Mathematical modeling is a promising tool for better understanding of cellular processes. In recent years, the development of coarse-grained models has gained attraction since these simple models are able to capture and describe a broad range of growth conditions. Coarse-grained models often comprise only two cellular components, a low molecular component as representative for central metabolism and energy generation and a macromolecular component, representing the entire proteome. A framework is presented that presents a strict mass conservative model for bacterial growth during a biotechnological production process. After providing interesting properties for the steady-state solution, applications are presented 1) for a production process of an amino acid and 2) production of a metabolite from central metabolism. To gain a full understanding of cellular processes, the usage of mathematical modeling and the analysis of those have become a standard in metabolic engineering, systems biology, and process engineering. Predictive models which can describe relevant cellular processes can be used as a basis for process observation and process design with the intention to optimize the properties and behavior of the cells. Even though bacteria are very diverse, the basic principles of their metabolism are quite similar. In general, every bacterial population has to cope with its environment, scavenge for nutrients, and then coordinate its central metabolism accordingly for growth and survival. The underlying regulatory networks are very densely intertwined, large, complex, and not fully known or incompletely understood, thus providing a big challenge to understand the processes in its entirety. Mathematical models trying to describe all these processes in detail are challenging and nearly impossible because of the huge number of variables and uncertain parameters.Coarse-grained models have been used in the recent years and are now frequently used to get a better understanding on cellular control strategies, gene expression, and resource allocation Typically, coarse-grained models are written down as a set of differential equations for the components of the model, whose unit, for example, is the number of molecules per cell or mol/g dry cell weight. However, mass balance equations must fulfill the conservation of mass as dictated by the first fundamental theorem of thermodynamics, and often, a consistent transfer from mass balance equations to differential equations for the concentrations of the model components is faulty or inadequate. Therefore, we start by a brief recapitulation of the structure of the ordinary differential equations for coarse-grained models that are combined with models for the environment, for example, in a bioreactor process system. Here, a new relationship for the specific growth rate in dependence on the exchange reactions of the entire network is given. This equation is fundamental since it guarantees strict mass conservation for the complete system. Conventionally, the growth rate is an empirical function and, therefore, strict mass conservation is not ensured. In a second step, we analyze the model and show interesting properties of the steady-state behavior. We provide a general steady-state solution for biochemical networks and compare outcomes of a traditional flux balance analysis with our new approach. Finally, various applications and extensions for a broad spectrum of problem formulations in biotechnology are provided: 1) an L-phenylalanine production process and 2) the production of a metabolite from central metabolism. Hereby, problems of resource allocation as well as problems of parameter estimation are addressed.mi of a component i, the mass balance reads asFrom thermodynamic principles, mass conversion is crucial and plays the major role of determining the time course of selected quantities of interest  which are called state variables. From a static view on the biochemical reaction equations alone, however, it is not possible to infer on the time course of the state variables. A mass balance equation that describes the change of a compound over time and sums up the material flow in and out of the system comes into play here. It is a differential equation. Since we are interested in the mass J describes the mass flow into the system while P describes conversion inside the system, for example, by biochemical reactions. Both summands depend on other state variables in the system. In the current form, the equation cannot be applied. The reason is as follows: for biochemical reaction networks, P describes mass conversion by reactions, and the reaction velocity strongly depends on the concentration of a compound given by ci = mi/V of the reaction partners and not on the mass mi of the reaction partners alone. For applications in systems biology, synthetic biology, and biotechnology, a different convention is used for the definition of the concentration of the cellular components . Since it is much easier to determine the entire biomass mX than the cellular volume, the following definition is used, instead, for the concentration of an intracellular metabolite: ci = mi/mX.In this general equation, N and a rate vector μ is an integral parameter that—in a strict sense—is determined by the mass exchange of the population with its environment. Therefore, it is defined asTo avoid inconsistencies, it is recommended to always start from the mass balance and reformulate the mass balance into an equation for the concentration . For a cellular network, the basic differential equation then readsτ of the population in the relationship τ =  ln 2/μ. For the biomass itself, also, a mass balance is set up that takes into account the changes of biomass concentration due to removal from the bioreactor and biomass formation due to growth. However, a different approach can be used as shown in the Supporting Information that describes the changes of biomass based on the changes of all compounds representing the biomass. Therefore, from a formal point of view, the specific growth rate μ only depends on the rate vector The specific growth rate is related to the doubling time W representing the mass matrix. Together with the equations for the concentration for a substrate S and biomass X in a bioreactor system with feeding rate qin, feed concentration Sin, and stoichiometric vector Plugging N. Solutions for rate vector N, providing possible fluxes through the cellular network W has to be taken into account as well, and the steady-state solution for the intracellular network is obtained from the relationshipIn classic flux balance analysis, the equation for a cellular network only consists of the stoichiometric matrix N as in the classic flux balance analysis but also by the kernel of matrix W, which is determined by the molecular composition of the cell. The determinant of W is given bywhich is obtained from i, and given the strict mass conservation, the sum over all mass fractions equals one. Therefore, the determinant of W is always zero, and W is nonsingular. Additionally, one can show that the kernel of W is in fact one-dimensional (the proof can be found in Supplementary Information). The complete solution N, denoted by N, denoted by N+, and the kernel of W, denoted by The addends of the second term are the mass fractions of cellular component s and an arbitrary vector The two summands in the solution are not given in a unique way and can be written with scalar factor This principle holds true for all types of cellular networks independently of its size and form, which can range from whole-cell models to, in this case, coarse-grained models as shown in A typical minimal reaction system as shown in M, proteins P, and reactions ri that connect the pools with each other and the environment. Rate rT describes the transport of the substrate into the cell, while rate rO describes overflow metabolism. Proteins P are synthesized with rate rPIt is to be noted that an extension to two or more anabolic reactions can be performed easily since, in general, the mass fractions of the macromolecules are well-known. The reaction systems, therefore, consist of a pool of metabolites Thus, the intracellular network for this minimal model can be written asM and proteins P, respectively. With the observation from rP, is only defined by the second term. Thus, for the assumption of the known specific growth rate μ and molecular composition of the cell, this rate is fixed, while the remaining rates, meaning the substrate uptake rate rT and the overflow metabolism rate rO, are coupled through one degree of freedom a. If one of these rates is known, the degree of freedom a can be determined and, therefore, the last remaining rate.For the basic structure, with To illustrate the different outcomes when applying the new approach with strict mass conservation as seen in S is taken up and four metabolites are generated which in the conventional approach are consumed in reaction r5 to produce biomass. To make it realistic, we assume that the stoichiometric coefficient for biomass is 100, that is, 100 small molecules are used to get 1 mol protein/ biomass. The reaction system is given as follows:Substrate N1 for the internal network has four rows and five columns:In the case of constraint-based models that are used, for example, for flux balance analysis or by N1 is one-dimensional, and the only possible solution, when providing 1 unit flux of substrate uptake, results in 0.01 unit of biomass . The situation becomes different when strict mass conservation is taken into account. Here, we consider the formation of protein/biomass. Therefore, the overall biomass composition dictates the flux distribution in this case. From the depicted scheme, we infer the molecular weight for the components as follows  N2  and the vector of molecular weights, the specific growth μ can be calculated as mentioned before with Hence, the null space of r1 and r4) appear. In the case at hand, the stoichiometric matrix N2, taking into account the protein/ biomass fraction as additional component, has five rows and five columns and has full rank. However, the null space of W ⋅ N2 with W given in W strongly depends on the cellular composition; the composition itself is the steady-state solution, if the system is given in the standard form . This flux vector is very different from the solution not considering strict mass conservation.It is to be noted that here only reactions that exchange with the environment  is not possible since μ itself depends on R and T. Therefore, we proceed as follows: for the case at hand, an algebraic system could be set up for the steady-state solution of the differential equation system. The system readsS, relationships of the form T = g1, and R = g2 are determined. In this way, also the dynamical system could be simulated. In addition, in this format, the system for the intracellular network consists only of two independent variables M and P. T and R from the specific growth rate as measured experimentally .The program is simplified to only one design variable T fraction is always higher than that for the R fraction which is not the case in A comparison of the growth rate indicates that only a slightly higher growth rate could be achieved in the optimal case. This is based on the observation that the protein fraction that is allocated to the Escherichia coli strain with glycerol as the substrate and decoupled biomass and product formation due to L-tyrosine auxotrophism, meaning biomass is only formed if L-tyrosine is available . As reasons for this behavior have not been investigated at this point, the mechanistics to depict this are not incorporated in the model. Using t = 71 h was performed using MATLAB R2020a with ode15s as ordinary differential equation solver and was compared to experimental results  that is excreted into the medium via reaction r3  as a function of time and a fixed-end time tend = 20 h. With the feed, the substrate concentration can be adjusted in such a way that the metabolite is excreted at best. In contrast to a batch process, the substrate is fed into the medium and, therefore, high sugar concentrations in the beginning (as for the batch process) are avoided. Since a continuous process requires much more time, a steady state is reached normally first after five times the respective time constant (in our case approx. 20 h); this type of process design is also not considered here.• A fed-batch process in a bioreactor is considered with a flexible input profile for the incoming substrate feed rate • The initial conditions are set fix for all model state variables.• The bioreactor has a maximum working volume of 5 l, while in the beginning, the experiment starts with 1 l.tend: MexV.• The objective function is the amount of product expressed in mole at The second example considers the optimal production of a metabolite  A standard procedure, often applied in bioprocess engineering tries to feed the substrate in an exponential way to keep the specific growth rate r1 is estimated given the biomass yield coefficient Y during the batch phase and since the current substrate concentration is low , we finally obtainTypically, The feeding profile is applied after the end of the batch phase.ai to optimize.2) The second profile uses a polynomial function of time for the feeding rate:tk:3) The last profile is a piecewise linear profile with six fixed switching points R fraction is high  and, therefore, due to the coupling to the T fraction, the rate of byproduct formation is low. After starting the feeding, the intracellular system switches to high values for the T fraction Next, a comparison of the feeding profiles and the final value of the objective function is shown in First, a typical outcome for the standard case is shown in rmax for all reactions. To consider these uncertainties, an ensemble of 20 models is generated during each iteration step of the optimization. This results also in an ensemble for the values of the objective function Φ, following the approach proposed by α, expectation E, and variance Var. With this formulation, stronger variations in the values of the objective function, expressed in the variance of Φ, are penalized. Since the feeding profile is geared to the growth rate, a much more conservative output is expected; if the substrate uptake, for example, would be higher than expected, more biomass would be produced, and few byproducts will be released. As can be seen in left of The simulation studies are based on a fixed set of kinetic parameters and, so far, did not consider any uncertainties with respect to the quality of these. Typically, kinetic parameters are obtained by parameter identification and subsequent parameter estimation and analysis. For our example at hand, in the next step, the uncertainty of the kinetic parameters is taken into account during the optimization procedure. Important parameters of the model are the maximal reaction velocities tend, also the cost of substrate is of interest. In a fed-batch process, the total amount of substrate Stot fed, considering the substrate concentration at the beginning S0, can be calculated byBesides the optimization of the product at tend, and minimization of the substrate cost for the entire process can be presented with a Pareto front that is shown in The outcome of having two objective functions, maximization of product at W that requires information on the mass fraction of the components of the model. With a simple example, we could show that depending on the mass composition of the cell, larger differences in the flux distribution in comparison to the standard approach could appear. With the condition of strict mass conservation, we are also able to provide a general solution for a cellular network independently of its internal structure.In bioprocess engineering, the design of experiments often is based on a mathematical description. While simple growth models taking into account only biomass, substrate, and product often are insufficient to describe the observed dynamics, whole-cell models are cumbersome and are difficult to calibrate. A good model comprises coarse-grained models because they are simple in the model structure but take into account the most important cellular processes. In this study, we propose an approach to use coarse-grained models based on a strict mass conservation to model bacterial growth as a basis for metabolic engineering applications. In this way, classical flux analysis could be extended to take into account fluxes into macromolecules such as the proteome. Additional solutions are provided by the null space of the mass matrix R fraction representing the transcription and translation apparatus and a T fraction, taking into account metabolic and transport enzymes. The outcome of these simulation studies is compared with that of a model where the R fraction is an adjustable quantity in an optimization program (the sum of the R fraction and T fraction is taken as constant). The results of the optimization program show that an optimal allocation of proteins led to a slightly higher growth rate with a comparable course of the R fraction as a function of the specific growth rate, and we conclude that the measured data are in good agreement with the expectation of an efficient and optimal acting organism.As the focus of this research is coarse-grained models, we provided a formulation for a minimal model whose structure is in accordance with that in previous studies Many studies have dealt with the derivation of growth laws under various conditions Escherichia coli FUS4 (pF81kan) strain was used as described by aroF, pheA, and tyrA  along the aromatic biosynthesis pathway. Simultaneously, it harbors the pF81kan plasmid decoding for the genes aroF, pheA, aroB (3-dehydroquinate synthase), and aroL (shikimate kinase 2) under the control of an inducable tac promoter. Furthermore, kanamycin resistance is integrated as the selection marker For the L-phenylalanine production process, in a stirred-tank bioreactor of 3.6 l working volume, a recombinant The cells were cultivated in a defined minimal medium with glycerol as the sole carbon source. All the components with their corresponding concentrations as well as its production protocol were adapted from the study by 600) of 0.01. After incubation at 37°C and 250 rpm for at least 24 h, the cells were centrifuged  and resuspended in fresh minimal medium. These cell suspensions were used for inoculation of cultivations in the stirred-tank bioreactor with a starting OD600 of 0.1.Provision of cell biomass for the inoculation for the cultivation in a stirred-tank bioreactor was realized by a two-step preliminary cultivation in shake flasks. First, a single colony of cells grown on minimal medium agar plates (E. coli FUS4 (pF81kan) for L-phenylalanine production, a 3.6 glass stirred-tank bioreactor was used . The bioreactor was equipped with two six-bladed flat-blade turbines and three equidistant baffles. The minimal medium for cultivation was prepared ex situ and pumped into the bioreactor under sterile conditions to a starting volume of 1 l. The temperature was kept at 37 °C. 42% phosphoric acid and 25% ammonia were used as titration solutions to keep the pH at 7 ± 0.1. Dissolved oxygen levels above 30% were provided by step-wise increase of either stirrer speed or aeration rate up to 1,500 rpm and 5 l/min, respectively. Furthermore, an antifoam probe was used for controlled titration of antifoam solution, if necessary . The cultivation started with an initial batch phase. After depletion of glycerol, an exponential feeding was set for the biomass production phase with a defined growth rate of μset = 0.1 1/h. Two fed-batch media were used with either 120 g/l glycerol, 2.5 g/l L-phenylalanine, 3.6 g/l L-tyrosine, 60 g/l ammonium sulfate, and 0.1 g/l kanamycin (fed-batch medium 1) or 400 g/l glycerol, 1.11 g/l L-phenylalanine, 3.8 g/l L-tyrosine, 25 g/l ammonium sulfate, and 0.1 g/l kanamycin (fed-batch medium 2). The former and the latter were titrated with 25% ammonia or 5 M potassium hydroxide to allow complete dissolution of L-tyrosine. After a sufficiently high biomass concentration of at least 20 g/l was provided, the cells were induced with 0.3 mM IPTG. Fed-batch medium 3 with the components 800 g/l, 8 g/l ammonium sulfate, 8 g/l ammonium phosphate, and 0.1 g/l kanamycin was then constantly provided with a rate of 0.18 gglycerol/gBiomassh. At the start of each fed-batch media supply, the concentrated media components calcium chloride dihydroxide (15 g/l) and iron(II) sulfate heptahydrate with sodium citrate dihydrate (22.5 g/l and 200 g/l), magnesium sulfate heptahydrate (300 g/l), and thiamine hydrochloride (7.5 g/l) were mixed in a 1:5:1:1 ratio and injected to the fermentation broth via a septum. For the start of fed-batch phase 1, 2, or 3, a mixture of 4.8, 9.6, and 8.8 ml were injected, respectively For laboratory-scale cultivation of recombinant μm filter and were stored at 4°C upon measurement. The quantification method for both amino acids is already described by μl of samples was injected to an isocratic flow of 0.6 ml/min and 5 mM sulfuric acid as mobile phase with a constant temperature of 60°C. The quantification of each component was realized by measurement of external standards.Cell dry weights were measured gravimetrically. Pre-weighted dried 2-ml microcentrifuge tubes (80 °C for at least 24 h) were used for centrifugation of 2 ml of the cell suspension . The supernatant was further used for sample preparation for high-performance liquid chromatographies (HPLCs) or discarded. The cell pellet was dried again at 80°C for at least 24 h. The biomass concentration was calculated by the difference of weight between the microcentrifuge tube with dried cell pellets and the empty microcentrifuge tube. For the quantification of the amino acid concentrations of L-phenylalanine and L-tyrosine as well as for the organic compounds glycerol, acetate, lactate, succinate, pyruvate, malate, and ethanol, two different HPLCs were used. The samples for HPLC analysis were prepared by filtration of the supernatant of each sample through a 0.2-


Computational and statistical analysis of shotgun metagenomes can predict gene abundance and is helpful for elucidating the functional and taxonomic compositions of environmental samples. Gene products are compared against physicochemical conditions or perturbations to shed light on the functions performed by the microbial community of an environmental sample; however, this information is not always available. The present study proposes a method for inferring the metabolic potential of metagenome samples by constructing a reference based on determining the probability distribution of the counts of each enzyme annotated. To test the methodology, we used marine water samples distributed worldwide as references. Then, the references were utilized to compare the annotated enzymes of two different water samples extracted from the Gulf of Mexico (GoM) to distinguish those enzymes with atypical behavior. The enzymes whose annotation counts presented frequencies significantly different from those of the reference were used to perform metabolic reconstruction, which naturally identified pathways. We found that several of the enzymes were involved in the biodegradation of petroleum, which is consistent with the impact of human hydrocarbon extraction activity and its ubiquitous presence in the GoM. The examination of other reconstructed pathways revealed significant enzymes indicating the presence of microbial communities characterizing each ocean depth and ocean cycle, providing a fingerprint of each sampled site. The Gulf of Mexico (GoM) is an area with important oil industry activity that is constantly affected by the extraction and transportation of hydrocarbons, sometimes causing large-scale spills, which have repeatedly disturbed the area . The intShotgun metagenomics has become an essential tool for inspecting the metabolic potential and taxonomic composition of environmental DNA . SequencSeveral studies outlining the functional potential of ocean water columns and sediments are based on metagenomic studies. The metabolic potential of the microbial communities revealed in metagenomic studies is expected to correlate with the characteristics of the physical and chemical parameters of each sample. Examples of these measurable parameters are dissolved organic matter (DOM), inorganic matter, temperature, pH, and depth. Other parameters that are difficult to assess are the direct and indirect interactions between microorganisms distributed in water layers and sediments. The inference of pathways based only on the above information may make some functions challenging to appraise since these parameters are not always available.In recent years, publicly available metagenomic databases have grown to house thousands of studies, providing the unique opportunity to compare experiments to identify the differences between gene products and taxa in distinct biomes . HoweverWe evaluated two metagenomes of water samples from the GoM collected in March 2016 . Sample We downloaded the assembled sequences of 19 water samples from the Sequence Read Archive (SRA) database collected on GEOTRACES cruises  coveringe-value ≤ 1E−04 were classified based on their functions according to the Enzymatic Commission (EC) number (The GoM and reference samples and the annotated gene products of enzymes showing 45% sequence identity and an ) number . A minor) number .1 with default parameters to trim adaptors and clean reads. Trimmed paired-end reads were then merged into a single fasta file for later assembly with IDBA-UD , where θgl represents certain parameters to be determined. Thus, for a given metagenome, k, not belonging to the reference, we can tell if the observed frequencies, ygk, are within the reference values or constitute a statistically unexpected event.We extend this idea further and consider a collection of metagenomes processed according to the same workflow but under slightly different environmental conditions. In this case, we expect greater variability in the observed fitdistr function from the R package MASS2. Since the data to be fitted were frequencies, we try to fit a normal distribution only when s are the sample means and standard deviation. When this condition was not fulfilled, the theoretical distributions with which we attempted to fit the data were gamma, log-normal, Weibull. In this case, the goodness of fit for each distribution was verified through the Anderson–Darling test in the R package goftest.3 To choose the optimal fit, we used the Akaike information criterion.Once the parameters of each enzyme were assigned to each metagenome in the reference, we obtained an empirical distribution for the enzymes in the reference . We fittNext, for the two metagenomes under study, we calculated quantities in a manner analogous to Eq. 1:k = 1, 2, and using the set fg of fitted distributions, we calculated the probability:with Zgk score determined with the following equation:To ease interpretation, rather than to work directly with the values in Eq. 2, we choose to express these probabilities in terms of an equivalent N(x) is the PDF of a normal distribution with a zero mean and a unitary standard deviation. The set {Zgk} is the basic information that we use to derive our results. An example of the results of these procedures is provided in where z-scores lower than −1. The networks were displayed using Cytoscape software  using MCL software  with an The selection of genes encoding enzymes representing the metabolic potential of metagenomic samples is mainly based on the search of key (marker) enzymes of expected metabolic pathways of the microbial communities. The resolution of the reconstructed pathways is usually improved by searching for other enzymes using software and data bases as for example KEGG Mapper  or by maIn contrast, our approach aims to highlight enzymes, in a sample under study, with values of the rates in Eq. 1 that are significantly different from those of the reference group of metagenomes under similar environmental conditions. We then take this set of enzymes with atypical rates as a seed with which to reconstruct a metabolic network, which gives a hint of the metabolic potential of the sample.P directly, as described in “Materials and Methods,” it is convenient to transform it into a z-score using Eq. 3. These calculations can be easily performed with the appropriate software. We provide an example of the code used in R in z-score can be calculated by hand with the usual formula. As is well known, the z-score in the center of the curve is zero, meaning that enzymes with values close to zero behave the same as in the reference. The z-scores to the right of the mean are positive, and the z-scores to the left of the mean are negative. Additionally, an important property of the z-score is that it can be used to calculate the percentage of the population below a given value. For this study, enzymes of the GoM presenting an EZS ≥ 2.0 have a rate higher than 97.72 of the reference observations. The equivalent z-score (EZS) above this region should be considered to have an atypical behavior; i.e., the enzymes with an EZS ≥ 2 are involved in metabolic functions overrepresented in the GoM communities. However, the enzymes located in the negative part of the curve, with an EZS ≤ −1, are also atypical but may be more relevant in the reference’s metagenomes. Finally, those enzymes in the interval between −1 > EZS ≤ 2 should be relevant for the communities represented in both the reference and the GoM metagenomes.The reference, as described in “Materials and Methods,” comprises a set of PDFs for those enzymes present in all 19 selected metagenomes. To obtain a random sampling and maximized variability of the metagenomes, we gathered samples located at different longitudes, latitudes, and depths. As described in the methodology section, we calculated the PDF for each enzyme appearing in all 19 analyzed reference metagenomes. After the procedure was performed, 1,127 enzymes with a full EC description available were retained for posterior analysis. The statistical analysis showed that 49 (4%) enzymes had a gamma distribution, 257 (23%) had a Weibull distribution, 356 (32%) presented a log-normal distribution, and 465 (41%) showed a normal distribution . We thenWe then analyzed the behavior of enzymes essential for the maintenance of cellular integrity that should be present in every metagenome. Examples of such proteins include RNA polymerase subunits, aminoacyl-RNAt synthetase (aaRS), ribosomal proteins, and some metabolic enzymes that have been identified in representative organisms . The KEGIn the next step, we explored whether the enzymes were equally distributed in the pathways, which was not the case, as shown in n = 2); environmental adaptation, endocrine system (n = 1); and drug resistance: antimicrobial (n = 1).The 879 enzymes that were associated with a metabolic pathway and had an EZS were organized into the B functional classes stored in the KEGG BRITE database , yieldinAmong the boxplots shown in To evaluate the pathways defining the metabolic potential of both metagenomes, we took as a starting point the BRITE classes showing the highest proportion of enzymes with a significant EZS in q3 and beyond in the distribution.As shown in The results presented in From the network, it was evident that the pathways of drug metabolism (other enzymes) in A04_AAIW showed several connected reactions in which the enzymes presented a significant EZS . Among tphoA gene  was annotated in our metagenome but not in the reference samples, and cyclohexane-1-ene-1-carbonyl-CoA dehydrogenase (EC: 1.3.8.10) was annotated in the reference but not in our metagenomes. Finally, the enzyme (EC: 1.3.62.80) pimeloyl-CoA dehydrogenase was not detected in the reference or in A04_AAIW. We will later discuss the preprocessing and postprocessing methodologies that may affect enzyme identification in metagenomic samples.The following steps in the conversion of benzoyl-CoA to acetyl or succinyl-CoA can proceed eductase . The red species . After tortho- or meta-cleavage pathways   involved in the elimination of phenolic compounds was significantly represented in A04_AAIW . In phenpathways . These palt Lake . In our  (1–48%) . From thParvibaculum lavamentivorans, which is usually detected in habitats contaminated with oil, diesel, and other compounds  and Sulfitobacter sp. DFL_14 (D18_MAX), both reported as oil degraders as previously commented.Lipids are hydrocarbons that are soluble in non-polar solvents . They arA04_AAIW , we founn waters , and in r column . Moreover column . A set or column , we founThe examples presented in the previous section showed pathway reconstructions considering the BRITE classes with wider enrichment to be those related to xenobiotic and lipid metabolism. However, as shown in The network was grouped into 35 clusters in A04_AAIW and 51 in D18_MAX, from which we extracted those clusters included more than 4 enzymes. These clusters were regrouped to evaluate the interactions within the network . The cluHalomonas salina strain AS11, which produces high levels of aldehyde dehydrogenase   catalyzes reactions of the fatty acid degradation pathway, valine, leucine, and isoleucine degradation, tryptophan metabolism, lysine degradation, β-alanine metabolism, pantothenate and CoA biosynthesis, limonene and pinene degradation, ascorbate and aldarate metabolism, and glycolysis/gluconeogenesis, among others. This enzyme has been characterized in marine bacteria such as rogenase  that mayrogenase .D-amino acid lyase that, similar to other D-amino acid lyases, is a key enzyme involved in the use of D-amino acids as a nitrogen source integrating DOM in marine environments , which is shared by the reference and this metagenome. Cystathionine gamma-synthase catalyzes reactions involved in cysteine and methionine metabolism, seleno compound metabolism, and sulfur metabolism. Cluster 23 also shares with cluster 3 threonine ammonia-lyase , which was significantly detected in our metagenome. This enzyme is a ronments .Major cluster MC2_8_a1 contains 12 groups, five of which are interconnected  by purine nucleosidase , with EZSs equally represented in the reference and the metagenome. This enzyme is involved in purine metabolism and nicotinate and nicotinamide metabolism. Clusters 10, 11, 14, 22, 25, 26, 29, and 10 share a 5′-nucleotidase  and purine nucleoside phosphorylase , both of which have significant EZSs and catalyze reactions involved in purine metabolism, pyrimidine metabolism, nicotinate and nicotinamide metabolism, and the biosynthesis of secondary metabolites. In a previous section, we discussed the relevance of 5′-nucleotidase in the phosphate cycle. Clusters 11 and 14 share thymidine phosphorylase (EC: 2.4.2.4) and thymidine kinase (EC: 2.7.1.21), both of which were identified at significant levels and catalyze reactions involved in pyrimidine metabolism and drug metabolism. Clusters 10, 14, 22, and 29 contain enzymes related to drug metabolism – other enzymes, such as hypoxanthine-guanine phosphoribosyltransferase (PRTase) , whose activity is also associated with the purine salvage pathway that generates adenine and guanine ribonucleotides and deoxynucleotides from hypoxanthine. This enzyme shows broad specificity and is universally distributed in the three cellular domains .Other pathways involved in arginine metabolism and the one-carbon pool associated with folate were identified in MC2_8_a2, which included some enzymes related to nucleotide metabolism. In particular, the one-carbon pool involved in folate metabolism was associated with a cluster of five enzymes, three of which were detected at significant levels: dihydrofolate reductase (DHFR) , formyltetrahydrofolate deformylase , and methionyl-tRNA formyltransferase . DHFR catalyzes the reduction of dihydrofolate (DHF) to tetrahydrofolate (THF); this is an indispensable enzyme for all organisms because of its role in the biosynthesis of purine nucleotides and some amino acids . This enThe clusters in L-glutamine  and further into L-glutamate . An alternative pathway with the significant role of directly transforming ammonia into L-glutamate, mediated by glutamate dehydrogenase [NAD(P)+] , was also identified. The end product of this reaction enters D-glutamine and D-glutamate metabolism, in which glutamate is reversibly transformed into two oxoglutarate by glutamate dehydrogenase [NAD(P)+]. In turn, two oxoglutarate is taken up by the citrate cycle or is transformed into D-glutamate by glutamate racemase . D-Glutamate is subsequently transformed into UDP-N-acetylmuramoyl-L-alanyl-D-glutamate by UDP-N-acetylmuramoylalanine-D-glutamate ligase , and the next product enters peptidoglycan biosynthesis. All of the enzymes of this pathway have significant EZSs. The abovementioned findings are important since although D-amino acids are minor components of living organisms, they occur in a wide range of natural environments, such as soils  , which is then transformed into as soils , rivers,as soils , among oN-carbamoyl-β-alanine, catalyzed by dihydropyrimidinase . The last reaction is the conversion to β-alanine by the enzyme beta-ureidopropionase . Similar results have shown that the non-protein amino acids β-alanine and γ-aminobutyric acid can be found as dissolved free amino acids in marine water (n = 12), all of which participated in reactions involved in purine metabolism.MC1_9_a1 includes four populated pathways: β-alanine metabolism; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; and cysteine and methionine metabolism. Clusters 11, 19, and 24 contain aldehyde dehydrogenase, which participates in alkane degradation as previously mentioned. This versatile enzyme in this cluster also participates in the production of β-alanine. From this pathway, we identified all of the steps in the bidirectional conversion of uracil into β-alanine. The enzymes participating in this pathway are dihydropyrimidine dehydrogenase (NAD+) subunit PreT  and dihydropyrimidine dehydrogenase (NADP+) , which catalyzes the transformation of uracil into 5,6-dihydrouracil, followed by the reaction converting this compound into ne water . The seqExtracting meaningful biological information from metagenomes is a serious bioinformatics challenge. A considerable number of tools have been employed to identify useful information that can help to understand the taxonomic and genomic composition of several biomes . ConsequHowever, as shown in the results, some enzymes, such as some of the aaRSs, are non-normally distributed. Moreover, the log-normal and Weibull observed distributions have long tails, which are characteristic of these kinds of distributions. The meaning of this behavior is difficult to explain; however, we know that these enzymes are not well preserved in known species, and several of them have been tested only in representative organisms. It is possible that the organisms with these aaRSs are not heavily represented in the selected samples. This lack of preservation of certain aaRSs has been shown for some archaeal, bacterial, and organellar genomes that instead employ indirect aminoacylation pathways , which mAs previously mentioned, identifying valuable information in metagenome samples is challenging since the methodological decisions made concerning trimming, assembly, and annotation can impact the results and affect the tested distributions, especially for those genes that are less widely distributed in the samples. However, in the analysis performed herein, we attempted to consider methodological and biological variation by evaluating the distribution of each enzyme.Another factor affecting the PDFs of enzymes is the kind of metagenome selected to construct the reference dataset. The approach applied herein first considered samples collected worldwide, representing distinct depths and conditions, which were compared with the GoM samples under the assumption that they should differ, at least concerning the presence of hydrocarbons. However, the environmental conditions were poorly described in several metagenomic studies, even when physicochemical parameters were included. Therefore, the selection of reference samples could be challenging. In these cases, the analysis encountered reference conditions presenting small variations compared with our metagenomes. As a result, we found similarities between the samples, leading to poor enzyme identification, but we hope to identify some enzymes defining the tested conditions. Additionally, the databases used for annotation will introduce bias. In this work, we employed MG-RAST, which uses several databases to perform this task. Nonetheless, the annotations are incomplete.It may be possible to improve our statistical method by modifying the selection of the reference samples. This can be done by performing sampling at several points at the same depth, longitude, and latitude and probably in the same season, considering the impact of this last parameter on the biophysiochemical composition of ocean samples, which we think will highlight the uniqueness of certain enzymes in the tested metagenomes. Moreover, by selecting the reference in this way, we expect to increase the detection of enzymes in the reference since we expect all of them to be preserved in the metagenomes; however, this is also a disadvantage since not all enzymes are represented in all metagenomes. Nevertheless, we wanted to be strict concerning this point. We also expect the enzymes to be more normally distributed in these new references, considering that the samples may be more similar.The boxplots showing the distribution of EZS in The approach also naturally highlighted other enzymes related to petroleum biodegradation and those involved in lipid and amino acid metabolism in both metagenomes, in accord with previous studies that have demonstrated that “crude oil pollution constitutes a temporary condition of carbon excess coupled to limited availability of nitrogen that prompts marine oil-degrading bacteria to accumulate storage compounds”  such as Notably, the results related to the presence of enzymes involved in oil degradation are consistent with previous works of our own and other groups that investigated the Mexican exclusive economic zone and identified oil-degrading microorganisms derived from 16S amplicon analysis ; isolateIn this study, the inspection of BRITE classes provided a general view of the global metabolism represented in the GoM metagenomes. Nevertheless, for the definition of a more detailed metabolic profile, network reconstruction is necessary. The reconstructed network revealed the interconnection of compounds whose production was catalyzed by the identified enzymes. This reconstruction considered not only the enzymes with a significant EZS ≥ 2 but also the enzymes in the interval between −1 and 2. As explained above, the EZSs located in the interval corresponded to enzymes that were equally represented in the reference and in the GoM metagenomes.Phakellia fusca in another study  . This enhoA gene  that hashoA gene . MoreovehoA gene . The samhoA gene . HoweverhoA gene .Among these enzymes, we identified key enzymes of the benzoyl-CoA pathway and some deep-sea enzyme markers. Furthermore, the identification of the β-alanine pathway in D18_MAX was also consistent with reports showing that in the marine water column, these non-protein amino acids comprise a small fraction (<4%) of the total amino acid pool, which may be released during phytoplankton decomposition  and thenA general reconstruction of metabolic pathways that could be occurring within the marine samples analyzed in this study is depicted in The identification of enzymes with bioremediation potential in ocean samples as other molecules important for medical treatments and biotechnological uses is desirable because these enzymes are more tolerant to high pressure, salinity, and temperature than enzymes identified under other environmental conditions. We proved that our approach produced promising results in this regard. In this report, we describe some examples of the more significant cases to illustrate the scope of our method, leaving some enzymes and pathways less well explained. Moreover, metagenomic bins  encode aThe amount of available biological data is continuously increasing; hence, the bioinformatics community has the golden opportunity to solve or retrieve hidden information from genomic and metagenomic data and gather information about microorganisms and gene functions that have been unexplored thus far. In this paper, we present a method for identifying the metabolic potential within metagenomic samples. Our proposed strategy explores the statistical properties of the enzymes collected from the samples, converting them into a baseline and allowing us to explore the overrepresentation of the enzymes catalyzing the reactions describing the metabolic potential of tested metagenomes. As a case study, we used two metagenomes collected in the GoM and sequences annotated by our group that were compared to reference metagenomes of the GEOTRACES project. This statistical analysis provided detailed examples of how our strategy generated clues about the metabolic network-defining pathways. Furthermore, the network reconstruction showed congruence between the identified pathways and the features of the sampled sites. Our results are promising, and we are aware that several steps in the sample treatment, sequencing, and bioinformatics pipelines employed for assembly and annotation may improve metabolic profile prediction. However, this strategy provided consistent results and a new approach for exploring the metabolic network of biomes.The original contributions presented in the study are included in the article/AL and R-MG-R conceived and designed the methodology and analysis and wrote the manuscript. FG-G performed the network reconstruction and clustering analysis. AE-Z and LS performed the MG-RAST annotation. MS-O curated the essential enzymes to aid in the bioinformatics analysis, she assembled, binned, and annotated the genomes from the metagenomes, and prepared the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Prochlorococcus, the most abundant marine cyanobacterium, occurs at scales from a single droplet of seawater to ocean basins. To interpret the structuring role of variations in genetic potential, as well as metabolic and physiological acclimation, we developed a mechanistic constraint-based modeling framework that incorporates the full suite of genes, proteins, metabolic reactions, pigments, and biochemical compositions of 69 sequenced isolates spanning the Prochlorococcus pangenome. Optimizing each strain to the local, observed physical and chemical environment along an Atlantic Ocean transect, we predicted variations in strain-specific patterns of growth rate, metabolic configuration, and physiological state, defining subtle niche subspaces directly attributable to differences in their encoded metabolic potential. Predicted growth rates covaried with observed ecotype abundances, affirming their significance as a measure of fitness and inferring a nonlinear density dependence of mortality. Our study demonstrates the potential to interpret global-scale ecosystem organization in terms of cellular-scale processes.Extensive microdiversity within  Resolving the nano-niche: Simulations of metabolism and physiology across pangenomes link the molecular and ecosystem scales. Prochlorococcus is the most abundant photosynthetic organism in the ocean, contributing most of the primary production in vast regions of the nutrient-starved subtropical gyres  . The PanGEM consists of 1117 orthologous gene clusters, 1484 reactions, and 1282 metabolites and was manually curated and annotated with multiple bioinformatics, cheminformatics, and systems biology databases and tools . On the basis of a power law regression  and quality tested using a community standard  for each sequenced organism (referred to as in silico strains) are generated using a compressed sensing (CS)–based approach  on the Atlantic Meridional Transect cruise (AMT-13) in late summer of 2003. A suite of 66 physical, chemical, and biological measurements from satellite optics, shipboard sensors, and discrete samples were interpolated at 10-m depth intervals from 10 to 200 m at 32 stations from 48°N to 45°S, passing through the Senegalo-Mauritanian upwelling region  than a linear form (R2 = 0.40), suggesting a nonlinear density dependence of mortality. Direct taxon-specific in situ phytoplankton growth rate measurements are scarce . Hence, potentially important light history dynamics associated with deep convective mixing may be improperly captured by our steady-state model.At high latitudes, the temperatures, nutrient concentrations, and light levels near the surface correspond to those typical of the deep chlorophyll maximum in the central gyres, resulting in an outcropping of low light–adapted ecotypes. In the northern hemisphere, LLI was the dominant ecotype at the surface, both in our simulations of growth rates and in observed abundance. Breaking the general trend of correspondence, we predicted high growth rates of low light–adapted ecotypes in the southernmost stations of the transect where the observed population density was low. This might reflect a dominant top-down control of populations in that region  followed an expected trend, contributing less (15 ± 14%) in high-chlorophyll regions (>15 mg chlorophyll a m−2) and more (52 ± 31%) in the oligotrophic regions, in line with previous reports (f-ratio (the proportion of nitrogen uptake supplied by nitrate) , with higher values associated with deeper or higher-latitude samples. This range and pattern is consistent with previous measurements of Prochlorococcus elemental stoichiometry in the Sargasso Sea (5.4 to 7.4) (−1 near the surface to 30.3 KJ gDW−1 below the euphotic depth (fig. S10). Population elemental stoichiometry and energy content reflect both the strain composition of each population and the phenotypic states of those strains that, in turn, reflect a great many underlying physiological processes, including alterations to macromolecular compositions and photoacclimation.The elemental composition and energy content of phytoplankton constrain marine food webs and the cycles of carbon, nitrogen, phosphorus, and other bioelements . Especially under low-light conditions, physiological acclimation was necessary to acquire sufficient reductant to exceed nongrowth associated maintenance adenosine triphosphate (ATP) demands. For example, the threshold for net positive growth of a high light–adapted strain (MIT 9312) acclimated to ideal laboratory conditions required a minimum flux of 17.7 μmol photons mS*, uptake rates are held at the level demanded by some other limiting resource . As expected, diffusion limitation was prevalent for nitrite and nitrate transport in the lower latitudes above the nutricline, where concentrations were routinely in the low-nanomolar range. Observed ammonium concentrations were predominantly just below the predicted S* (fig. S13), and transport was often limited by membrane surface area. It is possible that additional nitrogen sources that were not resolved in our simulation, especially urea and amino acids, supply the remaining deficit in nitrogen demanded by maximal growth rates; however, the competition for membrane space among multiple transporters would be even more prevalent. Phosphate concentrations were mostly above S* throughout the transect, with the notable exception of the surface layer between 20°N and 30°N where concentrations approached a diffusion-limited regime, a region known to exhibit seasonal phosphorus limitation  are regulated by a trade-off between synthesis costs, available space on the membrane, and uptake rates. OptTrans explicitly accounts for these trade-offs using a mechanistic model that combines quantitative proteomics, molecular modeling, and FBA to predict the optimal abundance of each transporter and the optimal cell size  (+)–dependent glutamate dehydrogenase . The auxiliary role of GDH in nitrogen assimilation under low-light, high-nitrogen conditions has been recognized in the model freshwater cyanobacterium Synechocystis  indicated energy limitation, although there is also a small effect (in the same order) on encounter rates due to differences in molecular diffusivity. Several low light–adapted strains acclimated to growth in these low-light, high-inorganic nitrogen environments were further able to reduce the energy demands of nitrogen assimilation by redirecting a substantial portion (up to 48%) of the flux from the ATP-dependent glutamine synthetase–glutamate synthase complex (glutamate aminating) to the reduced form of nicotinamide adenine dinucleotide phosphate , protein content was reduced, leaving the remaining cellular mass to be partitioned predominantly between either carbohydrate or lipid stores. Because the energy requirements for de novo synthesis of lipids is considerably higher than of carbohydrates, allocating C to lipids was favored near the surface, while carbohydrates were relatively enriched just above the deep chlorophyll maximum depth interval. This solution points to lipid storage as an auxiliary electron sink to cope with high-light, low-nitrogen conditions. The resulting changes in the energy content of biomass can therefore be decoupled from synoptic changes in cellular C:N ratios, a prediction with implications for food web dynamics and trophic transfer efficiency . These predictions are consistent with observations of Ek) was remarkably consistent with both in situ observations , assimilation number (rvations  (17). Theratures . Subsurf+ reductase. In some strains, excess reductant is dissipated by the plastoquinone terminal oxidase (PTOX) reaction, while in other strains lacking PTOX, the generation of singlet oxygen and other free oxygen radicals is unavoidable, requiring glutathione or thioredoxin peroxidases. Partial recovery of the photorespiratory by-product 2-phosphoglycolate was only possible for a subset of the LLIV strains, while other ecotypes are known to excrete substantial amounts of the hydroxy acid glycolate . For example, accessory genes associated with the LLIV clade included an enrichment in chitin-degrading and other polysaccharide-degrading enzymes and transporters, resources that are expected to supplement their energy-limited niche below the deep chlorophyll maximum depth. While our metabolic networks resolve the diversity of mixotrophic potential across the Prochlorococcus pangenome, comprehensive determinations of compound-specific dissolved organic matter concentrations in the marine environment remains an analytical challenge. Although exogenous organic substrate uptake was not included in our AMT simulations, ecotype-specific patterns were evident from an analysis of growth capabilities on various sole N, P, and S sources. For example, LLI and LLIV strains were universally able to grow on nitrite as a sole N source, while only four strains from other ecotypes shared that capability (fig. S16).A survey of growth capabilities and pathway enrichment analysis among the core and accessory reaction subsets of the Prochlorococcus  and are provided as a gridded dataset (data S2). To compute downwelling irradiance, we combined in situ optical profiler diffuse spectral attenuation coefficients with synoptic measurements of satellite cloud cover as a mask over a model of clear-sky direct and diffuse spectral radiation. Simulations conducted in this study were restricted to a subset of 32 stations  for which ecotype abundance data , MIT 9312 (HLII), NATL2A (LLI), MIT 1304 (LLII/III), and MIT 9313 (LLIV). Strain MED4 was analyzed for macromolecular compositions  as well as elemental composition  and dry weight as in . The current pangenome consists of 77 sequenced isolates, seven metagenome-assembled genomes, and 564 single-cell amplified genomes, comprising 866,894 unique open reading frames. Translated coding sequences were mapped to KEGG orthologs (KOs) using a bidirectional blast with KEGG’s kofamKOALA, pairing the number of unique KOs to 2084. A database containing nucleotide sequences, translated amino acid sequences, header strings, database identifiers , metadata, and descriptive statistics for each genome was compiled; this database was the basis for draft reconstruction of the PanGEM network. The draft reconstruction was then manually curated following a standard process (section S1.3) .S is the stoichiometric matrix, v is the vector of fluxes and its respective lower and upper bounds (lbv and ubv), and c is a predefined coefficient vector whose entries define the weighted set of reactions that determine fitness. Here, the objective function is defined as growth rate, the rate of synthesis of 1 g of ash-free dry biomass of specified composition. Several variations of FBA and sensitivity analyses are used throughout this article, as described in the Supplementary Materials (section S1.2).A chemical flux balance can be derived from the law of mass action (section S1.2). Assuming a steady state, a set of optimal flux distributions can be predicted that maximizes an objective function by solving the linear programTwo sequential acclimation steps, OptTrans and PhysOpt, are performed on each strain GEM at each environmental grid point. Physiological data specific to each strain, or to its closest phylogenetic relative, are initially assigned to the corresponding GEM to represent its phenotype when grown under optimal laboratory conditions. Physiological data, collected as a part of this study (data S4 to S7) and from the literature (data S8), include cell size spectra, biochemical compositions , maximum carbon fixation rates, and maintenance requirements. Boundary constraints for all parameters  are assigned to each strain GEM by compiling experimental data collected under a wide range of growth conditions. The first bilevel optimization, OptTrans, searches for the optimal distribution of metabolic fluxes, transporter abundances, and cell size that maximizes fitness, given the external concentration of each nutrient . Quantitative proteomics data and molecular modeling are used to derive catalytic rates and transmembrane domain dimensions for each transporter, necessary to predict external substrate concentration–dependent uptake rates . The OGT dataset from TOME 1.0 .Growth rates and metabolic fluxes were adjusted for temperature effects by comparing in situ growth temperatures to a model of temperature-dependent growth, parameterized for each strain. Our approach provides a strain-specific parameterization while avoiding the effects of adaptive laboratory evolution, which can be substantial (


Phytophthora infestans. Subsequently, we review achievements obtained so far and identify and discuss potential pitfalls of current models. Finally, we propose a workflow for reconstructing high-quality GEMs and elaborate on the resources needed to advance a system biology approach aimed at untangling the intimate interactions between plants and pathogens.Metabolism is the set of biochemical reactions of an organism that enables it to assimilate nutrients from its environment and to generate building blocks for growth and proliferation. It forms a complex network that is intertwined with the many molecular and cellular processes that take place within cells. Systems biology aims to capture the complexity of cells, organisms, or communities by reconstructing models based on information gathered by high-throughput analyses (omics data) and prior knowledge. One type of model is a genome-scale metabolic model (GEM) that allows studying the distributions of metabolic fluxes, i.e., the “mass-flow” through the network of biochemical reactions. GEMs are nowadays widely applied and have been reconstructed for various microbial pathogens, either in a free-living state or in interaction with their hosts, with the aim to gain insight into mechanisms of pathogenicity. In this review, we first introduce the principles of systems biology and GEMs. We then describe how metabolic modeling can contribute to unraveling microbial pathogenesis and host–pathogen interactions, with a specific focus on oomycete plant pathogens and in particular  The metabolism of an organism defines its capabilities to take up nutrients from the environment and to convert these into essential building blocks such as nucleic acids and amino acids . CellulaPhytophthora infestans. We provide an overview of recent developments in this field and discuss challenges in reconstructing GEMs in these organisms. Finally, we propose a workflow for reconstructing high-quality GEMs and lay out a number of challenges that need to be addressed for systems biology to provide its full potential to study the intimate interactions between plants and pathogens.In this review, we first provide a broad overview of the field of systems biology, with a focus on genome-scale metabolic models (GEMs). We then discuss the relevance of GEMs to study pathogens and host–pathogen interactions, particularly oomycete plant pathogens such as The rise of computational biology, high-throughput analyses tools, and advanced measurement technologies in the early twenty-first century enabled biologists to analyze the presence of basically all molecules in an organism and to measure their quantities and interactions at cell or tissue level . Today, in silico identification of essential genes, reactions, or metabolites by predicting the effect of enzyme knockouts on the functioning of the system -based models for enzyme kinetics, Bayesian, Boolean, or rule-based models for signaling and regulatory networks, or constraint-based steady-state models of metabolism such as GEMs; e system . Since me system .BOX 1Metabolism is a complex system of thousands of biochemical reactions, most of which are catalyzed by metabolic enzymes. GEMs have been developed as an effective way to generate hypotheses about cellular metabolism . A GEM iGEMs are assumed to be in steady state, which means the net uptake of nutrient mass is equal to the net production of biomass, implying there is no net accumulation of metabolites. This simplifies the model to a system of linear equations. Each reaction in the GEM is considered to have a flux, i.e., a steady-state reaction rate. The most popular method to model metabolism in this framework is called flux balance analysis FBA; . An assuThe integration of omics data can be used to impose additional model constraints or objective functions . Since mThe linear program of a GEM can often be solved in a matter of seconds on modern computers, making it a powerful computational tool to predict the effect of perturbations to GEMs . For ins(A) GEMs are reconstructed from a genome sequence and connect enzymes to reactions. (B) A GEM is a network that simulates fluxes from nutrient uptake to the production of biomass precursors. (C) The mathematical representation of a GEM. Reactions are stored in a stoichiometric matrix that is multiplied by a vector of fluxes. The solution space is limited by constraints. Here, only two fluxes are shown, but the optimization is in n dimensions.Basic principles of GEMs. Because of their integrative nature, by relating genes with reactions and metabolites, GEMs can serve as a knowledge base for species-specific information on the biochemical capacity of an organism, as deduced from its genome and from prior knowledge. Prior knowledge typically integrated in a GEM includes, for instance, the nutrients taken up and the metabolites produced in the form of biomass or secondary metabolites. Importantly, prior knowledge should be used to correct the model where automated methods are limited, such as the inference of species-specific enzymatic substrates, gene–protein–reaction associations , subcellular localization of enzymes, directionality of reactions, and substrates of transporters . Next toThe main purpose of GEMs is enabling flux simulations that can be used to investigate system complexity and dynamics . For insSystems biology offers a powerful toolbox to study pathogens and their relation with their hosts . PathogeHaemophilus influenzae that causes disease in humans  we breathe, for sequestering CO2 and maintaining the balance in the global ecosystem, and for the production of food and feed. However, plants are under constant threat of pathogens, such as fungi, oomycetes, bacteria, and viruses. In agriculture, the resulting yield losses can be substantial, reaching up to 30%  and destruction (phthora). Its arrival in Europe marked the birth of plant pathology as a discipline and ever since P. infestans has been a favorite subject of investigation  and modeled the metabolic changes in the plant when challenged by P. infestans, based on transcriptome data.Although the GEM of a plant pathogen is in principle suitable to predict essential metabolic genes and reactions, and to simulate growth (biomass production) tans GEM  with a teraction . We used vitamin . In this vitamin . Botero Phytophthora pathogen and a plant is when a germ tube emerging from a sporangium or cyst senses a suitable surface and starts swelling at the tip. Host entry by Phytophthora is facilitated by a mechanical slicing mechanism that breaches the epidermal cells  and 2009 (P. infestans) revealed that these species profoundly differ in genome size and content  within the assembled genome sequences , which ince tags . Many sunce tags . Howevernce tags . This woSaccharomyces cerevisiae, about 20% of all genes lack any functional association , a system to cluster protein orthologs with validated functions , and thiociation . Lineageociation . KOs areociation . In our In omics-based bioinformatics studies, it is common practice to search for overrepresentation (enrichment) of functional annotations in differentially abundant molecules such as mRNA or proteins . Over thP. infestans) pathosystem was subject of several systems biology studies complete and ideally gapless genome assemblies of pathogens as well as their hosts are required. The genomes need to be sequenced and assembled using novel technologies and advanced assembly methods to attain the complete coding information . Gene prSecond, the identified enzymes are then used as input for reconstructing a draft metabolic network. Functional annotation of (subunits of) metabolic enzymes and the associated reactions and metabolites should be curated according to the literature and according to the established protocol , pinpoinin vitro assays can be performed to characterize the pathogen’s growth behavior under different conditions. The ideal medium is similar in composition to the pathogen’s natural hosts to mimic natural growth. The medium needs to be analyzed by untargeted metabolomics (mass spectrometry and/or nuclear magnetic resonance) over multiple time points to provide insight into the presence and abundance of specific metabolites. Metabolites that strongly change in abundance during pathogen growth and between subsequent sampling stages are likely assimilated or secreted. This can be indicative of a nutrient transporter on the plasma membrane that is capable of transporting the respective metabolite. In addition, isotope-labeled metabolites can be added to the medium to test the assimilation of specific nutrients (Third, utrients , such asPhaeodactylum tricornutum to quantify the percentages of carbohydrate, protein, DNA/RNA, and fatty acids per gram of cellular dry weight (Fourth, the relative pathogen biomass composition and growth rates should be measured. A promising approach for this is Fourier-transform infrared spectroscopy FTIR; . This mey weight . Once thin vitro, there are likely alternative enzymes or metabolic routes that compensate for this mutation. The model should be inspected on incorrect annotations or missing reactions accordingly.Fifth, predicted phenotypes, e.g., induced by specific nutrient starvation, should be validated by growth experiments and gene/reaction essentiality should be validated in knockout or knockdown mutants, for instance, as was done for the nitrate assimilation cluster . As discSixth, the refined pathogen GEM should be integrated with a similarly refined GEM for its host. This necessitates deploying more sophisticated constraints and objective functions to simulate a more realistic symbiosis for this pathosystem, such as multi-objective simulations to address the competition for nutrients .In addition to the here proposed steps, there is a large and rapidly increasing number of methods and algorithms that can be applied to GEMs to gain further insights into the complex system of pathogen–host interactions . For insSystems biology, in particular GEMs, offers a unique approach to study oomycetes and their intricate interactions with their hosts. GEMs not only offer a holistic overview of metabolism, but also constitute a foundation on which to incorporate omics measurements at various levels, allowing integrated analyses of key processes in pathogenesis and pathogen–host interaction. Although there are still a number of remaining technical and methodological challenges, GEMs hold great promise for providing mechanistic insight into strategies exploited by oomycetes to proliferate and infect their hosts, ultimately allowing us to develop new means of controlling these highly relevant pathogens.SR wrote the first draft of the manuscript. FG edited sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This work was funded by the Food-for-Thought campaign from Wageningen University Fund.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In this Commentary, we discuss the recent advances in the field that suggest that the kinetome may be more conserved than expected. A conserved kinetome will accelerate the development of future kinetic models of integrated cellular functions and expand their scope and usability in many fields of biology and biomedicine.Computational biologists have labored for decades to produce kinetic models to mechanistically explain complex metabolic phenomena. The estimation of numerical values for the large number of kinetic parameters required for constructing large‐scale models has been a major challenge. This collection of kinetic constants has recently been termed the  This Commentary highlights recent discoveries suggesting that the kinetome (a collection of kinetic parameters) can be conserved and discusses how a conserved kinetome will accelerate the development of future kinetic models. Once whole‐genome sequences became available in the mid‐1990s, the reconstruction of metabolic networks reached the genome‐scale. Over the past two decades, genome‐scale metabolic models (GEMs) built from these network reconstructions have contributed to our understanding of systems biology of metabolism, with applications ranging from metabolic engineering to bacterial evolution to infectious disease. Genetic variation can now be incorporated into genome‐scale models of metabolism, transcription, translation, proteostasis, and cellular stresses. GEMs are often simulated using flux balance analysis that requires a minimal number of parameters estimated from empirical data.Kinetic models, on the other hand, have traditionally been limited by the need for extensive parameterization. If the kinetome can be estimated from high‐throughput data, then we can develop large‐scale—even genome‐scale—kinetic models. Since GEMs have a direct genetic basis, a new generation of kinetic models can be directly rooted in protein structures and sequence variation. The kinetome of such models would be large, requiring the estimation of many unmeasured parameters. However, if segments of the kinetome are conserved, then parameterization will be simplified. Reference kinetomes can be estimated for well‐characterized strains and applied to less well‐known strains.in vivo turnover rates of bacterial enzymes have been characterized using ratios of proteomic and fluxomic data  studies. ALE allows for the generation of strains that have adapted to high growth rates following the deletion of genes that encode specific metabolic enzymes. This approach results in the generation of “metabolic specialist” strains whose pathway usage has been rewired by ALE following the loss of a key metabolic enzyme. A large study resulted in an estimation of turnover rates for the same enzyme in multiple metabolic specialists Fig . These eE. coli with orthogenes from a diverse range of other species, from hyperthermophilic archaea to humans  models), or they specify the enzyme abundances based on measurement (enzyme‐constrained models). Ultimately, these models enable the study of the principles that underlie how the limited number of protein molecules that a cell can carry are optimally allocated to metabolic enzymes that have an inherent set of kinetic constants , providing insight into whether different omic data types can be used together to infer kinetic parameter values.et al, ex vivo erythropoietic models might be used in conjunction with gene editing of the adult hematopoietic stem cells to engineer specialist RBCs.The RBC is the most abundant cell in the human body, thus representing an important starting point for the development of personalized medicine in human systems. In a recent study, personalized RBC kinetic models were constructed through parameterization based on metabolomic, fluxomic, and genotyping data  from individual tumor samples to constrain maximum flux rates for cancer patients  and protein concentration (protein molecules per unit volume) through the well‐known equation, in vivo flux (iv), determined via fluxomics, is given by iv = app,ik × ie, where app,ik is the apparent catalytic rate for enzyme i in vivo, and ie is the in vivo enzyme concentration as determined by proteomic measurements. The term app,maxk (the maximum appk observed across multiple conditions) is a proxy for kcatinvivo and can be used in simulation models. When working with a large number of enzymes, ie becomes constrained by limited proteome size (∑i=1nei), and thus proteome allocation becomes important (Nilsson et al, The


Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases. Cystic fibrosis (CF) is the most common form of the autosomal recessive disease in Caucasians and affects 1 in every 3000–3500 births in Europe and United States . Many riBurkholderia cepacia complex (Bcc) emerged as important pathogens in the airways of immunocompromised humans, especially those with CF. The treatment of Bcc infections is challenging because of the natural resistance to various classes of antibiotics and long-term colonization, and the ability to adapt to nutrient-deficient biofilm microenvironment [ironment . There iironment . The eraRevealing the pathogen and microenvironment interactions requires an analysis of large reaction networks, which can be identified through the use of systems biology approaches. Systems biology is an integrative discipline that assembles experimental data with computational methods to describe the sophisticated biological processes of cells, tissues, and microorganisms . Genome-B. cenocepacia with its microenvironment based on the metabolic modeling and flux balance analysis (FBA) approach. Next, we examined the influence of uptake reactions, which involve substrate exchange with the extracellular space, not only on bacterial growth rates but also on flux distribution across intracellular pathways based on synthetic lethality analysis. Afterward, gene expression data from two different conditions  were integrated with the GEM of B. cenocepacia to investigate the effect of environmental conditions on bacterial metabolism.In this study, we investigated the metabolic interactions between Burkholderia cenocepacia J2315 (iPY1537) was downloaded in SBML format from the authors’ website  [The GEM of er 2019) . Detailser 2019) . BiomassWe performed the simulation of single and double synthetic lethal reactions by constraining upper and lower bounds of the fluxes to zero and selecting the constraint that would make the objective function unfeasible. Individual reactions that inhibit the objective function considered as single lethal reactions. Constraining the model by removing one pair of reactions at a time , which resulted in no cell growth, was determined as double lethal reactions. Exchange reactions were settled by available COBRA functions and were constrained individually, and were paired by setting these fluxes to zero. Biomass formation was used as the objective function. Essential exchange reactions were defined as those in which the absence of nutrients by inhibition of these reactions eventually leads to cell death. Cellular metabolic pathways were determined from enclosed subsystems of the model, and unassigned pathways were disregarded.B. cenocepacia J2315 isolates under exponential and biofilm conditions were downloaded from ArrayExpress  [B. cenocepacia. Sample accession numbers of the transcripts are accessible in The raw RNA sequencing data of wild type EB19669) . Six RNAB. cenocepacia J2315 [index function. Then, estimation of the transcripts’ abundances was performed with quant function. The abundance estimates were reported in transcripts per million (TPM).We quantified gene expression levels with the alignment-free pipeline Kallisto (v0.46.1) , which dGene level differential expression data were analyzed with the DESeq2 package (v1.22.2)  in the RB. cenocepacia by the Metabolic Adjustment for Differential Expression (MADE) algorithm using Toolbox for Integrating Genome-Scale Metabolic Models, Expression Data, and Transcriptional Regulatory Networks (TIGER) package [B. cenocepacia. To understand how the transition from exponential phase to biofilm phase impacts the intracellular metabolic fluxes and growth of B. cenocepacia, we performed the flux variability analysis of all fluxes reaching the optimal solution. Constraints were defined according to synthetic cystic fibrosis sputum medium (SCFM), a medium designed to replicate the environment of the cystic fibrosis lung [We integrated transcriptomics data with the GEM of  package ,18. Thissis lung . MinimumB. cenocepacia (iPY1537), which contained 1667 reactions and 1513 genes. We evaluated the influence of a reaction without flux on the entire known metabolic network through the synthetic lethality analysis approach  of the single lethal reactions; among these lipid metabolism reactions, fatty acid biosynthesis  was most frequently found. In remaining 71 single lethal reactions, glycan metabolism reactions were the most common   . n = 162) of the model. Notably, as the exchange and transport reactions in the model of Burkholderia cenocepacia J2315 have been extensively curated based on medium information, we grouped them together in our essentiality analysis to investigate the potentially important metabolites in the environment for the growth of bacteria. Exchange of oxygen and O2 transport via diffusion were identified as single lethal exchange/transport reactions  were most commonly responsible for pairs becoming lethal and were found in 26.1% of the pairs  were most common in lethal pairs with at least one exchange/transport reaction, and paired with tryptophan supply of the cell  were in the same directionality, and 287 reactions (48.6%) were altered between conditions. We found that a total of 176 unique exchange/transport reactions, 79 (44.9%) reactions were common, and 97 (55.1%) reactions varied across two different conditions . ReactioMetabolism of amino acids with 59 (31%) altered reactions scored as the most varied pathway when we excluded exchange/transport reactions. This was followed by carbohydrate metabolism reactions with 52 (27%) and purine and pyrimidine metabolism with 29 (15%) reactions that introduced changes. Vitamin metabolism was the fourth most altered pathway with 20 (11%) changing reactions in total for ascorbate and aldarate metabolism, folate metabolism, and pantothenate and CoA biosynthesis .In detail, pathways in arginine and proline metabolism (100%) and glycine, serine, and threonine metabolism (100%) and ascorbate and aldarate metabolism (100%) were the pathways, in which all the reactions are altered between two conditions . This isWe also examined the regulation of vitamin metabolism and the subsequent effect on various physiological functions in the system during biofilm conditions. We found that both arginine proline metabolism and the ascorbate aldarate pathway are connected to the citrate cycle through 2,5-dioxopentanoate synthesis.Condition-specific models had fluxes on 119 and 157 exchange/transport reactions for exponential and biofilm conditions, respectively. In both phases, a total of 97 fluxes were unchanged. According to the altered exchange/transport reactions, more amino acids were consumed when the cells were at the biofilm phase: 3 and 14 additional amino acid intakes were predicted in addition to common exchange/transport reactions in exponential and biofilm conditions, respectively . As the B. cenocepacia J2315. Altered metabolic fluxes were identified from the synthetic lethality analysis as well as condition-specific models based on gene expression profile. The models predicted tendencies to exclusive pathways in the biofilm development when comparing exponential conditions.In this study, the systems biology approach for model perturbation and data integration allowed us to evaluate how environmental conditions affect the metabolic pathways of B. cenocepacia under exponential conditions. The influence of different conditions on synthetic lethal interactions was adjusted by perturbing the model based on reaction constraints. According to the synthetic lethality analysis, reactions involved in fatty acid biosynthesis were most frequently found in single lethal reactions. Control at the level of fatty acid biosynthesis is crucial for membrane homeostasis, because the biophysical properties of membranes are determined by the composition of the fatty acids that are produced by de novo biosynthesis. Since lipids are the cell’s primary structural components, the enzymes of bacterial fatty acid biosynthesis are attractive targets for antimicrobial drug discovery [First, we provided a comprehensive analysis of essential reactions and affected pathways in iscovery . To thisiscovery . We claiReactions involved in phenylalanine, tyrosine, and tryptophan biosynthesis were most frequently found in lethal pairs with at least one exchange/transport reaction when we excluded single lethal reactions. Tryptophan exchange was responsible for 36% of those reaction pairs becoming lethal. In this manner, Zhang et al. showed that the absence of exogenous tryptophan caused an immediate killing on auxotrophic mycobacterium species, which is consistent with our findings . Since tB. cenocepacia with its environment and how the metabolic network is regulated as a result of biofilm development, we integrated the transcriptomics data of exponential and biofilm phases into the iPY1537. Our results highlighted that glycerolipid synthesis, lipopolysaccharide biosynthesis, fatty acid biosynthesis, and glycerophospholipid metabolism were least affected when the conditions are altered. Bacterial lipid metabolism is responsible for producing structural molecules and biological energy storage, and the biosynthesis of the extracellular polymeric substances that are crucial for biofilm formation [Second, to obtain a holistic understanding of the interactions between ormation . Phosphoormation . The encB. cenocepacia allowed us to investigate these features in the context of the metabolic modeling. Integration of transcriptomic data of the biofilm conditions revealed that bacteria tend to uptake more amino acids from the environment for growth, and reactions of the pentose-phosphate pathway are activated. These in silico predictions are compatible with the fact that throughout exponential growth, carbohydrates are first consumed as the main supply of carbon and energy but are promptly exhausted, and additional fuel molecules such as lipids and proteins are utilized to maintain survival [While earlier studies have prospected the organization of amino acid and carbohydrate metabolism of microorganisms under different environmental conditions ,27, the survival .Bacillus subtilis entered the stationary phase [Escherichia coli and almost complete depletion of serine in the stationary phase [B. cenocepacia. It can be assumed that serine is first converted to pyruvate, then used for gluconeogenesis or channeled into the Krebs cycle for energy production. Therefore, antimetabolite for serine can be potentially used to block these serine related metabolic pathways and inhibit the growth or biofilm formation, which should be evaluated in future studies.Pathway-based analysis also unveiled which fluxes in amino acid metabolism were most affected by biofilm conditions. Glycine, serine, and threonine metabolism and arginine and proline metabolism were the two subsystems in which all of the reactions were altered between conditions. In particular, the glycine, serine, and threonine pathway is closely related to central metabolism. The amino acid serine is an indispensable biological molecule, either a basic constituent for protein synthesis or as a precursor of other amino acids, nucleotides, and phospholipids . In parary phase . They alry phase ,32,33,34Several reactions in ascorbate and aldarate metabolism, folate metabolism, and pantothenate biosynthesis were altered, based on our metabolic analysis. These reactions are involved in various biological processes as the energy supply for growth and survival. In this manner, the role of the 2,5-dioxopentanoate and related pathways offer targets for understanding the aspects of microbial biofilm metabolism.B. cenocepacia with its microenvironment and predicted the physiology during adaptation to the biofilm condition, which allowed us prior knowledge to provide testable hypotheses regarding the functionality of metabolic pathways. We pointed out considerable properties in metabolic response to extracellular nutrient availability and pathway susceptibility to altered growth conditions. These predictions can guide further experimental validation in the context of characterizing essential linkages of the bacteria with their microenvironment and discovery of potential targets for biofilm-based infections. As the metabolic models only focused on metabolic genes (i. e. regardless of genes involved in regulatory processes), future studies may integrate the metabolic models together with other biological systems, such as regulatory, signaling, and protein–protein interaction networks to enable more comprehensive understanding of the biofilm formation process and pathogenic role of the bacteria.In this study, we performed computational analysis to reveal the interactions between the global properties of


The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite that supports the interactive exploration of metabolic networks. Any metabolic network visualization tool must by necessity show only a subset of all possible metabolite connections, or the results will be visually overwhelming. Existing tools, even those that purport to show an organism’s full metabolic network, limit the set of displayed connections based on predefined pathways or other preselected criteria. We sought instead to provide a tool that would give the user dynamic control over which connections to follow.The Metabolic Network Explorer is an easy-to-use, web-based software tool that allows the user to specify a starting metabolite of interest and interactively explore its immediate metabolic neighborhood in either or both directions to any desired depth, letting the user select from the full set of connected reactions. Although, as for other tools, only a small portion of the metabolic network is visible at a time, that portion is selected by the user, based on the full reaction complement, and it is easy to switch among alternate paths of interest. The display is intuitive, customizable, and provides copious links to more detailed information pages.The Metabolic Network Explorer fills a gap in the set of metabolic network visualization tools and complements other modes of exploration. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a BioCyc Pathway/Genome Database. Metabolic networks are large and complex graphs that can be daunting to comprehend. Past research has provided multiple modes for exploring metabolic networks. Highly localized modes include viewing single reactions, single metabolites and the reactions they participate in , 4, 6, aThe Metabolic Network Explorer fills a gap in the existing modes of exploration, enabling the user to begin at a starting metabolite and interactively explore the metabolic neighborhood around that starting point by moving forward and backward in the network and building up a linear path of arbitrary length. The Explorer enables exploration of connected sets of reactions without the use of pre-defined pathways.E. coli K-12 MG1655, and MetaCyc, a database of metabolic reactions and enzymes from all branches of life. The BioCyc website is powered by Pathway Tools [https://BioCyc.org (or any other Pathway Tools website) and select Metabolic Network Explorer from the Metabolism menu. When an organism PGDB is selected, this tool will access the metabolic network of that organism. When MetaCyc is the selected PGDB, this tool accesses metabolites and reactions from all domains of life for applications such as metabolic engineering. Access to EcoCyc and MetaCyc is free; access to other PGDBs beyond a limited number of free monthly pageviews requires a paid subscription. Alternatively, users can install Pathway Tools locally, build a PGDB for any organism of interest from its annotated genome, and use the Metabolic Network Explorer to interactively explore its predicted metabolic network.The Metabolic Network Explorer is a new addition to the BioCyc.org website and the Pathway Tools software suite. BioCyc  is a colay Tools , a softwWe are not aware of other tools that permit the sort of neighborhood-based exploration provided by the Metabolic Network Explorer. Metabolic databases such as BioCyc , KEGG 66, ReactoM is a JavaScript object that includes a near-complete list of possible precursor and successor metabolites based on the set of reactions in the database.  A precursor metabolite is any metabolite P such that a reaction exists with P as a reactant and M as a product. A successor metabolite is any metabolite S such that a reaction exists with M as a reactant and S as a product. Some reactions are reversible or of unspecified directionality; in such cases, a metabolite could be either a predecessor or a successor, so is included in both categories. For every neighbor metabolite, we record the list of reactions that link the neighbor to M. The data for each reaction includes its full reaction equation, its EC number, the list of enzymes and genes that catalyze the reaction, and any pathways in which the reaction participates.The Metabolic Network Explorer is a web application with its display generated entirely using HTML, JavaScript and CSS. The primary data structure is a simple linear path consisting of a list of steps, such that each step consists of a metabolite and an optional successor reaction that links the metabolite to the next step. A second data structure records the metabolic neighborhood of every metabolite that has been retrieved from the database. The metabolic neighborhood of a metabolite The user specifies a starting metabolite, and a query is issued to BioCyc or another Pathway Tools server to retrieve the immediate metabolic neighborhood of the selected starting metabolite. The web browser displays the starting metabolite with the list of possible precursors to the left, and the list of possible successors to the right. The user can mouse over any precursor or successor metabolite to see information about the linking reaction(s). The user can also click the plus icon next to any metabolite to add that metabolite to the path. If the metabolite to be added is a precursor to the first metabolite in the path or a successor to the last metabolite in the path, the path is extended in that direction. If the selected metabolite is connected to an intermediate metabolite on the current path, it will replace the existing portion of the path connected to the intermediate metabolite. Thus, only a single linear path  can be displayed at a time. When a portion of a path is replaced, the original path is saved to a list of previous paths. The user can easily switch back and forth among previous paths. A path containing more than one metabolite is displayed as a vertical linear pathway diagram, with each reaction optionally including side metabolites, enzymes and genes, EC numbers, compound structures, and links to pathways.l-homoserine (bottom). Other paths were explored along the way; they are listed in the Previous Paths section in the panel at right and can be restored by clicking on them. The display is information-rich, yet easy for a biologist to quickly comprehend. All the potential predecessor or successor metabolites of a given metabolite are collected into a single list  displayed to the left or right of each central metabolite, minimizing diagram clutter. Color-coding on the plus icons indicates whether the reaction to and/or from that metabolite is unidirectional, potentially bidirectional, spontaneous, or if there are multiple reactions. When the path includes two metabolites connected by multiple reactions, as in the case of the middle two metabolites in the figure, the number of reactions is listed, and the user can select which is displayed. The diagram is customizable, allowing the user to select which display elements should be visible. Figure Figure The result is a tool that enables easy, interactive browsing of a portion of the metabolic network. Unlike other more general network visualization tools, no prior preselection or preloading of a sub-network is required beyond the initial selection of the database of interest. Limiting the tool to showing only a single linear path at a time allows for a compact, easy-to-read, aesthetically appealing display, but full predecessor and successor metabolite lists visible for every metabolite in the selected path make it easy to identify and explore alternate paths. Ready access to detailed information pages for the reactions, pathways and enzymes associated with the current path provides important context.The Metabolic Network Explorer is a new web tool that enables interactive generation and display of small metabolic subnetworks of interest without being limited to predefined pathways. Its primary strengths are its ease of use, diagrams that are intuitive to biologists, and its integration with the broader corpus of data provided by a PGDB. It complements other tools for interacting with metabolic networks and fills an important gap in the set of metabolic network visualization tools.