Noise effect in metabolic networks

Constraint-based models such as flux balance analysis （FBA） are a powerful tool to study biological metabolic networks. Under the hypothesis that cells operate at an optimal growth rate as the result of evolution and natural selection, this model successfully predicts most cellular behaviours in growth rate. However, the model ignores the fact that cells can change their cellular metabolic states during evolution, leaving optimal metabolic states unstable. Here, we consider all the cellular processes that change metabolic states into a single term‘noise＇, and assume that cells change metabolic states by randomly walking in feasible solution space. By simulating a state of a cell randomly walking in the constrained solution space of metabolic networks, we found that in a noisy environment cells in optimal states tend to travel away from these points. On considering the competition between the noise effect and the growth effect in cell evolution, we found that there exists a trade-off between these two effects. As a result, the population of the cells contains different cellular metabolic states, and the population growth rate is at suboptimal states.

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.

FunHoP:Enhanced Visualization and Analysis of Functionally Homologous Proteins in Complex Metabolic Networks

Cytoscape is often used for visualization and analysis of metabolic pathways.For example,based on KEGG data,a reader for KEGG Markup Language(KGML)is used to load files into Cytoscape.However,although multiple genes can be responsible for the same reaction,the KGMLreader KEGGScape only presents the first listed gene in a network node for a given reaction.This can lead to incorrect interpretations of the pathways.Our new method,FunHoP,shows all possible genes in each node,making the pathways more complete.FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq.Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads,and weighting the reads on gene length and ratio,a new expression value is calculated for the node as a whole.Differential expression at node level is then applied to the networks.Using prostate cancer as model,we integrate RNA-seq data from two patient cohorts with metabolism data from literature.Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically.Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.

Petri Net Based Metabolic Network Parameters Fitting with GPU Acceleration

Classical Petri net has been applied into biological analysis, especially as a qualitative model for biochemical pathways analysis, but lack of the ability for quantitative kinetic simulations. In our study, we presented an integra- tion work of the qualitative model--Petri nets with the quantitative approach-ordinary differential equations （ODEs） for the modeling and analysis of metabolic networks. As an application of our study, the computational modeling of arachidonic acid （AA） biochemical network was provided. A Petri net was set up to present the constraint-based dynamic simulations on AA metabolic network combined with the validated ODEs model. Furthermore, Graphics Processing Unit （GPU） was adopted to accelerate the calculation of kinetic parameters unavailable from experi- ments. Our results have indicated that the proposed simulation method was practicable and useful with GPU accel- eration, and provides new clues for the large-scale qualitative and quantitative models of biochemical networks.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine(TCM)in vivo.However,this process is usually hindered by the insufficient characteristic fragments of metabolites,ubiquitous matrix interference,and complicated screening and identification procedures for metabolites.In this study,an effective strategy was established to systematically characterize the metabolites,deduce the metabolic pathways,and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo.The strategy was divided into five steps.First,the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry(MS)system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites.Second,an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites,which reduced approximately 39%of the interfering ions.Third,an acquisition model was developed and used to trigger more tandem MS(MS/MS)fragments of precursor ions based on the targeted ion list.The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode.Fourth,the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction.The main in vivo metabolic pathways of bufadienolides were elucidated.A total of 147 metabolites were characterized,and the main biotransformation reactions of bufadienolides were hydroxylation,dihydroxylation,and isomerization.Finally,the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS,and the metabolic profiles were clearly identified.This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

A novel hyper-cube shrink algorithm to predict metabolic fluxes based on enzyme activity only

OBJECTIVE One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the yield of an expected end product.METHDOS Metabolic control analysis(MCA)performs such role to calculate the sensitivity of flux change upon that of enzymes under the framework of ordinary differential equation(ODE)models,which are restricted in small-scale networks and require explicit kinetic parameters.The constraint-based models,like flux balance analysis(FBA),lack of the room of performing MCA because they are parameters-free.In this study,we developed a hyper-cube shrink algorithm(HCSA)to incorporate the enzymatic properties to the FBA model by introducing a pair of parameters for each reaction.Our algorithm was able to handle not only prediction of knockout strains but also strains with an adjustment of expression level of certain enzymes.RESULTS We first showed the concept by applying HCSA to a simplest three-nodes network.Then we show the HCSA possesses Michaelis-Menten like behaviors characterized by steady state of ODE.We obtained good prediction of a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues.Finally we showed its capability of predicting the flux distribution in genome-scale networks by applying it to sporulation in yeast.CONCLUSION We have developed an algorithm the impact on fluxes when certain enzymes were inhibited or activated.It provides us a powerful tool to evaluate the consequences of enzyme inhibitor or activator.

Energy intake,metabolic homeostasis,and human health

The energy substances(mainly carbohydrates and fats)are the basis and guarantee of life activity,especially the oxidative phosphorylation for energy supply.However,excessive absorption and accumulation of these substances can lead to metabolic diseases such as obesity,hyperlipidemia,diabetes,and cancers.A large amount of studies demonstrate that G protein-coupled receptors(GPCRs)play a key role in identification and absorption of energy substances,and the signaling network of nerves,immune,and endocrine regulates their storage and utilization.The gastrointestinal mucus layer not only identifies these substances through identification in diet components but also transfers immune,metabolic,and endocrine signals of hormones,cytokines,and chemokines by promoting interactions between receptors and ligands.These signaling molecules are transferred to corresponding organs,tissues,and cells by the circulatory system,and cell activity is regulated by amplifying of cell signals that constitute the wireless communication network among cells in the body.Absorption,accumulation,and utilization of energy substances in the body obey the law of energy conservation.Energy is stored in the form of fat,and meets the demand of body via two coupled mechanisms:catabolism and oxidative phosphorylation.Under normal physiological conditions,fat consumption involves ketone body metabolism through the circulatory system and glucose consumption requires blood lactic acid cycle.Accumulation of excessive energy leads to the abnormal activation of mammalian target of rapamycin(mTOR),thus promoting the excretion of glucose or glycogen in the form of blood glucose and urine glucose.Alternatively,the body cancels the intercellular contact inhibition and promotes cell proliferation to induce carcinogenesis,which can induce the consumption of large amounts of glucose.Intercellular communication is performed by signaling molecules via sensing,absorption,accumulation,and utilization of energy substances,and anabolism and catabolism are controlled by the central metabolic pathway.Therefore,slower catabolism will result in longer life expectancy,whereas faster catabolism results in shorter life expectancy.Energy substances in diet influence the balance between energy and metabolism in the body through the sensing function of the gastrointestinal system at two levels:cellular communication network and metabolic network.The present review of studies aims to strengthen our knowledge on cellular communication and metabolic networks to offer a dietary guidance on the metabolism and communication role of various foods.

Current status of nitrous oxide use in pediatric patients

Nitrous oxide is one of the most commonly used inhalational anesthetic agents used in practice.It is a cost-effective,pleasant,safe,and versatile anesthetic agent with many desirable properties like good quality analgesia,decreased awareness,accelerated induction and recovery from anesthesia,and reduced utilization of other expensive inhalational agents with potential cost savings.The use of nitrous oxide has been questioned by a lot of studies and case reports perceiving its adverse systemic,hematological,immune,and neurologic adverse effects.However,the literature in the recent past has tried to resolve the controversies related to its use.The concerns over an increase in cardiovascular complications and mortality following nitrous oxide use have been negated by recent data.However,its use in certain vulnerable populations like children with cobalamin and folate deficiency or defects in their metabolic pathways remains a cause of concern for its toxic effects.In this narrative review,we aim to discuss the pharmacological properties of nitrous oxide,the potential advantages and drawbacks of the use of nitrous oxide in children,address the neurodevelopmental and other systemic effects,and throw light on the evidence regarding the safety of nitrous oxide use and its current role in pediatric procedural sedation and anesthesia practice.The literature related to its use in the pediatric population for painful procedures and surgeries has been summarized.

SynBioEcoli： a comprehensive metabolism network of engineered E. coil in three dimensional visualization

Background： A comprehensive metabolism network of engineered E. coli is very important in systems biology and metabolomics studies. Many tools focus on two-dimensional space to display pathways in metabolic network. However, the usage of three-dimensional visualization may help to understand better the intricate topology of metabolic and regulatory networks. Methods： We manually curated large amount of experimental data （including pathways, reactions and metabolites） from literature related with different types of engineered E. coli and then utilized a novel technology of three dimensional visualization to develop a comprehensive metabolic network named SynBioEcoll. Results： SynBioEoli contains 740 biosynthetic pathways, 3,889 metabolic reactions, 2,255 chemical compounds manually curated from about 11,000 metabolism publications related with different types of engineered E. coil Furthermore, SynBioEcoli integrates with various informatics techniques. Conclusions： SynBioEcoli could be regarded as a comprehensive knowledgebase of engineered E. coli and represents the next generation cellular metabolism network visualization technology. It could be accessed via web browsers （such as Google Chrome） supporting WebGL, at http：//www.rxnfinder.org/synbioecoli/.

Network Visualization in Cell Biology

Many of the processes known to take place in biological cells are analyzed in the form of different types of network. The complexity of these networks increases along with our knowledge of these processes, making their analysis more difficult. Network visualization is a powerful analysis method that will have to be developed further to deal with this complexity. This survey provides a brief overview of network visualization in general, followed by an in-depth discussion of its application to three network types specific to cell biology, namely gene regulatory, protein interaction, and metabolic networks. Finally, we discuss the difficulty of visually integrating these network types and trying to compare networks of cells that belong to different organisms.

Seed metabolomic study reveals significant metabolite variations and correlations among different soybean cultivars

Soybean[Glycine max（L.） Merr.]is one of the world＇s major crops,and soybean seeds are a rich and important resource for proteins and oils.While ＂omics＂ studies,such as genomics,transcriptomics,and proteomics,have been widely applied in soybean molecular research,fewer metabolomic studies have been conducted for largescale detection of low molecular weight metabolites,especially in soybean seeds.In this study,we investigated the seed metabolomes of 29 common soybean cultivars through combined gas chromatography-mass spectrometry and ultra-performance liquid chromatography-tandem mass spectrometry.One hundred sixty-nine named metabolites were identified and subsequently used to construct a metabolic network of mature soybean seed.Among the 169 detected metabolites,104 were found to be significantly variable in their levels across tested cultivars.Metabolite markers that could be used to distinguish genetically related soybean cultivars were also identified,and metabolitemetabolite correlation analysis revealed some significant associations within the same or among different metabolite groups.Findings from this work may potentially provide the basis for further studies on both soybean seed metabolism and metabolic engineering to improve soybean seed quality and yield.

New approaches for archaeal genome-guided cultivation

Archaea,one of the three domains of life along with Bacteria and Eukarya,contains ancient life forms such as methanogen that are observed today on Earth,and one lineage Asgard archaea is also considered the closest ancestor of Eukarya.Recently,with the development of interdisciplinary studies from Earth and Life sciences,archaeal organisms are considered to play pivotal roles in geochemical cycling in nature.However,our understanding of the attributes,origin and evolution,geochemical and ecological functions of Archaea is hampered by the scarcity of archaeal isolates,which has represented a challenge to researchers for the last 40 years.Cultivation-independent sequencing and phylogenomic analyses have demonstrated a considerable diversity of Archaea with more than 20 novel phyla.However,only four archaeal phyla have cultured representatives,leaving large gaps in our knowledge of the metabolic capabilities and ecological functions of the majority of archaeal strains identified exclusively by DNA sequencing.In this review,we summarize the discovery and development of archaeal research,highlight the knowledge gap between uncultured and cultured archaeal microbes,and call on the importance of devoting greater research efforts to archaeal cultivation.Finally,we outlined new ideas and strategic approaches,namely,(1)genome-based methods,(2)microbial network information-based methods,(3)genome-scale model-guided methods,and(4)machine learning methods,to enable the cultivation of uncultivated archaeal microbes using accumulated high-throughput sequencing data.

OP-Synthetic： identification of optimal genetic manipulations for the overproduction of native and non-native metabolites

Constraint-based flux analysis has been widely used in metabolic engineering to predict genetic optimization strategies. These methods seek to find genetic manipulations that maximally couple the desired metabolites with the cellular growth objective. However, such framework does not work well for overproducing chemicals that are not closely correlated with biomass, for example non-native biochemical production by introducing synthetic pathways into heterologous host cells. Here, we present a computational method called OP-Synthetic, which can identify effective manipulations （upregulation, downregulation and deletion of reactions） and produce a step-by-step optimization strategy for the overproduction of indigenous and non-native chemicals. We compared OP-Synthetic with several state-of-the-art computational approaches on the problems of succinate overproduction and N- acetylneuraminic acid synthetic pathway optimization in Escherichia coli. OP-Synthetic showed its advantage for efficiently handling multiple steps optimization problems on genome wide metabolic networks. And more importantly, the optimization strategies predicted by OP-Synthetic have a better match with existing engineered strains, especially for the engineering of synthetic metabolic pathways for non-native chemical production. OP- Synthetic is freely available at：http：//bioinfo.au.tsinghua.edu.cn/member/xwwang/OPSynthetic/.