Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All the implemented features are provided as part of the Glycosylation Network Analysis Toolbox (GNAT), an open-source, platform-independent, MATLAB based toolbox for studies of Systems Glycobiology. Glycosylation is among the most common post-translational modifications in nature. A vast majority of cell-surface and secreted proteins in mammalian cells bear glycans Unlike nucleic acids and proteins which are predominantly composed of template-driven linear polymeric structures, glycans are often composed of branched structures. Due to this complexity, a heterogeneous population of carbohydrate structures may be found at a single protein site. Thus, instead of having a uniform chemical structure, the glyans added to proteins are composed of a group of chemically related entities or ‘glycoforms’ There has been a large effort in the Glycomics field over the last decade to capture the complexity of the glycosylation process using high throughput analytical experimental methods, notably mass spectrometry in silico is an important step that can enable the quantitative analysis of biochemical experimental data. In this regard, the manual construction of such networks can be tedious and error-prone, particularly when the network size is large. Recent efforts have attempted to address this problem Enzyme class that integrates glycosyltransferase and glycosidase enzymatic data from the IUBMB  database and substrate specificity information from a variety of sources , enzyme names , and reaction description  and hydrolase (HlEnz) are subclasses of Enz. They inherit properties from Enz. Additionally, in order to describe the reaction type catalyzed by these enzymes, as shown below, these subclasses include fields that store donor, acceptor, donorprod and acceptorprod in the case of TfEnz and prod_h and prod_oh in the case of HlEnz.The transferase (TfEnz) reaction: Transferase (HlEnz) reaction: Hydrolase  format in GTEnz and GHEnz classes can be viewed in the source code or GNAT user manual. Detailed documentation about these classes can be also accessed using the help or doc commands provided in MATLAB. Note that enzyme rate constants required for kinetic modeling have to be user-defined based on experiments or prior literature. These are not automatically populated by GNAT.by GHEnz . This fiGTEnz) and glycosidase (GHEnz) class allows automated single-reaction ‘product/substrate inference’  are formed in a given step for the set of enzymes specified in the system. All newly generated molecules and reactions generated in this manner are incorporated into the reaction GTEnz and GHEnz classes. The algorithm initially chooses a pair of glycans. For this pair, based on the enzymes present in the system, the program designates one of the molecule to be the ‘initial substrate/starting material’ while the other is the ‘final product’. For the initial substrate-final product pair, ‘substrate inference’ is applied in a step-wise manner in order to determine all possible reaction pathways that link the final product to the initial substrate. For m glycans provided as input, the above steps are repeated m×(m-1)/2 times. Finally, all the reactions and species are consolidated into the Pathway object by removing duplicate reactions and species.‘Connection network inference’ joins two or more input glycans using enzyme-substrate specificity data defined in the annotation field of the Species element as shown below:Pathways generated by the above algorithms are exported in System Biology Markup Language (SBML) format files <species>…<annotation>http://www.eurocarbdb.org/recommendations/encoding”>  <glycoct xmlns = “   <sugar version = “1.0”>    <residues>    …    </residues>   <linkages>   …   </linkages>  </sugar> </glycoct></annotation>…</species>http://www.jgraph.com/) and GlycanBuilder The constructed pathway can be graphically visualized using the glycosylation network visualizer (GNV), a visualization component of GNAT. GNV is a Java-based library that uses JGraph to display network structures , the systematic removal of user-specified number of species results in a set of smaller reaction networks where the specified number of species has been randomly deleted. In the second and third methods, user-specified species are either deleted using the subnetgenbyspecdel function or retained using the subnetgenbyspeckeep function. These methods represent alternate strategies to generate subset reaction networks.ii. A set of ‘subset generation’ functions enable the dynamic synthesis of reaction networks. These functions construct small reaction networks by extracting portions of the larger ‘master’ reaction network. In the first method  and intensity data from.msd format files into the computational environment. The peak selection function then converts the raw m/z vs. intensity data into a list of peaks and their corresponding half height width column data using a four-step method. These steps include background adjustment, data normalization, noise removal, and peak finding. Peaks identified in this manner can be related to specific glycans using algorithms described elsewhere Two basic MS functions are implemented in GNAT in order to read raw glycomics experimental data and preprocess them in order to identify MS peaks corresponding to the glycans. To this end, the raw data processing function enables loading of raw mass/charge (GTEnz and GHEnz classes within the GNAT environment enables the systematic creation of enzyme databases that can collate enzyme specificity data available in literature The definition of the X) GlcNAc) attached to a core-2 structure on PSGL-1 is a key glycan that initiates this cellular attachment. Glycosylation reaction network analysis can be performed to study the biosynthetic routes regulating the formation of this glycan in order to define key rate-limiting steps O-linked glycans attached to the leukocyte cell surface molecule P-selectin glycoprotein ligand-1 (PSGL-1) bind adhesion molecules belonging to the selectin family inferGlyConnPath in GNAT) (in silico (shown in red box) that are predicted to exist based on enzyme specificity rules.Biochemical studies have identified twelve possible O-glycans on PSGL-1 . Among tin GNAT) . The patX bearing glycan number-9. This smaller pathway, which is a subset of the larger pathway shown in pathfinding function of GNAT.in silico. In this regard, GNAT provides a range of functions to generate subset pathways by either eliminating a fixed number of species, retaining or deleting specified species. During this process, any reactant(s) that is not connected to the remaining pathway are determined using the detectIsolatedSpecies method of GNAT and they are deleted using removeIsolatedSpecies. In the O-linked reaction pathway, deleting between 1–10 species using the subset network generation function generated a set of subset pathways. These subset pathways all start with the core-2 trisaccahride glycan 0 and contain at least one of the sLeX species 5 or 9. Further, all ‘intermediate’ structures generated in silico  in metazoans, to asparagine residues on nascent proteins in the endoplasmic reticulum (ER) MGAT1-5. Branching by these enzymes and additional terminal modifications result in the formation of a variety of carbohydrate structures. The end products of such synthesis include three types of structures called oligomannose, hybrid and complex N-glycans, each containing the common Man3GlcNAc2 core structure. In this regard, oligomannose glycans have only mannose residues attached to the core. Some branches of hybrid N-glycans are mannose rich while others are extended by the addition of GlcNAc. Complex glycans have GlcNAc attached to all antennae.The biosynthesis of N-linked glycans is initiated by the inferGlyForwPath) is used here to construct the reaction network. targetBranch property of these enzymes is often populated in the enzyme class, since it helps define the exquisite substrate specificity of the glycosyltransferases. In addition, the linkage specificity of Man II is described such that it can release mannose residues from both α1,3 and α1,6 linked glycans  was applied to construct a relatively large N-linked glycosylation pathway with 360 reactions and 188 species (pathfinding function to isolate the network connecting the GlcNAcMan3GlcNAc2 glycan to the tri-bisecting antennary glycan (m/z = 2937.9).Considering the presence of 9 enzymes, GnT I to GnTV, Man II, SiaT, FucT and GalT, ‘connection network inference’ ( species . The staGTEnz) and glycosidases (GHEnz). Using this and related algorithms, it is possible to construct glycosylation networks starting from either conventional biochemical or mass spectrometry based experimental data. Once constructed, a variety of network analysis strategies can be implemented to analyze components of the overall glycosylation network using graph theory based concepts, like the ‘subset modeling’ and ‘path finding’ algorithms.We present a computational framework that can automate the construction of glycosylation reaction networks using streamlined enzyme definitions. Specifically, the new methods implement machine-readable definitions for glycosyltransferases (The network construction and analysis algorithms described here are implemented in GNAT, the only toolbox currently available for the analysis of biochemical pathways related to the field of Glycobiology. In this regard, GNAT is a MATLAB-based, open-source, platform-independent computational toolbox for Systems Glycobiology GNAT was originally designed with the goal of providing a strategy to integrate glycan structure information into SBML format files that describe biochemical reaction pathways. In particular, a variety of object oriented concepts were implemented to modularize the programming structure. By integrating JAVA based visualization suites, namely JGraph and GlycanBuilder, the core functions of GNAT allow high quality visualization of glycosylation reaction networks. In addition, since GNAT is implemented in the MATLAB environment, the software can be readily enhanced by writing additional.m scripts and plugs-ins.Enz and its sub-classes. These new definitions enable the construction of general enzyme databases that can be called by multiple.m scripts. While the databases that we have developed for the current manuscript contain 14 enzymes, it is conceivable that this can be expanded in the future to include the specificity of all 200+ enzymes that regulate glycan biosynthesis Whereas the original GNAT software contained basic functions that are listed in the center box of The goal of the current manuscript is to provide a package that can systematically automate the construction of glycosylation pathways since the manual construction of such large networks can be error-prone. In this regard, the systematic definition of enzymes in the modeling framework can allow the generation of cell-specific and protein-specific reaction network models. Once constructed, a variety of routines are available in MATLAB for the deterministic and stochastic simulation of such networks. Sophisticated optimization methods are also available for the purpose of experimental data fitting. Complex post-simulation analysis can also be performed in this environment with the goal of generating experimentally testable hypotheses. Depending on the specific biological questions being addressed, such analysis is feasible both for the quantitative simulation of features regulating glycan expressions on the cell-surface and for the analysis of glycan heterogeneity on specific proteins. Enzyme definitions and rate constants for such pathway generation have to be curated from literature. Many examples of such glycosylation network analysis are available in literature and facilities are available in GNAT to curate these. Examples include, but are not limited to, mathematical models of N-linked glycosylation branching in Chinese Hamster Ovary cells Enz definition in GNAT may be modified in order to incorporate peptide backbone effects on glycosyltransferase activity and specificity.The range of problems that can be studied using the computer framework described in the current manuscript is large, and a few examples are provided below for the sake of completion. First, the cellular glycome depends on the sub-cellular location of individual enzymes, specific enzymatic activity, substrate availability and sugar-nucleotides levels. The effect of each of these features can be studied in GNAT by either specifying the compartment where specific enzymes are located, varying their rate constants or by altering the concentration of reactants and sugar-nucleotides during simulations. Second, beyond considering glycomics based datasets, the computational framework can also be extended to capture genomics, transcriptomics and metabolomics based data that describe changes in cellular gene expression and sugar-nucleotide synthesis. Such advancements can be implemented using the MATLAB scripting language and the resulting findings could be relevant to metabolic disorders and also cancer. Third, the in silico, using both conventional biochemical and high-throughput MS experimental data. The implementation of these algorithms in GNAT provides an open-source computational framework for the generation of experimentally testable hypotheses.In conclusion, the streamlined enzyme definitions enable the rapid construction of glycosylation reaction networks


Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily.This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’  known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering. With the development of quantitative functional genomics approaches, it has become possible to analyse the cellular adaptation of cell physiology to altered environmental conditions experimentally, by monitoring changes in fluxes, metabolites, proteins or mRNAs. Such adaptations tend to occur at multiple regulatory levels if not simultaneously, then subsequently, depending on the time scales of observation-3. In prThe adaptive changes of reaction rates through metabolic and genetic regulation are usually due to feedback and/or feed-forward mechanisms. In biology, there is a perception that evolutionary optimization has made these mechanisms perfect. If this were so, this would suggest that such mechanisms might be identical to ‘perfect’ regulatory mechanisms designed by control engineering. Indeed,Control engineering has examined which network structures may make adaptation of a network upon a sustained perturbation of a network component, ‘perfect’. Perfection was defined as the phenomenon that some important system variables (known as ‘controlled variables’) should be completely robust to the perturbations, i.e. with steady states values unaffected by the perturbations. Such perfect robustness can be achieved when a time integrator is applied to any variation of the controlled variable (or system error). This control feature is known as ‘integral control’. Through this time integral, the network would continue to change until the controlled variable is restored completely to its initial value. Because there must be some compensation for the perturbation, a different system variable then has to move away from its initial state. This so-called ‘manipulated variable’ is non-robust (fragile) to the perturbation, but enables the controlled variable to be robust.If the control action is proportional to the variation of the perturbed variable itself, or a function thereof that is zero when that variation equals zero, the ultimate deviation of the controlled variable from its value before the perturbation, will be nonzero. This is the so-called ‘proportional control’ of control engineering. Perturbations may also result in a sustained oscillation of the controlled variable and to prevent this from happening, the third type of control focused on by control engineering can be useful, i.e. so-called ‘derivative control’ , which will not be discussed in this paper, but has been exemplified in reference.As mentioned above, the mechanism of integral control is often referred to as ‘perfect adaptation’. Other authors have referred to similar network behaviour that was not based on the same integrative mechanism by the same phrase of perfect adaptation. One such case is that all steps in a metabolic pathway are regulated identically, i.e. their activities being modulated by the same factor. Tyson et al. referredIn this work, we shall try to bridge two rather unconnected approaches in analysing regulation of network properties. The one is that of control engineering which has devised networks structures that lead to perfect or imperfect adaptation. The other is that of biochemistry with MCA and supply–demand theory, as well as true-to-life examples of intracellular biochemical networks involving both metabolism and gene expression. We shall focus on pathways synthesizing precursors for macromolecule synthesis  in which that precursor often inhibits an enzyme early in the pathway, both directly and through gene expression. Such end-product regulatory structures allow for some simplifications. This maIn this section, we demonstrate that the unique steady state of a metabolic network under regulations can be analysed by both the kinetics-based analysis and by metabolic  control analysis. A hierarchical supply–demand theory linking hierarchical control analysis with classical supply–demand analysis, is developed for when gene-expression regulation is active. As a result the steady state properties of a metabolic network subject to various regulatory mechanisms can be analysed within a unified theoretical framework.B. subtilis + [ADP]) because the reactions only convert the one into the other. The metabolic regulation addresses the interplay between the supply and demand processes (s and d).Let us consider the simple example given in Figure E are here assumed to follow mass action kinetics:The dynamics of ADP and enzyme kb = 0) or dependent  on the enzyme concentration. The first order degradation rate reflects the assumption that there is a rather unspecific protease activity for which the particular enzyme E we are considering here is a minority substrate. The zero order degradation would reflect a case where there is a specific protease system for enzyme E (e.g. an ubiquitination followed by a generic protease) that is saturated by the already high concentration of the enzyme relative to the KM of the ubiquitin transferase. In (17) all rate constants are considered non-negative and k0 = 0 whenever E is non-positive. The closed-loop control system structure of the pathway can be represented as in Figure The degradation of the enzyme is here written as the sum of two terms, which will serve to emphasize the implication of this degradation to be independent , and reformulating the kinetics of ADP and E (see Appendix C), we haveThe control system diagram suggests that the ADP concentration is the controlled variable and the enzyme concentration X, we recognize on the right-hand side first a proportional response term, then an integral response term, and then the perturbation term. The proportional response corresponds to the direct ‘elasticity’ of the supply and demand reactions with respect to the error function δ[ADP], which is a metabolic and instantaneous regulation. The integral response is related to the protein synthesis and degradation and thus to gene-expression regulation.Comparing this to a general closed-loop control system (16), with ADP for E (i.e. when kb = 0), the gene-expression regulation becomes an ideal integral control loop, and the metabolic network can exhibit robust perfect adaptation to the external or parametric perturbations. This can be understood by requiring (18) to be valid in a steady state, i.e. with time independent values for [ADP] and E. Because the time integral reaches to infinity this requires that the argument of the integral, i.e. ka ⋅ δ[ADP] - kb ⋅ δE, must equal zero. The mechanism for this perfect adaptation is that after the perturbation the concentration of the enzyme will vary until it makes the time dependences equal zero by itself, forgoing the more usual process in metabolic regulation that changes in the controlled variable arrange for the steady state to be re-attained in the presence of the sustained perturbation.When the degradation of enzyme is zero order in terms of For the more general case where the enzyme degradation may depend on the enzyme’s concentration, the  control of the enzyme level by the demand reaction can be expressed in terms of the kinetic parameters and the steady-state ADP concentration (see Appendix C):The flux control exercised by the demand reaction is quantified by:kb. For the ideal integral control scenario of kb = 0, the enzyme concentration E tracks the activity of the pathway degrading ATP perfectly, i.e.kb ≠ 0, the adaptation of the pathway to the perturbation will not be perfect and both control coefficients are smaller than 1.Both the control of enzyme level and the control of demand flux by the perturbation equal 1 minus a hyperbolic function of vis-à-vis perturbations in the demand reaction can be expressed in terms of kinetic constants and the concentration of ADP . For the above model the elasticity coefficients assume the following magnitudes:For the robustness of the ADP concentration vis-à-vis increased demand one then finds:This shows that robustness is infinite if the enzyme degradation reaction is zero order . The corresponding conclusions pertain to the tracking of the demand by the enzyme level kcat). At this stage, the system may then undergo a second ‘adaptation’ through gene expression which should be expected to be slower because the cell has to produce enzyme, but still ultimately lead to an increase in the concentration of the first enzyme. This increase should then decrease the direct metabolic stimulation of the catalytic activity of the enzyme discussed above. In this sense, the regulation of the first enzyme of the pathway is bi-functional in dynamic terms/dt = 0 and dE/dt = 0:The steady state values of ADP and andADP]ss) and that in the absence of the perturbation; the cell has adjusted Ess and the rate constant k0 (and perhaps kd and ks) to meet this requirement. We assume that the cell will do this also at different values of kb. The implication is that if different values of kb are considered, ka is also different as defined by (36).It is assumed that cell function requires ADP concentration to be at a certain level  into (38) one finds (18). By substituting the steady state condition of Ess in (35),By integrating the time dependence of the change in enzyme level For that change in ADP level to be time independent, the integrand in the integral control term should equal zero around steady state, so that:By using this expression to eliminate the change in enzyme level from (39) for the time dependence of the change in ADP level and set the latter to zero, the robustness coefficient in (21) can be obtained. Similarly, by using (40) to eliminate the change in ADP level from (39), the control of enzyme level (19) can be obtained.The authors declare that they have no competing interests.FH and HVW jointly discussed and developed the main ideas of paper, and both authors wrote the manuscript. FH developed the hierarchical supply–demand theory, proposed the control engineering analysis of the regulatory systems, and implemented the simulation studies. HVW provided the MCA and HCA analysis, developed the first ATP metabolism example, identified the three terms in control analysis, and interpreted the results biochemically. VF contributed to the kinetic analysis of the end-product and initial-product modules. All authors read and approved the final manuscript and HVW rewrote the final version.


Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology  provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species  Optimization of algal metabolism toward improved bioproduct production while maintaining strain robustness remains a challenge that requires experimental strategies informed through systems-level analyses of metabolism. The use of metabolic network models can guide the development of optimization strategies that would be otherwise difficult through rational designs   , 95 substrate utilization assays for nitrogen sources (PM03), 59 nutrient utilization assays for phosphorus sources, and 35 nutrient utilization assays for sulfur sources (PM04), along with peptide nitrogen sources (PM06–08) were utilized. A defined tris-acetate-phosphate (TAP) medium  was used to inhibit bacterial growth in all plates. In addition, ampicillin and kanamycin were used at 50–100 μg/ml occasionally. Bacterial contamination was monitored by streaking cells on yeast extract/peptone plates and performing gram stains before and after Biolog assays. All microplates were incubated at 30°C for up to 7 days and the dye color change (in the form of absorbance) was read with the OmniLog system every 15 min. As the OmniLog instrument does not provide a source of continuous light during incubation, the algae is assumed to be carrying out heterotrophic respiration.xy and level plots, and a statistical analysis was carried out to visualize the metabolic properties and generate OmniLog values. An OmniLog value or the curve parameter “A” simply lists the maximum height of the growth curve.The Biolog PM data analysis was carried out using an OmniLog phenotype microarray (OPM) software package  was positive after subtraction from the negative control well and the respective blank well. This summation is a representation of the abiotic reaction of the dye with the media in the presence of the tested compound.C. reinhardtii (taxid: 3055). PSI-BLAST hits with E values of ≤0.05 were manually curated for relevance to the searched EC number through either the evaluation of their described enzymatic activity, or by querying those BLAST hits through EMBL-EBI PfamGene to reaction associations for compounds were established as follows: assignment of a compound’s enzyme commission number (EC) and relevant reactions were performed by searching KEGGIdentified reactions with their associated genes were added to iRC1080 using the COBRA Toolbox functions add Reaction and Change Gene Association. In addition, transport reactions for the new metabolites were incorporated into the model as transport by passive diffusion from the extracellular medium into the cytosol. The behavior of the new resultant model, iBD1106, was tested by carrying out flux balance analyses under light and dark conditions for the maximization of biomass as the objective function. The comparison of the two models was based on reported shadow prices (sensitivity of the objective function to changes in system variables) of the metabolites. The Biomass function was defined previously  and time (x-axis). In addition, the data was transformed to a heat map format to allow for a quick comparative overview of the multitude of the kinetic data. The heat map presents the kinetic values with different colors  with the iRC1080 metabolites and the metabolites measured using gas chromatography time-of-flight (GC-TOF)  and four positive assays for phosphorus utilization  were detected. C. reinhardtii showed positive results for several nitrogen sources including both l-amino and d-amino acids, and less common amino acids such as l-homoserine, l-pyroglutamic acid, methylamine, ethylamine, ethanolamine, and d,l-α-amino-butyric acid. Furthermore, a large number of dipeptides and a few tripeptides assayed positive . Detection of acetate as the only carbon source from this plate is consistent with the  plate. Dd-amino acids, tetrathionate, thiophosphate, dithiophosphate, cysteamine-S-phosphate, l-pyroglutamic acid, and ethylamine, 108 dipeptides, and 5 tripeptides. We note that sequence specificity was observed for utilization of both di- and tripeptides. The identified metabolites are summarized in Table Altogether, we identified 128 new metabolites from the PM data that were not present in our iRC1080 metabolic model: eight Chlamydomonas annotation sources for each of the metabolites. Five different sources were used to obtain genomic evidence for the reactions. These included Phytozome Version 10.0.2  were then entered into the Universal Protein Resource website (UniProt)d-amino acids, 2 l-amino acids, 108 dipeptides, and 5 tripeptides. The phosphate sources were cysteamine-S-phosphate, thiophosphate, and dithiophosphate. The only new sulfur source metabolite was tetrathionate. No genomic evidence for tetrathionate was found in databases and its PSI-BLAST E values did not pass the threshold of 0.005, thus, no reaction for this metabolite was added to the model. In addition, l-pyroglutamic acid, thiophosphate, dithiophosphate, and ethylamine were not added to model due to lack of genomic evidence.The metabolites identified as new to the network were categorized and annotated in the model based on their utilization into nitrogen sources, phosphate sources, and sulfur sources. The nitrogen source metabolites were 8 d-amino acids are oxidized into ammonium and a 2-oxo-carboxylate via the following reaction with EC number of 1.4.3.3 and associated gene Cre02.g096350.t1.3:To expand the existing model, reactions associated with the new metabolites and the genes associated with the new reactions were added to iRC1080 model to generate an expanded network, iBD1106. iBD1106 accounts for 2,445 reactions, 1,959 metabolites, and 1,106 genes Table . The newd-amino acids. However, some d-amino acids contribute to different reactions in addition to their own oxidation reactions. For example, d-serine reacts with ATP producing ADP and phospho-d-serine. Moreover, the chirality of d-amino acids can also be inverted into L forms and vise versa through annotated racemases , and one for tripeptides (Cre16.g675350.t1.3). The dipeptides and tripeptides are decomposed into their unit S-phosphate into cysteamine and phosphate was added according to the following reaction that is associated with the gene JLM_162926:With respect to sources of phosphorus, a reaction for hydrolysis of cysteamine-In order to specify the cellular compartment where the new reactions occur, we used the WoLF PSORT tool  and dark conditions (with acetate) by carrying out flux balance analyses with the biomass as the maximized objective function. To assess the contribution that each metabolite makes to the set objective function, shadow prices for all metabolites were obtained Tables . Shadow Algae are a group of diverse photosynthetic eukaryotes, which are polyphyletic in origin  , which is an organic phosphorothioate anion that is derived from deprotonation of thiophosphate OH groups and protonation of the amino group, (2) thiophosphate (or phosphorothioate), and (3) dithiophosphate, which is the product of the reaction of a base with phosphorus pentasulfide.From these newly identified metabolites, three phosphorus compounds were found: (1) cysteamine-Salmonella enterica  were found to grow better on both sulfur and tetrathionate. In the archeon Acidianus hospitalis, tetrathionate is secreted to form filaments from tetrathionate homomultimers  synthesis  has a significant and differential effect on the shadow prices of metabolites under light and dark conditions for biomass production . Also the oxidation of d-asparagine produces d-aspartate as oxo-carboxylate -pyrimidine (25dhpp). The latter can be converted into 5-Amino-6-(5′-phosphoribosylamino)uracil (5apru) in the riboflavin metabolism resulting in an excess of 4-(1-d-Ribitylamino)-5-aminouracil (4r5au) and 5aprbu, with shadow prices of 0.168 and 0.158, respectively. Those results were not observed in iRC1080.Under light growth, the d-serine reactions in iBD1106. Addition of d-serine limited the availability of the metabolite 1-(9Z)-octadecenoyl,2-(11Z)-octadecenoyl-sn-glycerol-3-phosphate (pa1819Z18111Z) (shadow price −0.009 in iRC1080 and −0.65 in iBD1106). This metabolite is produced and consumed by the reactions of glycerolipid metabolism for the production of Palmitoyl-CoA (n-C16:0CoA) (pmtcoa). The addition of l-serine in iBD1106 results in more consumption of pmtcoa in the sphingolipid metabolism through the reaction serine C-palmitoyltransferase (SERPT) that produces 3-dehydrosphinganine.Another example of model discrepancy under light growth is the effect of adding d-histidine and d-glutamate dipeptides hydrolysis reactions, e.g., Ala–His, and inversion into l-histidine and l-glutamate through a racemase. This addition increases the consumption of l-glutamate and l-histidine along with 4-aminobutanoate in glutamate and arginine and proline metabolisms, respectively. Moreover, the dark growth condition did not affect the behavior of 4-aminobutanoate significantly in iBD1106; however, in iRC1080 it was shifted from a limiting metabolite (−0.07) into an excess metabolite (0.18) . The wealth of phenotypic data can be used along with other references to compare organisms with known mutants or unknown isolates. This wealth of information will also shed light on new and novel metabolic pathways. The substrate utilization information and the newly identified metabolites were used for metabolic network expansion and refinement of the iRC1080 metabolic model. The study also provides a framework to bridge the missing links between genomics and metabolomics in microalgae. The described work provides an excellent method for the initial characterization of newly isolated or uncharacterized strains of algae. This combination of high-throughput phenotypic screening with metabolic modeling can allow for rapid refinement of existing metabolic network models as demonstrated and also provides biochemical evidence to support de novo reconstruction of new algal models.Phenotypic profiling has tremendous utility in modeling and understanding algal metabolism and is essential in elucidating genotypic differences in algae and the effects of environmental conditions on metabolism. The method presented here demonstrates the first reproducible study utilizing PM assays in profiling microalgae using 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.http://www.frontiersin.org/Journal/10.3389/fbioe.2014.00068/abstractThe Supplementary Material for this article can be found online at Data Sheet S1:Table S1PM assays for PM01 to PM04 and PM06 to PM08, positive assays are shaded with yellow.Click here for additional data file.Table S2A table of new metabolites that were added to generate iBD1106 along with their genetic annotations.Click here for additional data file.Table S3Shadow prices for metabolites of iRC1080 and iBD1106 when biomass was set as objective function under growth with light and no acetate.Click here for additional data file.Table S4Shadow prices for metabolites of iRC1080 and iBD1106 when biomass was set as objective function with growth under dark with acetate.Click here for additional data file.Table S5Shadow prices for new metabolites in iBD1106 when Biomass was set as the objective function (growth under light without acetate and under dark with acetate).Click here for additional data file.Data Sheet S2Chlamydomonas reinhardtii metabolic network model, iBD1106, in SBML format.Click here for additional data file.Figure S1Phenotype microarray results for plates 1–4, and 6–8.Click here for additional data file.


Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown. Recent advances for system-wide experimental data generation have facilitated the quantification of signaling systems, but the integration of multi-level dynamic data remains challenging. Here, we co-designed dynamic experiments and a probabilistic, model-based method to infer causal relationships between metabolism, signaling, and gene regulation. We analyzed the dynamic regulation of nitrogen metabolism by the target of rapamycin complex 1 (TORC1) pathway in budding yeast. Dynamic transcriptomic, proteomic, and metabolomic measurements along shifts in nitrogen quality yielded a consistent dataset that demonstrated extensive re-wiring of cellular networks during adaptation. Our inference method identified putative downstream targets of TORC1 and putative metabolic inputs of TORC1, including the hypothesized glutamine signal. The work provides a basis for further mechanistic studies of nitrogen metabolism and a general computational framework to study cellular processes. We tested the consistency of our dataset across experiments by principal component analysis of transcript dynamics for the 909 metabolic enzymes , prototypic interaction classes (network motifs), and approximations of biological control mechanisms (dynamic dependence). We expect that our approach for model-based data integration will extend to other (nutrient) signaling networks because, while the formalization of the prior knowledge is application specific, the approach itself is generic: target sets, network motifs, and temporal relations of events and variables could be formulated analogously for other applications. Note, however, that the approach may not be applicable to existing datasets in retrospect: a co-design of experiments is critical, for example, because equivalent perturbations of signaling via natural inputs and drugs  are needed for the inference approach. Another possible limitation concerns the availability of prior knowledge on, for example, transcriptional regulation. Here, we employed only experimentally documented interactions between TORC1-controlled transcription factors and target genes, but already general sampling biases in the well-characterized yeast transcriptional network could lead to biased inference results. However, our robustness analysis indicates that such confounding factors have less severe effects on prediction accuracy than limited biological knowledge. In addition, the approach requires a careful definition of the “prototypic” motifs  to improve resolution and confidence of inferred causal relationships. For example, the structure of a metabolic network could provide constraints because any change in a metabolite concentration should be preceded by changes in educt concentration(s) in the corresponding reaction(s). In terms of experimental data, constant protein abundances allowed us to focus on metabolite and transcript dynamics. With more detailed dynamic measurements, such as (phospho-)proteomics data, it should become possible to unravel regulatory interactions across multiple network types and timescales, addressing one of the most prominent current challenges for data integration in complex cellular networks. Provided that the topology of transcriptional or phosphorylation regulation networks, or substantial parts thereof, is known, the presented approach is also applicable to regulation in healthy and diseased higher cells.S. cerevisiae strain YSBN6  of 0.03–0.08, and grown at 30°C, pH 5, impeller speed of 1,000 rpm, and sparging with 3 l air per min. Growth was monitored by OD600 measurements and the concentration of CO2 in the reactor exhaust gas stream. The yeast minimal medium (YMM) contained (per liter): 20 g d-glucose, 5 g K2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, 15 mg EDTA, 4.5 mg ZnSO4·7H2O, 0.3 mg CoCl2·6H2O, 1.0 mg MnCl2·4H2O, 0.3 mg CuSO4·5H2O, 4.5 mg CaCl2·2H2O, 3.0 mg FeSO4·7H2O, 0.4 mg NaMoO4·2H2O, 1.0 mg H3BO3, 0.1 mg KI, 0.05 mg biotin, 1.0 mg Ca-pantothenate, 1.0 mg nicotinic acid, 25 mg inositol, 1.0 mg pyridoxine, 0.2 mg p-aminobenzoic acid, 1.0 mg thiamin, and 10 mM potassium hydrogen phthalate buffer (pH 5). Nitrogen source supplementation was as followed : “preculture-YMM” with 1 g/l of either l-glutamine or l-proline ; “downshift-YMM” with 500 mg/l of l-glutamine and 250 mg/l of l-proline; “rapamycin-YMM” with 1 g/l of l-glutamine; and “proline-YMM” with 200 mg/l of l-proline. All experiments were performed in three independent biological replicates.Throughout we used the prototrophic 600 = 1.07 ± 0.24 , still in exponential growth, glutamine was instantaneously added into the bioreactor to a final concentration of 400 mg/l. The time of glutamine addition is referred to as the “time of shift”.The initial culture was inoculated in “proline-YMM”. At OD600 of 0.84 ± 0.02 .The initial culture was inoculated in “downshift-YMM”, containing both glutamine and proline as nitrogen sources. At appropriate intervals, the concentration of glutamine in the broth was monitored by using at-line direct flow injection MS analysis on an Agilent LC-QTOF system, allowing readouts within < 2 min after sampling. The time at which glutamine was not detected anymore by direct flow injection MS is referred to as the “time of shift”, at an OD600 = 0.85 ± 0.08 , still in exponential growth, rapamycin was instantaneously added into the bioreactor to a final concentration of 400 μg/l. The time of rapamycin addition is referred to as the “time of shift”.The initial culture was inoculated in “rapamycin-YMM”. At OD600 and extracellular concentrations of glucose, glutamine, proline, ethanol, glycerol, acetate, and pyruvate (Supplementary t0), since concentrations at this time scale can be assumed as constant. The high sampling frequency following the shift was enabled by independent sampling ports, installed at the bottom of the reactor, specific for each measurement type. Sampling for intracellular metabolomics was performed by an automated sampling device with a vacuum pump to maintain a rapid and reliable sampling frequency.Throughout the cultivation, we monitored OD600 in a spectrophotometer. The correspondence to dry cell weight (DCW) for the YSBN6 strain growing in minimal media was initially determined to be 0.463 gDCW/l/OD600. Extracellular concentrations of glucose, ethanol, acetate, glycerol, succinate, and pyruvate were determined with an HPLC system (Agilent HP1100), equipped with a polymer column (Aminex HPX-87H from Bio-Rad)  were dried completely in a vacuum centrifuge . The extracts were resuspended in water before analysis.Culture broth was quickly quenched into a tube containing (1:4 v/v) 60% (v/v) methanol buffered with 10 mM ammonium acetate (pH 7.5), precooled at −40°C, and pelleted by centrifugation. Intracellular metabolites were extracted from the pellet with 1 ml 75% (v/v) ethanol buffered with 10 mM ammonium acetate pH 7.5, at 80°C for 3 min. Samples for LC-MS/MS were supplemented with 50 μl et al  operated in the negative ionization mode. High-precision mass spectra were recorded from 50 to 1,000 in vitro transcribed in the presence of biotin-labeled nucleotides using GeneChip® 3′ IVT Express Kit . The quality and quantity of the biotinylated cRNA were determined using NanoDrop ND 1000 and Bioanalyzer 2100.To cover the entire dynamics for transcriptome analysis, 14 or 15 samples were processed per nutritional shift from one representative bioreactor experiment (Supplementary Biotin-labeled cRNA samples (15 μg) were fragmented randomly to 35–200 bp at 94°C in fragmentation buffer  and were mixed in 300 μl of hybridization mix  containing hybridization controls and control oligonucleotide B2 . Samples were hybridized to GeneChip® Yeast 2.0 arrays for 16 h at 45°C. Arrays were then washed using an Affymetrix Fluidics Station 450 FS450_0001 protocol. An Affymetrix GeneChip Scanner 3000 was used to measure the fluorescent intensity emitted by the labeled target. The raw CEL files were normalized using R/Bioconductor, Affy package . The resulting dataset with one intensity per ORF per time point per experiment was the final working dataset. For clustering and PCA analysis, all 25 CEL files comprising the three complete time series  were normalized together, allowing the relative comparison of transcripts across all experiments. For statistical dependency analysis, all CEL files from each specific shift, including the replicates, were normalized together , allowing adequate statistical treatment for each shift. Raw data are deposited in NCBI GEO under the accession number GSE54852.15N stable-isotope labeled protein reference yeast extract was spiked into all samples. After reduction, alkylation and trypsin digestion peptide mixtures were purified using C18 cartridges, and all protein samples were assayed in biological triplicates.Protein samples were taken by addition of trichloroacetic acid directly to the yeast culture, followed by cell lysis using glass beads beating in condition of 8 M urea. As internal standard, a S. cerevisiae: (i) A proteome-wide mass-spectrometric map generated from synthetic reference peptides  and (ii) a yeast ion-trap consensus spectral library from the National Institute of Standards and Technology (NIST) . Relative protein quantification was carried out based on the ratio between summed transition areas for light (endogenous) and heavy (15N reference) peptide form. For proteins quantified with several peptides the average peptide ratio was used to compute the protein ratio. The quantitative SRM dataset has been deposited to the public repository “Panorama” and is accessible via the link https://daily.panoramaweb.org/labkey/project/Aebersold/ludwig/BigY_SRMdata/begin.view.SRM measurements were performed on a TSQ Vantage QQQ mass spectrometer (Thermo Fischer Scientific) equipped with a nano-electrospray ion source and a nano-LC system (Eksigent). For all targeted proteins, proteotypic peptides and optimal transitions for identification and quantification were selected based on two high-quality SRM assay repositories of et al, PCA was performed with the R-package pcaMethods (method ppca) on the entire transcript set , metabolic gene set [909 transcripts encoding for metabolic enzymes described previously . Model selection and statistical validation was performed with the principle of Approximation Set Coding to determine the most predictive clustering and the number of clusters (Chehreghani Depending on how we computed the “dynamic dependence” (see below), we either used interpolated trajectories of the average  response of each metabolite in every shift, or we interpolated each biological replicate independently for every shift. In both cases, we used two interpolation techniques: (i) linear interpolation, and (ii) cubic smoothing spline interpolation. We interpolated the experimental time points between 0 and 10 min of the metabolic response. To estimate the rate of change of each transcript in time , we interpolated each transcriptional time course  with cubic smoothing splines and differentiated the resulting polynomials. All the interpolated curves were sampled every minute to establish sampled datasets.et al, th min of the transcriptional response). Hence, for each pair of metabolite–transcript, we computed 11 values  of dynamic dependence for each experimental case. To achieve robust results, we used three definitions of pairwise correlations: (i) average trajectories: average metabolite samples  and transcript samples in time define two one-dimensional feature vectors; (ii) individual trajectories, variant 1: the one-dimensional vector of metabolite responses is constructed from all biological replicates; and (iii) individual trajectories, variant 2: the metabolite and transcript vectors are both three-dimensional (for the replicates). With these three variants, two versions of the interpolated metabolite data, and four different levels of variance for the Gaussian noise, we computed the dynamic dependence for each metabolite–transcript pair by 24 different methods. Finally, we selected the 10% of the transcripts with the highest maximum distance correlation value  to define a set of genes associated with a metabolite . The “dynamic dependence” feature value of a metabolite used for network assignment was defined as the mean value of the distance correlation values of the SSG of this metabolite.To compute the dynamic dependence between every metabolite–transcript pair for each experiment, we employed the sampled version of distance correlation  is a likelihood that represents the probability that the data are produced under the assumption of the motif M, P(M) is the prior value of the motif M , and P(D) is the evidence of the data. We assumed uniform priors for each metabolite-network assignment such that the Bayes factor Bij is given by:x) from the median of the random set distribution is computed only when eCDF(x) > 0.5. If eCDF(x) < 0.5, then the eCDF distance equals zero. Note that we employed two ways to compute the likelihood of assigning a metabolite to a network motif M: (i) by accounting for the likelihood in all three conditions, and (ii) by accounting for the likelihood values of the rapamycin downshift and of the nutrient shift with the highest likelihood value. Finally, by computing the values of:Given the computed values for the features of “dynamic dependence” and of “representation of TOR genes” for each experimental condition, we employed Bayes factors . For a metabolite, the frequency of assignment to a network motif is defined as the fraction of the methods that support the metabolite-network assignment with more than 50% probability. Note here that although the probabilities of the network assignments always sum up to one, this does not apply to the frequencies of assignments. When, for a metabolite, the assignment probabilities (computed with a particular method) are identical for the four network motifs (i.e. 25%), then this method will increase the frequency of “no network motif”.N = 100 samples each. We call “assignment robustness” the fraction of perturbation sets in which a metabolite is assigned to the same network motif as in the unperturbed case. Supplementary The only prior knowledge we use for the analysis is the set of transcription factors (TFs) that, according to the literature, are direct targets of TORC1 (11 TORC1-TFs). This set of TFs influences directly the values of the feature “representation of TOR genes”, which in turn affects the motif frequency value , and hence the final assignment of a metabolite to a network motif via the dominant motif frequency. We performed two types of perturbations to the TORC1-TFs set: (i) We removed all possible combinations of one (−1), two (−2), or three (−3) TFs and recomputed the assignments of the metabolites to network motifs for all possible TF subsets . (ii) We added one (+1), two (+2), or three (+3) TFs to the TORC1-TFs set by random selection from the yeast TFs not known to be targets of TORC1, with http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54852). Each individual shift experiment can alternatively be retrieved under accession numbers GSE54844 (N-upshift), GSE54850 (N-downshift), and GSE54851 (rapamycin-induced downshift). The protein quantitative SRM dataset is deposited in the public repository Panorama and accessible via the link https://daily.panoramaweb.org/labkey/project/Aebersold/ludwig/BigY_SRMdata/begin.view. All data were processed as described in Materials and Methods. The Matlab code for the network inference method is provided as Supplementary File S1.The raw gene expression data comprising all three shift experiments are deposited in the NCBI Gene Expression Omnibus (GEO) under the accession number GSE54852 (


Aerobic glycolysis or the Warburg Effect (WE) is characterized by the increased metabolism of glucose to lactate. It remains unknown what quantitative changes to the activity of metabolism are necessary and sufficient for this phenotype. We developed a computational model of glycolysis and an integrated analysis using metabolic control analysis (MCA), metabolomics data, and statistical simulations. We identified and confirmed a novel mode of regulation specific to aerobic glycolysis where flux through GAPDH, the enzyme separating lower and upper glycolysis, is the rate-limiting step in the pathway and the levels of fructose  bisphosphate (FBP), are predictive of the rate and control points in glycolysis. Strikingly, negative flux control was found and confirmed for several steps thought to be rate-limiting in glycolysis. Together, these findings enumerate the biochemical determinants of the WE and suggest strategies for identifying the contexts in which agents that target glycolysis might be most effective.DOI:http://dx.doi.org/10.7554/eLife.03342.001 Cells generate energy from a sugar called glucose via a process called glycolysis. This process involves many enzymes that catalyze 10 different chemical reactions, and it essentially converts glucose step-by-step into a simpler chemical called pyruvate.Pyruvate is then normally transported into structures within the cell called mitochondria, where it is further broken down using oxygen to release more energy. However, in cells that are rapidly dividing, pyruvate is converted into another chemical called lactate—which releases energy more quickly, but releases less energy overall. Cancer cells often convert most of their glucose into lactate, rather than breaking down pyruvate in their mitochondria: an observation known as the ‘Warburg effect’. And while many factors affect how a cell releases energy from pyruvate, it remains unclear what regulates which of these biochemical processes is most common in a living cell.In this study, Shestov et al. have developed a computational model for the process of glycolysis and used this to investigate the causes of the Warburg Effect. The model was based on the known characteristics of the enzymes and chemical reactions involved at each step. It predicted that the activity of the enzyme called GAPDH, which carries out the sixth step in glycolysis, in many cases affects how much lactate is produced. This suggests that this enzyme represents a bottleneck in the pathway.Next, Shestov et al. performed experiments where they used drugs to block different stages of the glycolysis pathway, and confirmed that the GAPDH enzyme is important for regulating this pathway in living cancer cells too. In these treated cells, the levels of a chemical called fructose-1,6-biphosphate (which is made in a step in the pathway between glucose and pyruvate) were either very high or very low. Shestov et al. proposed that the flow of chemicals through the glycolysis pathway is controlled by the GAPDH enzyme when the chemicals used by the enzymes upstream of GAPDH in the pathway  are plentiful. However, if these chemicals are limited, other enzymes that are involved in earlier steps of the pathway regulate the process instead.The findings of Shestov et al. reveal that the regulation of glycolysis is more complex than previously thought, and is also very different when cells are undergoing the Warburg Effect. In the future, these findings might help to identify the types of cancer that could be effectively treated using drugs that target the glycolysis process, which are currently being tested in pre-clinical studies.DOI:http://dx.doi.org/10.7554/eLife.03342.002 KRAS, PIK3CA, and cMYC and microenvironments such as hypoxia and hypoglycemia that promote aerobic glycolysis but a complete understanding of the necessary and sufficient biochemical alterations associated with this phenotype is unknown. Furthermore successful translation for biomedical applications is limited by understanding the contexts in which therapies that target glycolysis might be effective.Proliferating cells increase their glucose consumption and secrete lactate as opposed to completely oxidizing the glucose in the mitochondria  and is kComputational modeling has a successful history in the study of metabolism . Genome-Therefore, we collected and integrated multiple forms of data into a modeling framework involving flux balances of glycolysis, detailed chemical kinetics based on reaction mechanisms and parameters measured, physico-chemical constraints from thermodynamics and mass conservation, metabolic control analysis, and Monte Carlo sampling of parameter space. We next use mass spectrometry and isotope tracing to probe concentrations and fluxes through the pathway and their responses to several perturbations. Together, we elaborate the determinants of aerobic glycolysis and identify and confirm novel points of regulation in glycolysis that have remained unidentified for over 50 years since the discovery of the pathway.We investigated the kinetics of the glycolytic pathway from glucose uptake to oxidation of pyruvate in the mitochondria or export of lactate out of the cytosol. We modeled each step of the pathway according to enzymatic mechanism and known modes of allosteric control resulting in a set of differential equations . While iSince glycolysis is the most extensively studied biochemical pathway, there is a wealth of information on the kinetic parameters and enzyme expression that govern the equations. Nevertheless, it is also not possible to capture cellular physiology in any biochemical model with single values of kinetic parameters . This di+, and NADH and energy-associated metabolites ATP, ADP, and AMP. Next, the model is constrained to expression data so that protein concentrations are subsumed in the Vmax values and chosen from typical concentrations in cancer cells, measured kinetic parameters, and measured concentrations of nutrients such as glucose, oxygen, and total intracellular adeno-nucleotide concentration.First the model is constrained using mass conservation constraints that conserve the balance of glucose, redox state, and energy status. Next, thermodynamic constraints are used to constrain fluxes according to the free energy of the reactions determined by Haldane relationships. These physical constraints are combined with the kinetic mechanisms that define each step of glycolysis and the chemical reactions involving the redox-associated metabolites NAD+ fluorescent reporter  are rejected. In each simulation, concentrations, fluxes, metabolic control coefficients, and thermodynamic quantities are computed and recorded. This statistical analysis explores the space of glycolysis in the context of the Warburg Effect. By assessing the statistics, inferences can be made on the determinants of the Warburg Effects and its context dependence to pharmacological intervention and nutrient environment. For experimental confirmation of the model, we first utilized a recently developed a NADH/NADreporter  and measrivation . This varivation . The dis+ levels and NADH/NAD+ redox potential consistent with those observed experimentally  provides a mathematical framework for evaluating the extent that a change in metabolic activity affects a given flux. In MCA, a pure rate-limiting step occurs when the flux control coefficient (FCC) (Supplementary Information) is one at that step and zero at all other steps. In most cases, values of FCCs are distributed with gradual values across a pathway. We carried out a metabolic control analysis using MCA within our statistical algorithm to investigate the influence of each node in glycolysis on lactate flux across the ensemble of statistical realizations of glycolysis . We firsTo further investigate the contexts in which steps along glycolysis can be limiting, we correlated the FCCs with one another and carried out a hierarchical clustering that revealed modules of co-occurring flux control . It was Surprisingly, in several instances, negative flux control is observed for several enzymes that have been previously thought to be the rate-limiting steps. Negative flux control implies that inhibiting the enzyme would actually increase the rate of flux of glucose to lactate. Given the surprising findings on flux control in glycolysis, we sought to experimentally investigate these predictions. We therefore considered systematic perturbations to glycolysis in cancer cells.13C isotope tracing from glucose -bisphosphate (FBP) levels exhibited highly dynamic and counterintuitive behavior with each drug perturbation. An analysis of metabolite levels across a series of 14 conditions in triplicate involving pharmacological perturbations of PFK2, LDH, and GAPDH at different concentrations and two separate vehicle treatments  revealedTogether, the combined computational model, metabolite profiling, and flux analysis points to a different picture of glycolysis . When thTogether, our analysis yields a comprehensive, quantitative framework for understanding glycolysis and its regulation in the context of the Warburg Effect. Historically, glycolysis is thought to have a rate-limiting step at several points in the pathway . These p+, and the levels of glucose-derived intermediates in the pathway that affects the thermodynamics of glycolysis. There are multiple mechanisms that lead to this finding. ATP consumption has previously been reported to control the rate of glycolysis and this effect likely occurs to some extent through GAPDH  activity and lactate transport through the monocarboxylate transporter (MCT),Oxygen consumption, transport and activity of oxidative phosphorylation (OxPhos),ATP-buffering mechanisms involving adenylate Kinase, creatine Kinase, and ATPase activity,+-mediated mitochondrial shuttles including the Malate–Aspartate Shuttle and the Glycerol-3-Phosphate Shuttle.NADH/NADThe model includes a compartment involving enzymes in glycolysis and additional compartments involving with reactions that are coupled to glycolysis. The following compartments are considered in addition to the enzymes in glycolysis are considered:More generally glycogen metabolism, pentose phosphate pathway and alanine biosynthesis could also be included but are omitted since the conclusions drawn in this study do not depend on their activities. A schematic representation of the kinetic model and resulting network is shown in We first introduce notation and other conventions. Each symbol indicates the respective concentration. A subscript ‘0’ refers to the steady-state value. Reaction rates are expressed in millimoles per hour per unit intracellular volume. Together with initial state vectors The dynamics of the system are therefore formulated as initial value problem for ordinary differential equations (ODEs). Starting parameter values based on published data are defined in + ratio) together with glucose, lactate, and oxygen supply through exchange fluxes from the intercellular and extracellular compartment, (b) steady states and time courses of all variables of the metabolic network, and (c) effects of metabolic parameter perturbations on the overall system output. Cellular energy homeostasis through ATP is supported by several mechanisms with different relaxation times and regulation mechanisms: (a) directly by glycolysis that converts glucose into intracellular pyruvate, (b) mitochondrial respiration through consumption of pyruvate and oxygen via the TCA cycle, and (c) the buffering effect of creatine kinase that facilitates the reversible reaction of phosphocreatine (PCr) with ADP to produce creatine (Cr) and ATP, and (d) adenylate kinase activity that catalyzes interconversion of adenine nucleotides: 2ADP ⇔ ATP + AMP, and (e) the additional NADH pool produced in cytosol and transported to mitochondria by different shuttle mechanisms. For oxygen consumption, we assumed that mitochondrial respiration (O2 consumption) depends on cellular pyruvate and oxygen concentrations and that respiratory chain activity is activated by ADP concentration.Therefore, the model is designed to describe: (a) steady-state and dynamic behavior of energy metabolism, including the Warburg effect  and includes 27 state variables whose names, balance equations, and steady-state values are presented in i in the intracellular compartment, the general form is:i is Ci; i between the media and cell; j) or utilize (k) cellular species i, e) species is Ce and rie is the ratio of cell volume to media volume. To ensure that steady states are obtained during perturbations of parameters without loss of generality, we assume that the media boundary metabolites have constant concentrations. The transport of boundary species is taken to be either facilitated  or passive (oxygen). For passive diffusion of oxygen the equation for the net transport flux is:O2 diffusion, and Cei is the extracellular concentration for species i, +-mediated mitochondrial shuttle flux from NADH to NAD+ by assuming that the total shuttle flux is balanced with the NADH-generation reactions involving LDH and PHGDH.The state of the system is represented by the state vector of time-dependent metabolite concentrations CKA and KI are the corresponding activation and inhibition constants. With this convention, there are 4 (or 5 including the control function) independent parameters for each metabolic flux with a one-step binding mechanism. For fluxes with no allosteric regulation, When possible, in order to minimize the number of parameters, we utilized a ‘one-step binding enzyme mechanism’ with a rate law of the form :(6)V=(Vfction is :(7)χ subintervals of equal size. Then, we randomly sample n values , one from each subinterval, for the i-th parameter. We next randomly permuted the n values for each parameter to get the parameter vector. We then evaluated the following: the Warburg Effect value, energy and redox states, metabolite steady-state concentrations and fluxes and flux control coefficients (FCCs). The flux control coefficient that an enzyme Ei exerts on the lactate flux JLac is defined as  followed by statistical assessment of the resulting solution. The idea of this method is to inject uncertainty of the parameters in the model by randomly selecting parameter values from uniform probability distributions. This was achieved by a Monte Carlo method using a randomly drawn set of parameters. The range of parameters chosen with uniform sampling was within two orders of magnitudes of each Vmax . Such a sampling was chosen to be large enough to cover all feasible solutions of aerobic glycolysis. The sampling was carried out using a Latin Hypercube sampling method . For eacfined as :(11)FCCiTo compute the FCC values numerically, we considered a perturbation 0.01 times of the enzyme value and then evaluated the change in flux. The model was implemented in Matlab as a system of 27 ordinary differential equations (ODEs) with an ODE solver designed for stiff ODE systems.2. For treatments, the cells were seeded in 6-well plate at a density of 2 × 105 to 5 × 105 cells per well. After overnight incubation, full growth media were removed, and cells were washed with 2 ml PBS before the addition of RPMI media (without glucose), supplemented with 10% dialyzed and heat inactivated FBS, 100 U/ml penicillin, 100 μg/ml streptomycin and 5 mM 13C-U-glucose  (fresh growth media). For drug treatments and flux measurements, full growth media were replaced with fresh growth media, containing either 0.1% DMSO, or the concentration of the indicated drug. Media were then collected at 30, 60, 90, and 120 min after incubation. From 20 µl media, 80 µl of ice cold H2O was added, together with 400 µl ice cold methanol . After vigorous vortexing, the solution was then centrifuged at 20,000×g at 4°C for 10 min and then the supernatant was dried under vacuum. At 120 min, the media were removed as completely as possible, and then 6-well plates were immediately placed on dry ice, followed by the addition of 1 ml extraction solvent, 80% MeOH/H2O , which was pre-cooled in −80°C freezer for at least 1 hr. The dishes were then transferred to the −80°C freezer. The plates were left for 15 min and then cells were scraped into the solvent on dry ice and then transferred to two 1.7-ml eppendorf tubes, and centrifuged with the speed of 20,000×g at 4°C for 10 min. Metabolite extracts are prepared from three separate wells to make three replicate samples. The supernatant is then transferred to new eppendorf tubes, and dried under vacuum. For analysis, each extract was then re-constituted into water (15 μl for cell extract and 50 μl for medium extract) and then diluted with an equal volume of 50% Methanol/Acetonitrile. Finally, 5 μl was injected into the column for analysis.HCT116 cells were cultured with a growth medium, which contains RPMI 1640, 10% Fetal Bovine Serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin. The cells were obtained as a gift from Lewis Cantley's laboratory. The cells were cultured in 37°C with 5% CO13C lactate were first measured by mixing a standard of unlabeled lactate with a defined concentration into the medium. For each calculated flux, an estimation of the flux from glucose to lactate was obtained from the slope of the time course of 13C lactate production using the time points of media collection described above. Slopes were computed using Graphpad Prism with time courses determined to be linear from the goodness of fit (R2 > 0.98 with few exceptions). For corresponding plots for different agents, fraction of inhibition was computed from Ki values that were taken based on previous reports (Absolute concentrations of  reports .m/z) was used. The resolution was set at 70,000. The maximum injection time was 200 ms with typical injection times around 50 ms. These settings resulted in a duty cycle of around 550 ms to carry out scans in both positive and negative mode. Automated gain control (AGC) was targeted at 3,000,000 ions.The Q Exactive Mass Spectrometer (QE-MS) is equipped with a heated electrospray ionization probe (HESI), and the relevant parameters are as listed: heater temperature, 120°C; sheath gas, 30; auxiliary gas, 10; sweep gas, 3; spray voltage, 3.6 kV for positive mode, and 2.5 kV for negative mode. Capillary temperature was set at 320°C, and S-lens was 55. A full scan range from 60 to 900 (Liquid chromatography (Ultimate 3000 UHPLC) is coupled to the QE-MS for metabolite separation and detection. An Xbridge amide column  is employed for compound separation. The mobile phase A is 20 mM ammonium acetate and 15 mM ammonium hydroxide in water with 3% acetonitrile, pH 9.0, as described above, and mobile phase B is acetonitrile. A linear gradient was used as follows: 0 min, 85% B; 1.5 min, 85% B; 5.5 min, 35% B; 10 min, 35% B; 10.5 min, 35% B; 14.5 min, 35% B; 15 min, 85% B; and 20 min, 85% B. The flow rate was 0.15 ml/min from 0 to 10 min and 15 to 20 min, and 0.3 ml/min from 10.5 to 14.5 min. All solvents are LC-MS Optima grade and purchased from Fisher Scientific.Human mammary epithelial MCF-10A cells  stably expressing Peredox-NLS were generated and cultured as previously described . 2–4 daym/z and detected retention time known metabolites is used for targeted metabolite analysis with data collected in both positive and negative mode. m/z width is set at 10 ppm. The output file including detected m/z and relative intensity in different samples is obtained after data processing. For resulting simulation data, hierarchical clustering was carried out using spearman ranked correlations and the Gene-e software package (Broad Institute). Box-plots  was calculated and made with the Graphpad Prism software package.Raw data collected from the QE-MS is processed using Sieve 2.0 (Thermo Scientific). Peak alignment and detection are performed according to the protocol described by Thermo Scientific. For a targeted metabolite analysis, the method ‘peak alignment and frame extraction’ is applied. An input file of theoretical  review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent , 2 experts in the field of cancer metabolism, as well as a member of our Board of Reviewing Editors.Thank you for sending your work entitled “A quantitative model of aerobic glycolysis identifies GAPDH as a rate-limiting step” for consideration at The Reviewing editor and the other reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.All the reviewers and editors agree that the work is interesting as it addresses how glycolytic rate is controlled, a question lacking conclusive answers. The experiments are rigorous and the results are for the most part convincing. However, there are a number of concerns that need to be addressed before the manuscript can be accepted.1) An important conclusion is that GAPDH exerts a surprising level of control over glycolytic rate and thus it would be important to expand upon how GAPDH constrains flux. Is the bottleneck imposed simply by reduced levels of enzyme expression, in which case over-expressing the enzyme should produce major changes in metabolite levels and overall flux? Or are other effects at work, such as ROS-mediated inhibition, which has been observed in several systems?2) A concern is that the specificity of the glycolytic inhibitors used is questionable. This is a general issue for the field and not easily addressed by these authors, but should be acknowledged in interpreting the experiments.3) Measuring glucose consumption directly would be a more straightforward way to assess changes in overall glycolytic flux, and addresses aspect of the Warburg effect (increased glucose uptake) that is not considered by this work.4) Distributions of NADH/NAD+ ratios are presented for cells at different glucose concentrations, but how glucose concentrations were maintained in culture over a 24-hour experiment is not clear. Particularly for the 0.5 mM glucose sample, was the concentration of glucose still 0.5 mM when ratios were measured? This seems unlikely.5) A straightforward explanation for why GAPDH activity predicts lactate production is that these steps are linked by NAD+ use and regeneration. Even in the absence of modeling a consideration of glycolysis suggests this relationship yet it is hardly mentioned. This point needs to be discussed. 1) An important conclusion is that GAPDH exerts a surprising level of control over glycolytic rate and thus it would be important to expand upon how GAPDH constrains flux. Is the bottleneck imposed simply by reduced levels of enzyme expression, in which case over-expressing the enzyme should produce major changes in metabolite levels and overall flux? Or are other effects at work, such as ROS-mediated inhibition, which has been observed in several systems?The reviewers raise important points that warrant further discussion. We first note that many different mechanisms can lead to changes in GAPDH activity. For example, one instance was investigated in Furthermore, increases in GAPDH expression are also predicted to increase flux through the pathway although as the model predicts the correlation is small (R2 ∼0.4) so this will not always be the case. In fact, GAPDH is the most abundant enzyme in glycolysis suggesting that in many cases the expression of glycolytic enzymes is designed to achieve optimal flux through the pathway . An increase in activity can occur through multiple mechanisms including the alleviation of its inhibition by ROS or other mechanisms, increasing its activity through allosteric activation, or by increasing its expression which proportionately increases the enzyme activity through increasing Vmax. Together each of these possibilities would manifest themselves in different contexts. We have included the mention of these multiple possibilities in the Discussion. A thorough discussion of each of these effects is mentioned in a revised manuscript.2) A concern is that the specificity of the glycolytic inhibitors used is questionable. This is a general issue for the field and not easily addressed by these authors, but should be acknowledged in interpreting the experiments.We thank the reviewers for raising this important point. We have modified the text to include caveats regarding the specificity of the inhibitors, resulting limitations in the interpretation of the data, and our efforts to minimize these confounding variables.3) Measuring glucose consumption directly would be a more straightforward way to assess changes in overall glycolytic flux, and addresses aspect of the Warburg effect (increased glucose uptake) that is not considered by this work.We thank the reviewers for this suggestion. In designing this study, we reasoned that a more conclusive measurement of glycolytic flux was the rate at which glucose was being converted to lactate. This measurement accounts for both glucose uptake and production of lactate. We reasoned that glucose uptake alone does not account for the portion of glycolytic flux that is siphoned into biosynthetic pathways or into the mitochondria. Nevertheless, we have also measured additional fluxes (Author response image 1) including glucose uptake rate, and a biosynthesis rate from glucose and found that each does correlate reasonably well with the rate of glucose to lactate. This result is shown below:4) Distributions of NADH/NAD+ ratios are presented for cells at different glucose concentrations, but how glucose concentrations were maintained in culture over a 24-hour experiment is not clear. Particularly for the 0.5 mM glucose sample, was the concentration of glucose still 0.5 mM when ratios were measured? This seems unlikely.We appreciate this concern and have included further clarifications in the Methods. By our calculations shown below and now contained in the Methods, the glucose concentration should be close to constant over the duration of the experiment. The concentration of glucose is maintained by considering a culture system in which cells are seeded at low density and media is present in vast excess. A quantitative estimate of glucose maintenance in the media can be considered using values of the glucose uptake rate of the cells (∼100fmol/cell/hr), the cell number (∼2000), the volume of media used (400 μL), and concentration of glucose in the media (500μM). Thus in 24 hours, we estimate that about 5nmols of glucose are consumed. The media contains about 200nmols of glucose which is in excess of the amount consumed by the cells.5) A straightforward explanation for why GAPDH activity predicts lactate production is that these steps are linked by NAD+ use and regeneration. Even in the absence of modeling a consideration of glycolysis suggests this relationship yet it is hardly mentioned. This point needs to be discussed.In the Discussion, we have expanded on the mechanisms of GAPDH exerting control over glycolytic rate. The mechanism is partially explained by NAD+ use and regeneration but the full explanation appears more complicated and is due to GAPDH existing at a unique point in the pathway where it can be affected by redox (NAD+/NADH), energy  that together allow for more points of regulation.However we respectfully disagree with the reviewers that this finding could be obtained without modeling especially since it was not previously known that GAPDH exerts control over glycolysis. In fact numerous introductory biochemistry textbooks for undergraduate and medical school students have discussed in detail the control of glycolysis and the emphasis has been in these discussions on the steps in glycolysis that involve changes in standard free energies such as pyruvate kinase or phosphofructokinase.


Despite several recent advances in the automated generation of draft metabolic reconstructions, the manual curation of these networks to produce high quality genome-scale metabolic models remains a labour-intensive and challenging task.We present PathwayBooster, an open-source software tool to support the manual comparison and curation of metabolic models. It combines gene annotations from GenBank files and other sources with information retrieved from the metabolic databases BRENDA and KEGG to produce a set of pathway diagrams and reports summarising the evidence for the presence of a reaction in a given organism’s metabolic network. By comparing multiple sources of evidence within a common framework, PathwayBooster assists the curator in the identification of likely false positive (misannotated enzyme) and false negative (pathway hole) reactions. Reaction evidence may be taken from alternative annotations of the same genome and/or a set of closely related organisms.By integrating and visualising evidence from multiple sources, PathwayBooster reduces the manual effort required in the curation of a metabolic model. The software is available online at http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/.The online version of this article (doi:10.1186/s12859-014-0447-2) contains supplementary material, which is available to authorized users. The production of a genome-scale metabolic model for any organism is a time-consuming and laborious task . During The first stage of a genome-scale metabolic reconstruction is the creation of a draft metabolic model. Following the identification and functional annotation of protein-coding genes, comparison of predicted enzymatic functions to a database of known metabolic reactions produces a set of reactions that are presumed to be available to the organism, and hence a network of compounds, reactions and associated enzymes. Resources available for the automated production of a draft genome-scale model include SuBliMinaL Toolbox , Model SIn the next stages of curation, obvious pathway holes (due to the lack of an assigned enzyme) and false positive reactions (due to enzyme misannotation) need to be found and corrected. To address both of these issues there is a need to collect and analyse evidence for each reaction from the literature and from genomic and metabolic databases, across multiple closely-related species. Without automation this process is tedious and repetitive.There are already some tools that can tackle this problem allowing comparative analysis of metabolic pathways, such as Comparative Pathway Analyzer , FMM 9]9] and CoComparative Pathway Analyzer (CPA)  is a webFMM  is a webComPath  is a comAn ideal piece of software for curating a metabolic model would provide a pathway visualiser together with annotation confidence information and existing literature references. However, none of the packages above contains these features all together.We have developed PathwayBooster as an open-source software tool to support the comparison and curation of metabolic models. Although other tools exist for the comparative analysis of metabolic pathways, PathwayBooster presents a unique combination of features. Amongst other capabilities, PathwayBooster can be used to compare the functional annotations of genes with ‘bidirectional best BLAST hits’ analyses between the target organism and the relevant related species. It also compiles a list of literature references obtained from BRENDA  to suppoPathwayBooster is implemented in Python and can either be used as an command-line tool or through a graphical interface. The user supplies input in the form of GenBank, EMBL or FASTA files for all the organisms that are to be compared. Output is presented as a browsable set of HTML files, with sections that are described in more detail below. Instructions on how to run PathwayBooster can be found in the user manual  numbers present in a pathway of interest. Annotated genes are presented by EC number for all specified organisms. Each gene is hyperlinked to the KEGG database, where associated information can be viewed. It also indicates the origin of each annotation. This is relevant when more than one genome annotation source is under consideration. With the exception of KEGG, all annotation sources must be supplied by the user. In the case of KEGG annotations the data is retrieved using the REST web service as before.Two proteins from two different organisms are called ‘best reciprocal hits’ when each is the best BLAST hit of the other. This is a simple method commonly used to find putative orthologous proteins, i.e. proteins descending from a common ancestor that have diverged following a speciation event . These pTo find possible candidate proteins for a particular function, the first three BLAST hits from the ‘query’ genome can also be viewed for every enzyme annotated in the reference species. This report also provides the functional annotation and EC number for each candidate, as well as the sequence similarity, E-value and Z-score as before.PathwayBooster makes use of the BRENDA database to provide information about publications connecting a given organism with a particular enzymatic function. For each pathway selected, publications from BRENDA that assert the presence of each EC number in each specified organism are listed. Publications indicating that a given EC number might be absent in an organism are also available. Each publication has a hyperlink to the PubMed website, where its abstract can be viewed. The number of manually annotated references available in BRENDA is currently over 100,000 .For a given KEGG pathway, we can define a Hamming distance between two organisms as the number of enzymatic functions present in one but not both of those organisms. In the PathwayBooster report a heat map is provided to show the Hamming distance between the organisms selected, according to the presence or absence of each enzyme in the pathway. This simple visualisation of the similarity between pathway structures can be used to support comparative analysis or to summarise the relative consistency of different annotation sources.This section presents examples from the curation of a genome-scale metabolic model where the advantages of using PathwayBooster are clearly seen.Geobacillus thermoglucosidasius NCIMB 11955 is a thermophilic bacterium with the potential to convert lignocellulose to ethanol in a highly productive manner. Thermophilic bacteria are especially useful in biofuel production since they can withstand the high temperatures that are unavoidable at certain stages of fermentation. Given these interesting properties, we would like to understand the metabolism of this organism in more detail.Escherichia coli, Bacillus subtilis), other species within the same genus as the target organism  and a different strain of the same species (Geobacillus thermoglucosidasius C56-YS9). Evidence for the presence of enzymes in these comparison genomes was retrieved from KEGG. In addition, BLAST analysis of the query organism was carried out against the E. coli and B. subtilis annotated proteomes.As an example, PathwayBooster results for cysteine and methionine metabolism (KEGG pathway 00270) are presented. The initial draft metabolic network was built using ERGO . ReferenGeobacillus thermoglucosidasius draft metabolic network and the the other organisms. Examining the pathway diagram  in subtilis . The artG. thermoglucosidasius NCIMB 11955 genome  was found in the annotation of the query strain and not found in the closely related reference organisms. The most probable explanations are that either the gene annotated with this enzymatic function has been wrongly assigned, or that By examining the ‘Publications’ reports, this function is not found in any of the relevant literature. Taking a closer look at the assigned gene, RTMO02286, in the ‘Resources such as Model SEED  can be uProject name: PathwayBoosterProject homepage: http://www.theosysbio.bio.ic.ac.uk/resources/pathwaybooster/Operating systems: Linux, Mac OSX, Windows.Other requirements: BRENDA flatfile database Programming language: PythonLicense: GPLv3