZM4 was constructed.The information from the improved genome annotation,literature,datasets of Biolog Phenotype Microarray studies,and recently updated Gene-Protein-Reaction information was combined for the curation of iZM516.Finally,516 genes,1389 reactions,1437 metabolites,and 3 cell compartments are included in iZM516,which also had the highest MEMOTE score of 91%among all published GEMs of Z.mobilis.Cell growth was then predicted by iZM516,which had 79.4%agreement with the experimental results of the substrate utilization.In addition,the potential endogenous succinate synthesis pathway of Z.mobilis ZM4 was proposed through simulation and analysis using iZM516.Furthermore,metabolic engineering strategies to produce succinate and 1,4-butanediol(1,4-BDO)were designed and then simulated under anaerobic condition using iZM516.The results indicated that 1.68 mol/mol succinate and 1.07 mol/mol 1,4-BDO can be achieved through combinational metabolic engineering strategies,which was comparable to that of the model species E.coli.Our study thus not only established a high-quality GEM iZM516 to help understand and design microbial cell factories for economic biochemical production using Z.mobilis as the chassis,but also provided guidance on building accurate GEMs for other non-model industrial microorganisms.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells.The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years.However,if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge,the conflicts between stoichiometric and other constraints,such as thermodynamic feasibility and enzyme resource availability,would lead to distorted predictions.In this work,we investigated a prediction anomaly of EcoETM,a constraints-based metabolic network model,and introduced the idea of enzyme compartmentalization into the analysis process.Through rational combination of reactions,we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites.This allowed us to correct the pathway structures of L-serine and L-tryptophan.A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments.Notably,this work also reveals the trade-off between product yield and thermodynamic feasibility.Our work is of great value for the structural improvement of constraints-based models.

Constrain-based analysis of gene deletion on the metabolic flux redistribution of Saccharomyces Cerevisiae

Based on the gene-protein-reaction (GPR) model of S. cerevisiae_iND750 and the method of constraint-based analysis, we first calculated the metabolic flux distribution of S. cere-visiae_iND750. Then we calculated the deletion impact of 438 calculable genes, one by one, on the metabolic flux redistribution of S. cere-visiae_iND750. Next we analyzed the correlation between v (describing deletion impact of one gene) and d (connection degree of one gene) and the correlation between v and Vgene (flux sum controlled by one gene), and found that both of them were not of linear relation. Furthermore, we sought out 38 important genes that most greatly affected the metabolic flux distribution, and determined their functional subsystems. We also found that many of these key genes were related to many but not several subsystems. Because the in silico model of S. cere-visiae_iND750 has been tested by many ex-periments, thus is credible, we can conclude that the result we obtained has biological sig-nificance.

Advances and applications of machine learning and intelligent optimization algorithms in genome‑scale metabolic network models

Due to the increasing demand for microbially manufactured products in various industries,it has become important to find optimal designs for microbial cell factories by changing the direction of metabolic flow and its flux size by means of metabolic engineering such as knocking out competing pathways and introducing exogenous pathways to increase the yield of desired products.Recently,with the gradual cross-fertilization between computer science and bioinformatics fields,machine learning and intelligent optimization-based approaches have received much attention in Genome-scale metabolic network models(GSMMs)based on constrained optimization methods,and many high-quality related works have been published.Therefore,this paper focuses on the advances and applications of machine learning and intelligent optimization algorithms in metabolic engineering,with special emphasis on GSMMs.Specifically,the development history of GSMMs is first reviewed.Then,the analysis methods of GSMMs based on constraint optimization are presented.Next,this paper mainly reviews the development and application of machine learning and intelligent optimization algorithms in genome-scale metabolic models.In addition,the research gaps and future research potential in machine learning and intelligent optimization methods applied in GSMMs are discussed.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades.This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant micro-bial pathogens.However,the detailed mechanisms of action,resistance,and toxicity of many antimicrobials remain uncertain,significantly hampering the development of novel antimicrobials.Genome-scale metabolic model(GSMM)has been increasingly employed to investigate microbial metabolism.In this review,we discuss the latest progress of GSMM in antimicrobial pharmacology,particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity,resistance,and toxicity.We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities(e.g.,gene expression),identi-fication of potential drug targets,and integration with machine learning and pharmacokinetic/pharmacodynamic modeling.Overall,GSMM has significant potential in elucidating the critical role of metabolic changes in antimi-crobial pharmacology,providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

Flux Balance Analysis Reveals Potential Anti-HIV-1 Metabolic Targets

Background:Human immunodeficiency virus type 1(HIV-1)remains a persistent global health challenge.Therefore,a continuous exploration of novel therapeutic strategies is essential.A comprehensive understanding of how HIV-1 utilizes the cellular metabolism machinery for replication can provide insights into new therapeutic approaches.Methods:In this study,we performed a flux balance analysis using a genome-scale metabolic model(GEM)integrated with an HIV-1 viral biomass objective function to identify potential targets for anti–HIV-1 interventions.We generated a GEM by integrating an HIV-1 production reaction into CD4+T cells and optimized for both host and virus optimal states as objective functions to depict metabolic profiles of cells in the status for optimal host biomass maintenance or for optimal HIV-1 virion production.Differential analysis was used to predict biochemical reactions altered optimal for HIV-1 production.In addition,we conducted in silico simulations involving gene and reaction knock-outs to identify potential anti–HIV-1 targets,which were subsequently validated by human phytohemagglutinin(PHA)blasts infected with HIV-1.Results:Differential analysis identified several altered biochemical reactions,including increased lysine uptake and oxidative phosphorylation(OXPHOS)activities in the virus optima compared with the host optima.In silico gene and reaction knock-out simulations revealed de novo pyrimidine synthesis,and OXPHOS could serve as potential anti–HIV-1 metabolic targets.In vitro assay confirmed that targeting OXPHOS using metformin could suppress the replication of HIV-1 by 56.6%(385.4±67.5 pg/mL in the metformintreated group vs.888.4±32.3 pg/mL in the control group,P<0.001).Conclusion:Our integrated host-virus genome-scale metabolic study provides insights on potential targets(OXPHOS)for anti-HIV therapies.

In silico cell factory design driven by comprehensive genome‑scale metabolic models:development and challenges

Genome-scale metabolic models(GEMs)have been widely used to design cell factories in silico.However,initial flux balance analysis only considers stoichiometry and reaction direction constraints,so it cannot accurately describe the distribution of metabolic flux under the control of various regulatory mechanisms.In the recent years,by introducing enzymology,thermodynamics,and other multiomics-based constraints into GEMs,the metabolic state of cells under different conditions was more accurately simulated and a series of algorithms have been presented for microbial phenotypic analysis.Herein,the development of multiconstrained GEMs was reviewed by taking the constraints of enzyme kinetics,thermodynamics,and transcriptional regulatory mechanisms as examples.This review focused on introducing and summarizing GEMs application tools and cases in cell factory design.The challenges and prospects of GEMs development were also discussed.

The future of genome-scale modeling of yeast through integration of a transcriptional regulatory network

Metabolism is regulated at multiple levels in response to the changes of internal or external conditions. Transcriptional regulation plays an important role in regulating many metabolic reactions by altering the concentrations of metabolic enzymes. Thus, integration of the transcriptional regulatory information is necessary to improve the accuracy and predictive ability of metabolic models. Here we review the strategies for the reconstruction of a transcriptional regulatory network （TRN） for yeast and the integration of such a reconstruction into a flux balance analysis-based metabolic model. While many large-scale TRN reconstructions have been reported for yeast, these reconstructions still need to be improved regarding the functionality and dynamic property of the regulatory interactions. In addition, mathematical modeling approaches need to be further developed to efficiently integrate transcriptional regulatory interactions to genome-scale metabolic models in a quantitative manner.

Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

Backgrounds Arachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small-molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle.什)-2-( 1 -hydroxy 1-4-oxocycIohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5-Iipoxygenase (5-LOX) and 15-LOX in vitro^ exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology. Methods: First, we generated an experimental data set by monitoring the dynamics of AA metabolites, concentration in A23187 stimulated and different doses of HOEC co-incubated RAW264.7. Then we constructed a dynamic model of A23187-stimulated AA metabolic model to evaluate how a model-based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC? Results: Compared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5-LOX and 15-LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early-stage of stimulation-related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model. Conclusion-. Using the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.

微生物制造绿色化学品研究进展

微生物制造利用生物质和二氧化碳等可再生原料进行化学品的绿色生产,显示出了巨大的二氧化碳减排潜力,是促进实现“碳中和”目标的重要途径,其核心内容之一是高效微生物细胞工厂的设计与构建。综述了基于基因组规模代谢网络模型的代谢流分析和代谢途径预测研究进展;介绍了新型基因组编辑工具助力微生物细胞工厂的高效开发;总结了代谢调控策略用于提升细胞工厂生产能力。此外,还概述了微生物制造关键技术在第三代生物制造中的应用。最后,展望了未来微生物制造在化学品生产中的应用和发展方向。

基于改进GM(1,n)的动态网络舆情预警模型

舆情的自由传播会导致网络集群行为的发生,易产生负面社会影响,威胁公共安全,因此建立网络舆情监控及预警机制是防控舆情传播、维护社会稳定的必要措施。首先,通过分析谣言的形成机制,构建了舆情发展预测指标体系;其次,通过建立多因素GM(1,n)模型对舆情发展的走向进行预测;然后,分别结合新陈代谢理论与马尔可夫理论改进上述预测模型;最后,以微博“新疆棉”事件和“成都四十九中”事件为例,对GM(1,n)模型、马尔可夫GM(1,n)模型和新陈代谢马尔可夫GM(1,n)模型预测舆情发展的能力进行对比,并比较了新陈代谢马尔可夫GM(1,n)模型与随机森林模型。实验结果表明,相较于原始模型与随机森林模型,新陈代谢马尔可夫GM(1,n)模型的平均预测精度分别提高了10.6和5.8%。可见,新陈代谢马尔可夫GM(1,n)模型在预测网络舆情发展趋势问题上具有良好的性能。

基于代谢分析的SARS-CoV-2药物靶点预测方法

提出一种基于代谢分析的抑制SARS-CoV-2复制的药物靶点预测方法。使用5组基因表达综合数据库(GEO)的人类肺部组织细胞的转录组学数据,提取出SARS-CoV-2入侵宿主细胞后显著高表达的基因,进而重构出病毒入侵肺部组织细胞后的代谢网络模型;之后采用基因敲除、毒性测试等系统生物学分析方法来预测药物靶点。对GEO中5个数据集的样本进行分析,结果显示各数据集预测的靶点基因具有一定的一致性。其中,PLPBP是5个数据集中预测的共有靶点基因,说明它对于SARS-CoV-2代谢活动具有重要作用,可作为治疗该疾病的潜在药物靶点;另外,BCAT1、BCAT2、ADI1也具有一定的研究价值。提出的方法为预测SARS-CoV-2的药物靶点提供一种新的思路,预测的药物靶点也具有进一步临床研究的潜力。

FluxExplorer: A general platform for modeling and analyses of metabolic net-works based on stoichiometry

Stoichiometry-based analyses of meta- bolic networks have aroused significant interest of systems biology researchers in recent years. It is necessary to develop a more convenient modeling platform on which users can reconstruct their network models using completely graphical operations, and explore them with powerful analyzing modules to get a better understanding of the properties of metabolic systems. Herein, an in silico platform, FluxExplorer, for metabolic modeling and analyses based on stoichiometry has been developed as a publicly available tool for systems biology research. This platform integrates various analytic approaches, in- cluding flux balance analysis, minimization of meta- bolic adjustment, extreme pathways analysis, shadow prices analysis, and singular value decom- position, providing a thorough characterization of the metabolic system. Using a graphic modeling process, metabolic networks can be reconstructed and modi- fied intuitively and conveniently. The inconsistencies of a model with respect to the FBA principles can be proved automatically. In addition, this platform sup- ports systems biology markup language (SBML). FluxExplorer has been applied to rebuild a metabolic network in mammalian mitochondria, producing meaningful results. Generally, it is a powerful and very convenient tool for metabolic network modeling and analysis.

利用代谢网络模型和线性规划法在线预测谷氨酸发酵中产物浓度

在好氧型的谷氨酸发酵实验中发现,溶解氧(DO)对发酵性能有很大的影响,谷氨酸的生成方式也因此有很大不同:较低的DO水平能够延长产酸期、提高谷氨酸的最终浓度,但是代谢副产物———乳酸也有较大程度的积蓄;而DO水平过高,虽然代谢副产物不会生成积蓄,但菌体消亡过快导致产酸期缩短、谷氨酸的最终浓度降低.同时,谷氨酸的生成方式与发酵过程中摄氧率(OUR)和CO2的释放率(CER)有着非常紧密的关联.作者利用代谢网络模型并结合使用线性规划优化法,通过在线测定OUR和CER,比较准确地在线推定出发酵过程中谷氨酸的质量浓度变化.与传统的非构造式动力学模型相比,上述预测方法具有建模简单、模型物理意义明确、通用性能好等优点,为后续过程的在线控制和优化提供一种全新和有效的途径.

用灰色神经网络组合模型预测农机总动力发展

农机总动力的需求预测是一个复杂的非线形系统,其发展变化具有增长性和波动性。该文首先在灰色预测模型的基础上建立了新陈代谢型灰色预测模型群,然后结合灰色GM(1,1)模型和BP网络模型的优缺点,建立了串联新陈代谢型灰色神经网络组合预测模型,并对中国农机总动力需求进行了预测,结果表明预测值和实际结果有很好的一致性。