The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical potentials (or log-concentrations) of intracellular metabolites; in the latter, we identify a restricted set of loops  in the periplasmic and cytoplasmic core as the origin of thermodynamic infeasibility in a large sample  consistent with empirical knowledge; in the latter, we have effectively corrected thermodynamically infeasible flux configurations.The operation of biological systems is constrained under all circumstances by the laws of physics. Thermodynamics, in particular, dictates preferential directions in which biochemical reactions should flow at stationarity. When applied to cellular reaction systems (like metabolic networks), it favors the emergence of some  ways to organize the flow of matter while prohibiting others. The development of detailed predictive models for the biochemical activity of a cell relies on the possibility to integrate the laws of thermodynamics in genome-scale reconstructions of cellular metabolic networks. In this work we have devised an efficient relaxation algorithm to implement thermodynamic constraints in genome-scale models. Besides allowing to check for thermodynamic feasibility of reaction flow configurations, it is also capable of providing information on other relevant physico-chemical quantities. We have applied it to two cellular metabolic networks of different complexity, namely that of human red blood cells and that of the bacterium  Constraint-based models of cellular metabolism are important tools to analyze and predict the chemical activity and response to perturbations of cells without relying on kinetic details that are often unavailable. In such frameworks, the metabolic capabilities of a cell are inferred from the overall configuration space compatible with minimal physico-chemical constraints describing the non-equilibrium steady state of the underlying reaction network. First, feasible reaction flux vectors need to satisfy mass-balance conditions. Then, according to the second law of thermodynamics, in an open chemical network at steady state and constant temperature and pressure the direction of each reaction should ensure a decrease in Gibbs energy. Thermodynamic consistency of flux configurations satisfying mass-balance alone is in general not guaranteed due to the presence of infeasible cycles Much work has been concerned with implementing thermodynamic constraints in genome-scale models of metabolism. The removal of thermodynamic inconsistencies was proved to be useful in estimating concentrations and affinities besides fluxes in Flux-Balance-Analysis The scalability of algorithms to solve mixed integer-linear (or non-linear) programming problems may become an issue when the underlying network size is large or when one is interested in sampling the solution space (for both free energies and fluxes) rather than focusing on a potentially small set of configurations (e.g. optima). Luckily, however, solutions to computationally hard problems can often be generated efficiently with the help of heuristic algorithms based on simple local rules. The use of message-passing algorithms to characterize the high-dimensional volume of the solution space of FBA models Our goal in this paper is to obtain information about the landscape of Gibbs free energies compatible with a given vector of reaction directions by following a route that allows to use all stoichiometric information via heuristics inspired by perceptron learning. In a nutshell, the method we propose consists in exploiting the network's structure to iteratively build up correlations between the chemical potentials of the reacting species starting from a seed of empirical biochemical knowledge, until a thermodynamically consistent profile is achieved. The resulting algorithm is completely scalable and can be employed for different purposes, like checking the feasibility of flux configurations, identifying and removing infeasible cycles, estimating reaction affinities, and obtaining bounds for (log−)concentrations and free energies of formation.Escherichia coli, iAF1260 In the following, we describe the method in detail, providing a mathematical proof of convergence as well as theoretical arguments highlighting the main idea behind the procedure. As applications, we focus on two metabolic networks of rather different complexity. First, we shall obtain a detailed reconstruction of the Gibbs energy landscape underlying metabolic activity in the human red blood cell (hRBC) starting from the flux maps obtained in Escherichia coli iAF1260, presented in Escherichia coli, we will consider the inner matrix of periplasmic and cytoplasmic reactions without repetitions, which consists of The cellular systems analyzed in this study are (i) the model of the hRBC metabolism developed in à la Von NeumannAccording to the second law of thermodynamics, in an open system at constant temperature see e.g. , Ch. 1 drives the gradual adjustment of chemical potentials by ensuring that, at every iteration, the least satisfied constraint  initializations of the above algorithm is a set of If there is no prior information on the direction of some reactions (e.g. because they are putatively reversible), the corresponding constraints (3) are formally absent, as if Finally, some observations are in order about the solution space of (3), which in general has the form of an unbounded cone passing trough the origin. If one is interested in uniform sampling the space of A number of interesting theoretical and computational questions arise at this stage, regarding e.g. the minimal amount of prior information on chemical potentials needed to bound the solution space of (3), or computationally efficient and scalable methods to obtain uniform sampling  has no solutions apart from the trivial one. In accordance with the Farkas-Minkowski lemma While running the algorithm to compute chemical potentials for a large number of iteration steps Search, within such subset of reactions, for a loop of length If a loop is found, change the direction of one of its reversible reactions chosen with uniform probability.In the http://chimera.roma1.infn.it/SYSBIO/.A computer code implementing the algorithms to compute chemical potentials and identify and remove infeasible loops is downloadable As a first application, we have employed the MinOver scheme outlined above to analyze the thermodynamic landscape of the hRBC metabolic network. As a starting point, we have considered the flux configurations obtained in see e.g. . VNC areaccording to according to As a first step, we tested the thermodynamic feasibility of these direction assignments, solving (3) by starting from the vector Results for the estimated concentrations and Gibbs energy changes (computed from (2) using the final chemical potential vectors) are showcased in We can now quantify the extent to which the solutions we generate are “close” to the prior. In Escherichia coli iAF1260 The application that we have just discussed shows that the algorithm we present can provide information on the Gibbs energy landscape, even correcting inconsistent input knowledge. We shall now employ the procedure outlined in the Since we are not focusing on the reconstruction of the Gibbs energy landscape but simply on the existence of solutions of (3), a detailed biochemical prior is not needed. Therefore, for the present purposes we have taken We have furthermore studied the set of loops that were thus identified and corrected. Quite remarkably this analysis revealed that thermodynamical infeasibility is related to the presence of a small set of cycles, 23 in total. These are reported in We remark that the corrected flux configurations thus obtained, like the starting ones (which were drawn from a uniform product measure over reactions), are not guaranteed to be consistent with any steady state assumption. On the other hand, see Supporting Ideally, constraint-based models of metabolic activity allow to appraise the energetic potential of cells based on minimal constraints related to local mass-balance and thermodynamic feasibility rules, possibly complemented with optimization principles that can encode for functional constraints. As a result, the flow of matter in non-equilibrium steady states could be characterized in terms of the Gibbs energy change of reactions, which specifies the directionality of interconversions, and of the average number of turnovers per time per volume, i.e. the flux, without the need of detailed information on enzyme kinetics or transport mechanisms. Thermodynamic constraints, strongly linked to overall intracellular conditions like ionic strength and pH Many important steps have been taken recently to tackle it. At one level, thermodynamic feasibility can be translated into topologic constraints (‘absence of loops’) for the flux configuration emerging from mass-balance constraints Escherichia coli, instead, we have focused on the problem of correcting thermodynamically infeasible flux states in the core formed by the periplasmic and cytoplasmic matrix. We have thoroughly analyzed a large ensemble of configurations of reaction directions, identifying the cycles responsible for thermodynamic inconsistency and correcting them in a very modest amount of CPU time. Quite intriguingly, we have related infeasibility to the existence of a relatively small number of short cycles in the flux configuration, whose removal suffices to ensure thermodynamic feasibility in worst-case flux configurations.The usefulness of the algorithm in the analysis of genome-scale networks has been tested in two different cases. For the metabolic network of the human red blood cell, our approach has proved capable of reconstructing the Gibbs energy landscape correcting inconsistent prior information. In turn, this has lead to predictions for intracellular metabolite levels. It is important to stress that the bounds on concentrations we have obtained (which vary rather heterogeneously across compounds) only reflect stoichiometric information. For the metabolic network of The main advantage of our method consists in our view in its efficient implementation. On the critical side, we point to two aspects that deserve further study. In first place, our tool requires flux configurations as inputs, i.e. it is still unable to produce thermodynamically feasible configurations of fluxes and chemical potentials starting from no previous reversibility hypothesis. However it may provide the basis of a more general procedure for the analysis of genome-scale metabolic networks that couples flux- and thermodynamic profiling, a challenging open problem in computational biology. Secondly, our method relies on prior biochemical information and it would be desirable for it to be effective even if much or most biochemical priors are unknown. As we pointed out, some information has to be injected into the problem for the sake of definiteness. The interesting question is therefore what is the minimum necessary prior needed to reconstruct the Gibbs energy landscape and how are predictions affected by restricted priors. Such problems are mathematical in nature and could bear a particularly high significance for modeling purposes.Dataset S1Stoichiometric matrix of the human Red Blood Cell metabolic network employed in this study.(TXT)Click here for additional data file.Dataset S2Thermodynamic potentials for the human Red Blood Cell metabolic network employed in this study.(XLS)Click here for additional data file.Table S1Escherichia coli metabolic network iAF1260.Detailed information about infeasible cycles (reactions and metabolites) identified in the (XLS)Click here for additional data file.Text S1A toy example of the computation of chemical potentials by MinOver with loop correction.(PDF)Click here for additional data file.


Flux-balance analysis based on linear optimization is widely used to compute metabolic fluxes in large metabolic networks and gains increasingly importance in network curation and structural analysis. Thus, a computational tool flexible enough to realize a wide variety of FBA algorithms and able to handle batch series of flux-balance optimizations is of great benefit.http://www.bioinformatics.org/fasimu including manual, tutorial, and plugins.We present FASIMU, a command line oriented software for the computation of flux distributions using a variety of the most common FBA algorithms, including the first available implementation of (i) weighted flux minimization, (ii) fitness maximization for partially inhibited enzymes, and (iii) of the concentration-based thermodynamic feasibility constraint. It allows batch computation with varying objectives and constraints suited for network pruning, leak analysis, flux-variability analysis, and systematic probing of metabolic objectives for network curation. Input and output supports SBML. FASIMU can work with free (lp_solve and GLPK) or commercial solvers . A new plugin (faBiNA) for BiNA allows to conveniently visualize calculated flux distributions. The platform-independent program is an open-source project, freely available under GNU public license at We present a flux-balance optimization program whose main merits are the implementation of thermodynamics as a constraint, batch series of computations, free availability of sources, choice on various external solvers, and the flexibility on metabolic objectives and constraints. The distribution of fluxes (i.e. reactions rates of enzymes and transporters) in large metabolic networks is commonly calculated by means of constraint-based optimization methods, usually referred to as flux-balance analysis (FBA). The first FBA applications relied on the steady-state assumption and biomass maximization only . However® Optimization Environment [Several software solutions for FBA are currently available. The freely available software COBRA  covers aironment  as the rironment  covers aironment  is a conironment  frameworironment  computesironment  is the oironment  is a simFor all of the above programs, the use of thermodynamic feasibility as a direct constraint is currently missing. AnNET  is the oWe have developed FASIMU - a comprehensive, flexible and user-friendly computation environment for FBA. Its command-line interface allows to tackle difficult problems in an interactive approach which can later be transferred into an executable computation script. FASIMU is "open" in two aspects: (i) the source code is open and written in widely known scripting languages which makes it easy to adapt and to implement new functions and (ii) intermediate results are stored in human-readable files rendering the calculation process traceable.For the computational effort, FASIMU is divided in two parts. The computationally expensive part, the solution of the optimization problem, is left to specialized and highly optimized software: the commercials programs CPLEX   is:In a network curation process the batch processing of defined simulations is required to verify metabolic functions on the fly. As an application example we show how FASIMU has successfully aided the curation of the genome-scale model of the human hepatocyte   is recommended providing bash, gawk, GLPK for almost any Windows version available.FASIMU can run on LINUX, Windows (from 98 or NT V ≥ 4 to the most recent Windows versions), MacOS, AIX, HP-UX and possibly many other operating systems since its minimal requirements, GNU bash , GNU awkFASIMU's input is a stoichiometric model given in SBML , level 2With other powerful flux-balance optimization programs at hand, the question arises why another product is necessary. The fact that important published methods have not been available as an executable program has already been mentioned. An alternative to a new software would have been to implement the required algorithms in one of the more open programming frameworks COBRA , SBRT 11, BioSPI® [http://pages.cs.wisc.edu/~swright/ooqp, the latter requiring BLAS and software from HSL [COBRA  has a co®  is requi®  the deve®  has quit®  is used ® , the dev®  also covfrom HSL  only avaThe main difference between FASIMU and all other comparable software is the concept of a concise description file for flux-balance simulations. Its development has been driven by the necessity of a clearly defined protocol for network testing. The main objective was that the description file contained the minimal necessary information but allows considerable flexibility to define heterogeneous network tasks, beside (i) the simple test on the producibility or degradability of metabolites, also (ii) simulations of enzymopathies, (iii) tests on the non-existence of solutions, and (iv) tests on side conditions in the flux distributions. For instance the 442 simulations to test HepatoNet1  are contA freely available software based on free and widely available software has large advantages for a first test as the program can immediately be tested. Aside from the consideration whether the fee for a commercial product such as MATLAB or CPLEX is worth the investment free software such as GNU bash, gawk, and GLPK is available for the maximum of possible computer architectures and operating systems.free software where possible, commercial where necessary. The same is true for the use of BiNA which uses powerful algorithms to draw network graphs. BiNA is freely available but not open source since it uses the commercial software yFiles licensed for free of charge use in conjunction with BiNA.Basing FASIMU on free software is a practical consideration rather than a decision on principle. We found out that for problems such as a feasible MILP implementation for a large metabolic system, the available free software is not yet sufficient. Thus, to integrate CPLEX or LINDO is a logical consequence of common sense: For FASIMU a compilable language such as JAVA or C++ was not used to support easy modification. FASIMU is also designed as a testing environment for newly developed algorithms, thus, development time is critical. Furthermore, the majority of the computation time is used by the external solver program. Therefore, to decrease the running time of the software which merely controls and transforms the input and output of the solver program would not have a great effect.Graphical user interfaces (GUI) allow an easy access to the possible options and require minimal learning time. However, users who want to combine and modify the given algorithms rely on a scripting language. Programs allowing both the control by a graphical user interface and by a command-line interface require considerably more software development time. For FASIMU the focus is clearly the command-line usability. Powerful network visualization products such as BiNA, Cytoscape, CellDesigner, SBW  have alrWe present a Flux Analysis SIMUlation framework which (i) offers the first available implementation of thermodynamic feasibility as a quickly computable MILP, (ii) is flexible in the choice of objective functions and constraints, (iii) allows for batch processing of heterogeneous computations and automatic evaluation of the solutions, (iv) facilitate visualization of the computed fluxes with plugins for BiNA and Cytoscape, and (v) can completely be based on free software.Project name FASIMUProject home page http://www.bioinformatics.org/fasimuOperating system(s) Platform independent.Programming language(s) bash version 3.0.0 or higher, version 4.0.0 or higher recommended. gawk version 3.0.0 or higher.Other requirements Any of CPLEX (version 9-12 tested), GLPK (version 4.42 tested), lp_solve (version 5.5 tested), LINDO (version 5.0.1.317 tested).License GNU GPLAny restrictions to use by non-academics none.AH developed FASIMU; SH, AG, and AH wrote faBiNA. AH and CG wrote the manual/tutorial. AH, SH, HH, and CG drafted the manuscript, approved by all authors.FASIMU 2.2.1 release archive. This archive contains the complete FASIMU distributions and unzips in five directories: FASIMU contains the programs, -Doc FASIMUcontains the documentation , -Ery-Example FASIMUcontains a small example of the human erythrocyte, -Ecoli-Example FASIMa large example of the E. coli, -Liver-Example FASIMUanother large example of the human hepatocyte.Click here for file


A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments . GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions,  of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants . The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species.The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production . Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for  Zea mays, commonly known as maize or corn, is a plant organism of paramount importance as a food crop, biofuel production platform and a model for studying plant genetics Arabidopsis thalianaOryza SativaSorghum bicolorZea maysTheobroma cacaoThis decade we witnessed significant advancements towards mapping plant genes to metabolic functions culminating with the complete genome sequencing and partial annotation of a number of plant species, namely, Arabidopsis thaliana). The model by Dal'Molin et al identifies the set of essential reactions, accounts for the classical photorespiratory cycle and highlights the significant differences between photosynthetic and non-photosynthetic metabolism. The model by Poolman et al includes ATP demand constraints for biomass production and maintenance and suggests strategies for the construction of metabolic modules as a consequence of variation in ATP requirement. Both models make a significant step forward towards assessing the metabolic capabilities of plants establishing production routes for key biomass precursors and major pathways of Arabidopsis primary metabolism. In addition, two recent efforts involved the reconstruction of plant models with an emphasis on specific physiological conditions or tissue types et al developed a metabolic network of only primary metabolism in barley seeds and studied grain yield and metabolic fluxes under a variety of oxygen availability scenarios and genetic manipulations et al. reconstructed a multi-compartmental model of the central metabolism of Brassica napus (Rapeseed) and simulated seed growth during the stage of oil accumulation and subsequently studied network properties of seed metabolism via Flux Balance Analysis, Principal Component Analysis and reaction deletion studies Genome-scale metabolic reconstructions are available for an increasing number of organisms Zea mays iRS1563). This is, to the best of our knowledge, the first attempt of globally characterizing the metabolic capabilities (both primary and secondary metabolism) using a compartmentalized photosynthetic model of an important crop and energy plant species. The development of a genome-scale model for maize is a significant challenge due to its genome size which is 14 times larger Arabidopsis thaliana (157 million base pairs) Zea mays iRS1563 in comparison to the AraGEM model by Dal'Molin et al. We chose the AraGEM model as a basis of comparisons as at the onset of this study it was the most comprehensive genome-scale compartmentalized model of a plant species capable of recapitulating basic plant physiological states. In order to deduce the genuine differences between maize and Arabidopsis irrespective of annotation chronology we also reconstructed an up-to-date model of Arabidopsis, A. thaliana iRS1597. A. thaliana iRS1597 contains 1597 genes, 1798 reactions and 1820 metabolites. In comparison to A. thaliana iRS1597, Zea mays iRS1563 has 445 new reactions and 369 new metabolites. Notably, 893 reactions and 674 metabolites are included in Zea mays iRS1563 that are absent from the maize C4GEM model. All reactions present in Zea mays iRS1563 are elementally and charged balanced and localized into six compartments including cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular space. Provisions for accounting that photosynthesis in maize  occurs in two separate cell types  are included in the model. GPR associations are delineated from the available functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. A biomass equation is established that quantifies the relative abundance of different constituents of dry plant cell biomass. Biomass production under three different physiological states  is demonstrated and the model is tested against experimental data for two naturally occurring maize mutants .In this paper, we describe the construction of a genome-scale in silico model of maize metabolism  Reconstruction of draft model via automated homology searches for the identification of native biotransformations; (2) Generation of a computations-ready model after defining biomass equation and system boundary and establishing GPR; (3) Model refinement via GapFind and GapFill A. thaliana−30. This fully automated process generated an initial model, termed as ‘Automodel’, containing 946 genes and 1,365 unique metabolites participating in 1,186 reactions (see The B73 maize genome ‘Draft model’ (see Oryza Sativa (Rice), Arabidopsis thaliana (Arabidopsis), Sorghum bicolor (Sorghum) and less frequently in other plant species such as wheat, tobacco, spinach, soya bean, etc. (See File S3). Notably, 802 orthologous genes from A. thaliana were added in the model Zea mays iRS1563 that were absent from AraGEM primarily due to recent annotation updates. Reactions associated with these genes were subsequently extracted from on-line databases such as KEGG and BRENDA. Genes not included in the automodel were scrutinized further by comparing them against the NCBI non-redundant protein database using the same BLASTp cut-off. This increased the model size to 1,485 genes and 1,703 unique metabolites involved in 1,667 reactions by pulling functionalities absent in AraGEM. This is referred to as the A computations-ready model requires a fully characterized biomass equation, assignment of metabolites to reactions, establishment of GPR associations, localization of reactions in compartment(s), and inclusion of intra- and extracellular transport reactions et al. and expressed on a dry weight basis A biomass equation that drains all necessary precursors present in maize was derived  was distributed across five different intracellular organelles enveloped by the cytoplasmic membrane. Exchange reactions were added in the model to ensure that gaseous metabolites , inorganic nutrient metabolites , sugar metabolites , water and photons could enter and leave the system whenever necessary depending on the physiological state. As shown in The stoichiometric matrix of the draft model see  containsThe draft metabolic model inherently contained gaps, unreachable metabolites, omitted transport mechanisms and missing biomass components. We used the procedures termed GapFind and GapFill −5. In light of the critical need of restoring biomass formation the less stringent cut-off for inclusion was accepted for these genes. Addition of these reactions ensured the production of biomass under all relevant physiological states validating the use of the term ‘Functional’ for the updated model  under any of the relevant three physiological states (e.g. photosynthesis (PS), photorespiration (PR) and respiration (R)). As a result, these blocked reactions prevented the formation of some of biomass precursors. GapFind odel see .−24) and also literature evidence Arabidopsis thaliana has a aldehyde dehydrogenase activity that catalyzes the conversion of phenylacetaldehyde to phenylacetic acid. Hence, by adding this chemical transformation to Zea mays iRS1563 a consumption pathway for phenylacetaldehyde is established. After adding these reactions to the functional model and following charge and elemental balancing and GPR association checking the ‘Final’ Zea mays iRS1563 model  blocked metabolites was restored through the addition of 159 metabolic and 3 inter-organelle transport reactions. odel see  is derivZea mays iRS1563 metabolic reconstruction contains 1,825 unique metabolites and 1,985 reactions associated with 1,563 genes and 876 proteins. Of these reactions 1,898 are metabolic reactions, 70 are inter-organelle transport reactions and 15 are exchange reactions between intra- and extracellular environments. GPR associations are established for all entries  is more complex compared to Arabidopsis or other C3 plants Zea mays iRS1563 captures these differences by accounting for (i) direct carboxylation of phosphoenol pyruvate and CO2 fixation to form C4 acids such as oxaloacetic acid [ATP: oxaloacetate carboxy-lyase (ocl)] and malic acid [Oxaloacetate: NADPH hydrogenase (oha)] in mesophyll cells, (ii) transport of malic acid from mesophyll cell to bundle-sheath cells, (iii) decarboxylation of malic acid [Malate:NADP+ oxidoreductase (mor)] in bundle-sheath cells to produce pyruvic acid and CO2, which enters the Calvin cycle, (iv) transport of pyruvic acid from bundle-sheath cells to mesophyll cells, and (v) production of phosphoenol pyruvic  acid  from pyruvic acid 4 plants. This comes at the expense of higher (ATP) requirements as C4 carbon fixation involves additional steps In plants photosynthesis reactions include light dependent and light independent or carbon fixation reactions p-coumaroyl alcohol, coniferyl alcohol and sinapyl alcohol) that are used in the generation of three major lignin subunits H-lignin, G-lignin and S-lignin, respectively O-methyltranferase (COMT) along with their associated reactions are unique to C4 plants and are not present in the lignin biosynthesis pathways of A. thalianap-coumaroyl-CoA towards caffeoyl-CoA while F5H and COMT regulate fluxes from coniferaldehyde and coniferyl alcohol to sinapaldehyde and sinapyl alcohol, respectively Zea mays iRS1563 contains all these enzymes and associated reactions thus providing a comprehensive lignin biosynthesis pathway for a C4 plant.In addition to photosynthesis, secondary metabolism plays a key role in the physiology of maize. For example, phenylpropanoid metabolism produces monolignols  enzyme that catalyzes the reaction for flavan-4-ols biosynthesis that channels flux towards 3-deoxyanthocyanins production In addition to phenylpropanoid metabolism, Zea mays iRS1563 and the A. thaliana AraGEM genome-scale-models Zea mays iRS1563 are present in AraGEM. This yields a surprisingly low degree of matching between these two models of 64% and 76%, respectively in terms of reactions and metabolites. In the interest of elucidating the true differences between maize and Arabidopsis irrespective of annotation chronology we constructed a more up-to-date genome-scale model for Arabidopsis by appending onto AraGEM newly annotated genes as well as full GPR annotations. We refer to this updated model containing 1,597 genes, 1,798 reactions and 1,820 metabolites as A. thaliana iRS1597 (see File S1). The newly added 228 reactions (absent from AraGEM) are involved in various pathways in primary  and secondary  metabolism of Arabidopsis.Zea mays iRS1563 with A. thaliana iRS1597 reveals, as expected, an increased degree of matching of 72%, 76% and 80% in terms of genes, reactions and metabolites, respectively  and C4  plant metabolism.A direct comparison of vely see . We findZea mays iRS1563 and maize C4GEM genome-scale-models. Degrees of matching between these two models are 39%, 53% and 63% in terms of genes, reactions and metabolites, respectively. This surprisingly low degree of matching is caused primarily due to the fact that maize C4GEM includes only metabolites and reactions in leaves during photosynthesis. Therefore, there are 893 reactions in Zea mays iRS1563 absent from maize C4GEM. 343 of these reactions describe secondary plant metabolism such as brassinosteroid, phenylpropanoid, carotenoid, flavonoid, mono- and diterpenoid, and glucosinolate metabolism. The remaining 550 reactions are found in a wide range of primary metabolism pathways such as central metabolism, photosynthesis, benjoate degradtion, starch and sucrose metabolism, lipid metabolism, nitrogen metabolism amino acid and fatty acid biosynthesis. Conversely, 116 (out of 149) new reactions in maize C4GEM have untraceable EC numbers and gene loci.Zea mays iRS1563 allows for the production of biomass under all three different physiological states (see Files S5 and S6 for detailed information of the model). Due to limited photorespiration C4 plants usually have higher photosynthetic efficiency Zea mays iRS1563 produces 0.0008 mole biomass/mole CO2 whereas A. thaliana iRS1597 yields 0.0006 mole biomass/mole CO2. Thus, the model predictions match with findings reported in literature brown midrib (bm) maize mutants  bm1 and bm3) was elucidated based on the analysis of cell wall composition bm1 and bm3 were found to have disrupted enzymatic activity for cinnamyl alcohol dehydrogenase (CAD) and caffeic acid 3-O-methyltranferase (COMT). Both of these enzymes are involved in the last stages of the monolignol pathway bm1 and bm3 using Zea mays iRS1563 under photosynthetic conditions by restricting the flux of the reactions catalyzed by enzymes CAD and COMT to 10% of the wild-type values. It is expected that the disruption of the activity for these genes will directly affect lignin content and composition (see File S7 to find literature data used for bm1 and bm3 mutants). We were interested to see whether the Zea mays iRS1563 metabolic model will be able to correctly propagate this disruption across the metabolic pathways and correctly predict the effect on other key metabolites. in silico predictions for the maximum theoretical yield of lignins, sugars and crude protein in terms of whether they increased, decreased, or remained the same in the mutant strains. Out of 21 compared components Zea mays iRS1563 correctly predicted the direction (or absence) of change for 17 cases.We simulated mutants bm1 and bm3, respectively. ‘Carbohydrate synthesis’ and ‘Hemicellulose synthesis’ are aggregate reactions that describe the utilization ratios of sugar molecules such as arabinose, fructose, galactose, glucose ribose, mannose, sucrose, and xylose for the production of carbohydrate and hemicellulose present in the plant cell wall. For simplicity, we have simulated the model under the photosynthetic condition where CO2 can be uptaken with a maximum allowable rate of 1000 mM/gDW-h along with photons in excess. In bm1 mutant flux values for reactions involving glucose as reactant including ‘Carbohydrate synthesis’, ‘Hemicellulose synthesis’, ‘Alpha,alpha-trehalose glucohydrolase’ [R00010], ‘Sucrose glucohydrolase’ [R00801], ‘Sn-Glycerol-3-phosphate: D-glucose 6-phosphotransferase’ [R00850] and ‘Cellobiose glucohydrolase’ [R00306], are highlighted. For the wild-type case, the maximum theoretical yield of glucose is predicted to be 1.66 moles/mole of CO2 but it is reduced to 0.93 moles/moles of CO2 for the bm1 mutant. The reduced capability of the bm1 mutant to direct flux towards ‘Carbohydrate synthesis’ and ‘Hemicellulose synthesis’ implies that less glucose is available for the formation of cell wall components which is consistent with the experimental finding of In bm3 mutant maximum theoretical yields for all reactions involving galactose including ‘Hemicellulose synthesis’, ‘ATP: D-galactose 1-phosphotransferase’ [R01092] and ‘Galactosylglycerol galactohydrolase’ [R01104], ‘3-O-alpha-D-Galactosyl-1D-myo-inositol galactohydrolase’ [R01194] and ‘alpha-galactosidase’ [R03634]. A reduction of the maximum theoretical yield of galactose from 0.81 to 0.65 moles/mole of CO2 for the bm3 mutant is observed. In addition, the maximum theoretical yield for reaction ‘Hemicellulose synthesis’ decreases by 4-fold compared to wild-type in line with the experimental finding. However, the experimentally observed increase of glucose availability in mutant bm3 and xylose availability for both bm1 and bm3 mutants are in contrast with the model predictions . Moving a step further, the use of computational strain optimization techniques Unfortunately, existing genetic engineering strategies to reduce lignin content are problematic as lignin reductions are usually achieved at the expense of plant viability and fitness Zea mays iRS1563) for maize metabolism. The model meets (or exceeds) the quality and completeness criteria set out Zea mays iRS1563 can be viewed as a mathematically structured database enabling systematic studies of maize metabolism.185 of unique to maize reactions accounting for a fraction of secondary metabolism were delineated. As a by product of this effort a more up-to-date version of AraGEM Zea mays iRS1563 and maize C4GEM also revealed the detail in description of primary and secondary metabolism. Model predictions of Zea mays iRS1563 for two widely occurring maize Mendelian mutants were tested against experimental observations with very good agreement in the direction of changes. By making use of high throughput enzymatic assays, proteomic and transcriptomic data across different parts of the maize plant, Zea mays iRS1563 could serve as the starting point for the development of tissue-specific maize models Zea mays iRS1563 could also serve as the stepping stone for the development of genome-scale models for other important C4 plants such as Sorghum and switch grass.In this paper, we introduced the first comprehensive genome-scale metabolic model  and (4) were respectively included in the linear model, whereas for respiration both constraints (3) and (5) were included.Flux balance analysis (FBA) bm1 and bm3) under the photosynthetic condition. The following two constraints were included individually in the linear model to represent the mutants:w represents the percent of residual activity of 10%. bm1v and bm3v are the fluxes of reactions catalyzed by CAD and COMT, respectively and bm1WF and bm3WF are the corresponding wild-type flux values under the photosynthetic condition.Once the model was validated, it was further tested for two maize mutants  was used in the GAMS  environment for implementing GapFind and GapFill File S1A. thaliana iRS1597 model along with established GPR for AraGEM.(XLS)Click here for additional data file.File S2Zea mays iRS1563 model development.Sequential stages of (XLS)Click here for additional data file.File S3Results from BLASTp searches against NCBI nr database.(XLS)Click here for additional data file.File S4Zea mays iRS1563 model.Biomass equation, added reactions for restoring biomass production under all three physiological conditions and unique reactions in (XLS)Click here for additional data file.File S5Zea mays iRS1563 model.(XLS)Click here for additional data file.File S6Zea mays iRS1563 model.SBML file of (XML)Click here for additional data file.File S7Zea mays iRS1563 model.Literature data used for testing (XLS)Click here for additional data file.


Listeria monocytogenes during infection. Twelve metabolic pathways were identified as differentially active during L. monocytogenes growth in macrophage cells. Intracellular replication requires de novo synthesis of histidine, arginine, purine, and branch chain amino acids (BCAAs), as well as catabolism of L-rhamnose and glycerol. The importance of each metabolic pathway during infection was confirmed by generation of gene knockout mutants in the respective pathways. Next, we investigated the association of these metabolic requirements in the regulation of L. monocytogenes virulence. Here we show that limiting BCAA concentrations, primarily isoleucine, results in robust induction of the master virulence activator gene, prfA, and the PrfA-regulated genes. This response was specific and required the nutrient responsive regulator CodY, which is known to bind isoleucine. Further analysis demonstrated that CodY is involved in prfA regulation, playing a role in prfA activation under limiting conditions of BCAAs. This study evidences an additional regulatory mechanism underlying L. monocytogenes virulence, placing CodY at the crossroads of metabolism and virulence.Intracellular bacterial pathogens are metabolically adapted to grow within mammalian cells. While these adaptations are fundamental to the ability to cause disease, we know little about the relationship between the pathogen's metabolism and virulence. Here we used an integrative Metabolic Analysis Tool that combines transcriptome data with genome-scale metabolic models to define the metabolic requirements of  Listeria monocytogenes. We identified 12 metabolic pathways to be differentially active during infection in comparison to growth in rich lab media. We validated the essentiality of the active pathways for L. monocytogenes intracellular replication. Pathways included: biosynthesis of histidine, arginine, purine, and branch chain amino acids (BCAAs), as well as the catabolism of L-rhamnose and glycerol. Next we analyzed whether the requirement for these nutrients associates with virulence. We found that limiting concentrations of BCAAs, primarily of isoleucine, results in robust induction of the bacterial virulence state, a response that is dependent on the isoleucine responsive regulator, CodY. CodY was responsible for the up-regulation of the major virulence regulator of L. monocytogenes, PrfA. This study supports the premise that pathogens metabolism and virulence are closely interlinked.Intracellular bacterial pathogens have developed sophisticated mechanisms to invade and replicate within eukaryotic cells. For successful replication, pathogens have adapted metabolically to the intracellular niche. While this adaptation is fundamental to the ability to cause disease, we know little about pathogen's intracellular metabolism and its association with virulence. In this study we took a global approach that combines computational and experimental methods to decipher the intracellular metabolic requirements of the human bacterial pathogen  Legionella pneumophilla and Mycobacterium tuberculosis, actively modify their compartment via secretion of effector proteins that enrich the vacuole with nutrients to support growth; nevertheless growth rate in the vacuole is much slower than in rich media. In contrast, intracellular cytosolic bacteria, such as Listeria monocytogenes, Sheigella felxenri and Burkholderia pseudomallei manage to exploit their niche such that growth rates resemble growth in rich media Intracellular bacterial pathogens have developed sophisticated mechanisms to enter eukaryotic cells and replicate within them. These mechanisms involve bacterial proteins that overcome host defense strategies and barriers as well as nutritional limitations. Intracellular pathogens are generally categorized into two groups based on the compartment where they replicate, the vacuole/phagosome or the host cell cytosol. Each intracellular niche presents unique nutritional challenges demanding that bacteria exhibit specific metabolic adaptations to proliferate successfully. Vacuolar pathogens, such as L. monocytogenes is a Gram-positive facultative intracellular bacterial pathogen and the causative agent of listeriosis in humans, a disease with a variety of clinical manifestations including meningitis and abortion L. monocytogenes infects phagocytic and non-phagocytic cells, using surface expressed proteins called internalins, which bind and induce bacterial uptake by endocytosis L. monocytogenes escapes from the phagosome/vacuole into the host cytosol by producing the pore-forming hemolysin toxin, listeriolysin O , and two additional phospholipases L. monocytogenes multiplies rapidly and expresses the surface protein, ActA, which recruits the host actin polymerization machinery to propel the bacteria in the cytosol and facilitate spread from cell to cell L. monocytogenes uses several carbon sources during intracellular growth, but primarily glycerol, di-hydroxyacetone and phosphorylated carbohydrates (such as glucose 1-phosphate), indicating the availability of these substrates in the cytosolic niche L. monocytogenes. Carbohydrates transported via the phosphoenolpyruvate phosphotransferase system (PTS) (such as glucose and cellobiose) repress the activity of PrfA whereas non-PTS carbon sources (such as glycerol and glucose 1-phosphate that are available in the host cytosol) induce PrfA activity, resulting in elevated expression of virulence genes [reviewed in: L. monocytogenes of its intracellular location.L. monocytogenes, including several amino acid biosynthesis pathways such as the branch chain amino acid (BCAA) and arginine pathways 1) L. monocytogenes to the cytosolic niche is its ability to obtain the co-factor lipoate from the host, as it cannot be synthesized by the bacteria. Listerial expression of a lipoate ligase, LplA1, enables the co-factor to be derived from host lipoyl-peptides L. monocytogenes utilizes ammonium, arginine and ethanolamine Salmonella, Enterococcus and ClostridiumSalmonella, listerial genomes encode the ethanolamine utilization pathway Various additional metabolic pathways were indicated as important for intracellular replication of L. monocytogenes metabolism has been developed L. monocytogenes during infection.Recent advances in genomic sequencing and development of constraint-based metabolic models enable now the reconstruction of genome scale metabolic networks of different organisms. This approach enables a better comprehension of the complete metabolic network of an organism under different growth conditions as well as the prediction of essential metabolic genes To model the metabolic state of an organism under a given set of conditions, the availability of nutrients needs to be simulated. This information is mostly unknown for complex systems, but a specific metabolic state can be specified by integrating relevant transcriptome, proteome or metabolome (‘omics’) data into the metabolic model. A computational tool that performs this data integration was recently developed, the integrative Metabolic Analysis Tool (iMAT) L. monocytogenes to better understand the bacterial metabolic requirements during growth in macrophage cells. Using this approach several metabolic pathways were predicted active or inactive during intracellular growth, potentially reflecting the metabolites availability within these cells. We experimentally deleted key genes in the pathways predicted to be active, validating their contribution to L. monocytogenes intracellular replication and studied the association of the corresponding metabolites in regulation of L. monocytogenes virulence genes.In this study we applied iMAT analysis to transcriptome data and a genome scale metabolic model of L. monocytogenes (strain 10403S) growing in two different conditions were assessed using whole genome microarray analysis. RNA was extracted from L. monocytogenes grown either inside macrophages  or in brain heart infusion (BHI) broth (mid log phase). Transcription levels in intracellular bacteria were designated as relative to those in bacteria growing in BHI. Gene expression was coded as 1, −1 or 0 for each gene, indicating a minimum of twofold increase, decrease, or no change, respectively, in transcript levels during intracellular growth versus growth in BHI . We then applied a standard hypergeometric test for pathway enrichment taking into account each metabolic reaction observed to change (up or down) to determine which pathways were enriched for altered activities. A metabolic pathway significantly enriched with up-regulated reactions was considered highly active, while a metabolic pathway enriched with down-regulated reactions was considered inactive  biosynthesis and glycerol utilization as pathways highly active during intracellular replication The global transcriptional profiles of h in BHI . Next, winactive . In agreL. monocytogenes. We defined a simulated medium that best represents the cytosolic environment . iMAT returns a three-valued output, rating the predicted activity of each reaction as confidently active, confidently inactive, or unknown. This output was analyzed by computing the hypergeometric enrichment score of each pathway based on the predicted tri-valued flux activity of each of its reactions   . The precision and recall for iMAT analysis are P = 0.583, R = 0.5, compared to that of the gene expression analysis P = 0.428, R = 0.357. Overall iMAT analysis resulted in a predicted metabolic shift in 12 metabolic pathways upon growth of L. monocytogenes within macrophages, five of which were missed by conventional gene expression analysis. Notably, both methods predicted the novel contribution of histidine biosynthesis and rhamnose utilization to L. monocytogenes intracellular replication.Next, we applied the iMAT algorithm to predict metabolic flux activity during intracellular growth of ironment , success manner) . In lineactions) . Indeed,r growth , revealeL. monocytogenes all the chosen genes are central and cannot be compensated . All six metabolic genes were up-regulated during intracellular growth whereas rpoD and bglA, two control genes, remained unchanged. As another control the transcription level of fabF gene (lmo2201), part of the FASII pathway that was predicted to be down-regulated intracellularly, was analyzed and indeed this gene was shown to be down-regulated during intracellular growth in macrophage cells in comparison to growth in BHI media  each one demonstrated a growth defect when the metabolite of their targeted pathway was depleted. As expected, introducing back the corresponding genes complemented the growth defects of these mutants. When tested for growth in BHI, all mutants, with the exception of ΔaroE , we investigated if low availability of BCAAs, histidine, arginine and purine might serve similarly as an intracellular signal that modulate the transcription of the virulence genes. Therefore, we tested whether the regulation of these four pathways is linked to the regulation of virulence genes. To this end, WT L. monocytogenes was grown in BHI, minimal defined medium (MDM) and MDM with reduced concentrations (10-fold less) of arginine, histidine, adenine and BCAAs , which aimed to mimic the intracellular free amino acids concentration prfA the master virulence activator gene, hly gene encoding for LLO, and actA gene) were then analyzed at mid-log phase. The expression of the metabolic genes  was induced during growth in MDM relative to growth in BHI rich medium  during growth in BHI, MDM and MDM with reduced concentration of isoleucine, using a lux reporter system. Wild type bacteria were conjugated with an integrative plasmid containing lux operon under the regulation of the hly promoter (pPL2-Phlylux) 600 nm) and luminescence were taken. As shown in hly promoter was observed during logarithmic phase under limiting concentrations of isoleucine. Notably, this response was not observed during stationary phase in any of the tested conditions, suggesting that a specific regulatory mechanism involves the activation of the virulence genes under low concentrations of isoleucine  represses genes that are required for adaptation to nutrient limitation L. monocytogenes virulence genes under low isoleucine concentrations. A ΔcodY complete gene deletion mutant was generated, conjugated with pPL2-Phlylux plasmid and tested under similar conditions. Interestingly, deletion of codY gene did not result in activation of hly transcription, as predicted by its role as a repressor, rather the induction of the hly promoter under isoleucine limiting conditions was strongly dependent on CodY   .ilvC and purH genes were analyzed in the ΔcodY mutant and WT bacteria grown under varying concentrations of BCAAs. Notably, we observed that while ilvC and purH were highly induced when BCAAs were limiting , this induction was largely dependent on CodY. However, under high BCAAs concentrations (i.e. in BHI), the ΔcodY mutant also exhibited up-regulation of ilvC and purH, but to a much lesser extent than under conditions of BCAAs depletion alone    . OverallV2-codY) .i.e., hly and actA genes) is direct or mediated by PrfA, the induction of both hly and prfA promoters by CodY was assessed using the lux reporter system. prfA transcription is initiated from two distinct regions, one proximal encoding two overlapping promoters named P1 and P2, and a second distal region encoding the P3 promoter located up-stream of plcA-prfA genes . Notably, while induction of hly and P3 promoters required both PrfA and CodY  biosynthesis pathway during L. monocytogenes intracellular growth, we show here for the first time that the rhamnose utilization pathway is induced and important during infection. This finding relates to an old observation that rhamnose fermentation could serve as a marker to distinguish between pathogenic and non-pathogenic isolates of L. monocytogenesL. monocytogenesEscherichia coli ΔrhaB mutant displays higher resistance to cell wall antibiotics Bacillus anthracis mutant defective in dTDP-rhamnose synthesis was shown to adhere less to macrophages L. monocytogenes rhaB gene encoding rhamnulose kinase is transcriptionally up regulated a 100-fold during intracellular growth. In L. monocytogenes rhamnose residues are known to decorate teichoic acids and were suggested to be antigenic determinants L. monocytogenes modifies its teichoic acids to evade recognition and avoid triggering the innate immune system. The observed metabolic shift in rhamnose catabolism could directly affect the composition of the cell wall and in this way, bacterial survival during infection. Future studies are necessary to delineate why this pathway is actively required during L. monocytogenes intracellular growth. Overall, our findings that several amino acid biosynthesis pathways are required for growth of L. monocytogenes in macrophages imply that these essential metabolites might be limited in the host cytosol or that the bacteria do not transport them efficiently during intracellular growth. Differentiation between these possibilities requires further studies. It will also be important to discover if these nutrients are limiting in the absence of infecting bacteria or alternatively, are actively depleted from macrophage cytosol as a mechanism to prevent bacterial replication.In addition to the discovery that histidine biosynthesis is required for i.e., that low availability of BCAAs, and primarily of isoleucine, leads to the induction of PrfA and in turn, of PrfA-regulated genes. The isoleucine binding transcription regulator, CodY, is directly linked to this response and, as shown, responsible for a positive regulation of prfA. Notably, biosynthesis of BCAAs lies at the crossroads of bacterial metabolism. The synthesis of valine and leucine requires the α-keto acid pyruvate and an amino group from glutamate, and thus is dependent on both glycolysis and the TCA cycle. Furthermore, isoleucine is synthesized from α-ketobutyrate, which is required for sulfur metabolism. Thus BCAA levels indicate the overall carbon, nitrogen, and sulfur metabolic status of the bacteria Bacillus subtilis and Streptococcus pneumonia have demonstrated that it can also function as a transcriptional activator L. monocytogenes, CodY was shown to repress genes involved in amino acid metabolism, nitrogen assimilation and sugar uptake, and was suggested to affect virulence via a functional association with RelA prfA via the proximal P1P2 promoters. It is still unclear whether CodY directly interacts with the prfA promoters or that this effect is mediated indirectly by another CodY-regulated protein. In summary, we propose that there are several regulatory connections between L. monocytogenes intracellular metabolism and virulence, with BCAAs and carbon source availability playing a key role in the cytosolic nutrient signature that signals the bacteria for their intracellular location.Pathogenic bacteria must express virulence factors to exploit nutrients within their host therefore it is not surprising that metabolism and virulence are closely linked. This intimate relationship is best demonstrated by the finding that intracellular carbon sources trigger up regulation of PrfA This study is the first to address directly the ability of iMAT to serve as a tool to study the metabolic requirements of microorganisms in their natural habitat. We establish that iMAT analysis provides a more comprehensive overview of the activity of the metabolic network of an organism in a given condition. Yet, model-based analyses such as iMAT still suffer from limitations, primarily due to incomplete annotation. In addition, metabolic virulence factors like Hpt and LplA1 The use of animals in this study was limited to preparation of bone marrow derived macrophages from mice. Experimental protocols were approved by the Tel Aviv university Animal Care and Use Committee (L-09-008) according to the Israel Welfare Law (1994) and the National Research Council guide .L. monocytogenes 10403S was the wild type strain (WT) and served as the parental strain when generating gene deletion mutants (E. coli XL- 1 Blue (Stratagene) was used for vector propagation and E. coli SM-10 strain L. monocytogenes. The full list of the strains and plasmids used in this study are described in L. monocytogenes bacteria were grown at 37°C with agitation in brain heart infusion (BHI) as rich medium or in minimal defined medium (MDM), which is identical to the improved minimal media (IMM) described in i.e., leucine, isoleucine, valine, methionine, arginine, histidine, tryptophan, glutamate, cysteine and phenylalanine), 600 µg/ml glutamine, 0.5 µg/ml biotin, 0.5 µg/ml riboflavin, 20 µg/ml ferric citrate, 1 µg/ml para-aminobenzoic acid, 5 ng/ml lipoic acid, 2.5 µg/ml adenine, 1 µg/ml thiamine, 1 µg/ml pyridoxal, 1 µg/ml calcium pantothenate and 1 µg/ml nicotinamine. For growth under limiting concentrations of nutrients, MDM was freshly made with 10-fold less of the indicated nutrients: BCAAs, histidine, arginine, adenine, phenylalanine and tryptophan . mutants . E. coli6L. monocytogenes were used to infect 2×106 macrophage cells seeded in a 60 mm Petri dish, resulting in 1–2 bacteria per cell. Thirty minutes after infection, macrophage monolayers were washed three times with PBS and fresh media added. At 1 hour post infection (h.p.i.) gentamicin (50 µg/ml) was added to limit bacterial extracellular growth. Intracellular growth was evaluated as follows. Macrophages were seeded on 13 glass cover slips in a 60 mm plate. At each time point three cover slips were removed and transferred to 2 ml of double-distilled sterile water, which released intracellular bacteria. Then serial dilutions of this 2 ml were plated on BHI plates and colony-forming units (CFUs) counted the next day. Phagosomal escape assay was performed as previously described L. monocytogenes expressing GFP (pPL2-GFP integrative plasmid) Bone marrow derived macrophages (BMDM) were used for infection experiments and were isolated from 6–8 week old female C57/BL6 mice (Harlan laboratories) as described previously L. monocytogenes microarrays were printed at the Microarray Core Facility of University of California San-Francisco, using the L. monocytogenes oligo set designed and provided by The Institute for Genomic Research (TIGR) RNA was harvested from bacteria growing mid-exponentially in BHI medium and from bacteria growing inside macrophages 6 h.p.i. (at which time the bacteria are cytosolic) as described previously 600 of 0.35. In all cases precultures were grown in MDM media overnight prior to the experiments. One microgram (1 µg) of RNA was reverse transcribed to cDNA using the High Capacity reverse transcription kit (Applied Biosystems). RT-qPCR was performed on 10 ng of cDNA using SYBER Green in a Step-one Plus real time PCR system (Applied Biosystems). The transcription level of each metabolic gene was normalized to that of a reference gene: 16S rRNA in the intracellular experiments and rpoD mRNA in the minimal medium experiments. Statistical analysis was performed using the StepOne™ V2.1 software. RT-qPCR primers are described in For validation of microarray data the intracellular transcription levels of bacterial metabolic genes were analyzed using real-time quantitative PCR (RT-qPCR) at 6 h.p.i. For the analysis of metabolic genes and virulence genes during growth in BHI medium, MDM and MDM with limiting concentrations of nutrients, RNA was harvested in mid log growth phase at O.D.vs. BHI grown bacteria. The metabolic reactions and pathways of L. monocytogenes were obtained from the genome scale automated metabolic model −1 g−1 of dry weight, in silico flux balance analysis simulations of bacterial replication, best representing the experimentally observed bacterial generation time. In both analyses, a correction for multiple hypotheses was done using the false discovery rate (FDR) method. Metabolic pathways with less than 3 reactions were neglected in the analysis. For statistical analysis of predictions accuracy, hypergeometric tests were applied to compare the predictions of both methods to a literature-generated database of metabolic pathways considered essential or non-essential during intracellular growth of L. monocytogenes  hly promoter pPL2-Phlylux was used and was a gift from Colin Hill prfAlux and pPL2-P3plcA/prfAlux reporter plasmids, the intragenic regions upstream of the prfA and plcA genes were cloned into the pPL2-lux plasmid. L. monocytogenes precultures were grown in BHI or MDM media  overnight and then diluted to O.D.600 of 0.03 in fresh media as indicated. Bacteria were grown in a Synergy HT Biotek plate reader at 37°C. O.D.600 nm and luminescence measurements were taken every 15 min.For luminescence assays, a plasmid harboring the Figure S1Listeria monocytogenes and metabolic mutants in MDM. B. Growth of WT L. monocytogenes, ΔhisC and ΔhisC+pPL2-hisC strains in MDM without histidine. C. Growth of WT L. monocytogenes, ΔilvC and ΔilvC+pPL2-ilvC strains in MDM without BCAAs. D. Growth of WT L. monocytogenes, ΔargD and ΔargD+pPL2-argD strains in MDM without arginine. E. Growth of WT L. monocytogenes, ΔpurH and ΔpurH+pPL2-purH strains in MDM without adenine F. Growth of WT L. monocytogenes, ΔrhaB and ΔrhaB+pPL2-rhaB strains in MDM with 10 mg/ml of L-rhamnose instead of D-glucose. The results represent 3 independent experiments (N = 3). Error bars represent standard error of the mean.A. Growth of WT (TIF)Click here for additional data file.Figure S2L. monocytogenes and the metabolic mutants in the rich lab media BHI. The results represent 3 independent experiments (N = 3). Error bars represent standard error of the mean. The ΔaroE mutant that exhibited a growth defect was complemented by introducing the aroE gene on pPL2.Growth of WT (TIF)Click here for additional data file.Figure S3L. monocytogenes, ΔpurH and ΔargD mutants and their complemented strains. B. Intracellular growth curves of WT L. monocytogenes, ΔaroE and ΔilvC mutants and their complemented strains. The results are representative of 3 independent experiments (N = 3). Error bars represent standard error of the mean.Intracellular growth curves of the metabolic mutants and their complementation strains. A. Intracellular growth curves of WT (TIF)Click here for additional data file.Figure S4L. monocytogenes in BHI, MDM and in MDM with reduced (Low) concentrations (10 fold less) of Arg, His, Ade and BCAAs. Precultures were grown in MDM overnight. For RNA extraction, the bacteria were harvested at O.D. of 0.3, which in all cases represented logarithmic growth. The results represnt two independent expreiments (N = 2). Error bars represent standard error of the mean.Growth curves of WT (TIF)Click here for additional data file.Table S1L.m. vs. BHI grown L.m.Gene expression analysis of intracellular (XLSX)Click here for additional data file.Table S2L. monocytogenes and stimulated medium used in this study.The modified metabolic model of (XLSX)Click here for additional data file.Table S3L. monocytogenes.Metabolites confidently predicted by iMAT to be imported or exported by intracellular (XLSX)Click here for additional data file.Table S4L. monocytogenes intracellular growth.Literature based “gold standard” of metabolic pathways that are essential or non-essential to (XLSX)Click here for additional data file.Table S5List of strains and plasmids used in this study.(XLSX)Click here for additional data file.Table S6List of Primers used in this study.(XLSX)Click here for additional data file.


Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints.Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol.A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations. Significant advances have been made in modeling ethanol fermentations [Management of ethanol yields is emerging as one of the most relevant challenges for biotechnology, including both ethanol maximization  mainlA widely used modeling approach to predict cell behavior beyond calibration data is Flux Balance Analysis (FBA), which represents cell biochemical networks as a set of underdetermined constrained mass-balances. In this framework, linear programming is applied to generate a flux distribution that optimizes a given objective function, subject to flux balance equations and constraints. Objective functions commonly used are maximization of ATP production ,10, maxii.e. growth rate and product secretion patterns [Applying FBA in large-scale metabolic reconstructions, termed Genome-Scale metabolic models (GSMM), has allowed establishing a direct relationship between genetic data and metabolic activity fluxes. These models have been shown to be very useful in predicting the physiological behavior of a microorganism under different genetic and environmental disturbances, patterns ,13,17-19patterns -22. For patterns , lactatepatterns , succinapatterns , 1,3-propatterns , hydrogepatterns , L-valinpatterns  and L-thpatterns . In turnpatterns -31; nevepatterns ,32-34.i.e. growth as well as non-growth periods, such as lag, log and steady-state phases. Furthermore, strain optimization needs microbial metabolic modeling expanded upon constraint-based FBA that incorporates experimental validation [Many computational tools for identifying strain modifications leading to targeted overproductions have been described in the literature. One of the earliest efforts was the OptKnock  procedurlidation , which hSeveral yeast genome-scale metabolic models have been described so far: iFF708, iND750, iLL672, iIN800, iMM904 and Yeast 4.0 ,19,38-41S. cerevisiae batch fermentations [S. cerevisiae metabolism [S. cerevisiae.Over the past few years, our group has been developing a dynamic FBA model (DFBA) to represent anaerobic ntations ,47. Thistabolism ,47 to geThe current version of our genome-scale dynamic-flux-balance-analysis model (GS-DFBA) consists of four interacting blocks, which are solved sequentially Figure . First, a priori are temperature, and the concentration of nitrogen-compounds, sugar-compounds, extracellular metabolites and biomass. The LP is bounded by fixed and dynamic constraints. Fixed constraints include carbon/nitrogen limiting medium, oxygen presence or absence, biomass equation, and genetic background. Dynamic constraints include sugar/nitrogen uptake kinetics, maintenance and carbohydrate accumulation. Following this procedure, it takes 43 s of CPU time to simulate a normal alcoholic fermentation (200 g/L of sugars and 300 mg/L of assimilable nitrogen) in a Core2Duo 2.66 GHz processor. The features of each block are detailed below . To solve the intracellular fluxes as well as the consumption and production rates of metabolites in the cell, an LP is defined (eq. 1), where an objective function for cell metabolism vj should be specified. Following common practice, maximization of growth rate was used to mimic growth in exponential phase [A standard FBA model ,55,56 coal phase -13; howeal phase ,16.(1)v is the vector of metabolic fluxes, and i, respectively. All LP calculations were carried out within MATLAB (The MathWorks Inc.) using Lindo optimization package (Lindo Systems Inc.).where i.e. maximizing/minimizing ethanol).To reduce the impact of multiple optimal solutions that normally appear in FBA , for metUp until now, several yeast genome-scale model databases have been published: iFF708 , iND750 in silico, consists of 715 structural ORF's that catalyze 705 distinct biochemical reactions and 145 putative reactions that have not been associated with any gene yet, comprising 590 metabolites and 1181 metabolic reactions in total. Compared with iFF708, the idFV715 model includes a more detailed description of flavor production related to acetoin and butanediol syntheses.The first published genome-scale model, iFF708, was the simplest and has good predictive performance. Therefore, The GS-DFBA model described here was developed using an updated version of the iFF708 stoichiometric matrix  coupled During microbial growth, the environmental conditions are changing constantly due to the consumption of nutrients and secretion of metabolites. These changes are modeled by the following set of ordinary differential equations:EX Mis the vector of concentrations of extracellular metabolites in the medium, EX Vis the vector of the respective specific consumption and production rates, and V Xis the concentration of viable biomass in the medium.Where These include all constraints that remain constant during the whole fermentation, such as genetic background , carbon/nitrogen limiting medium that modify the biomass equation, and lack of oxygen that modify oxygen-related reactions.S. cerevisiae physiology under these conditions as closely as possible. The expression of approximately one hundred genes is regulated by oxygen availability [As yeast responds differently to aerobic or anaerobic conditions -60 we delability ,62, affelability . This, ilability ,65.i.e. during anaerobic fermentation the TCA cycle has been shown to be broken at the level of succinyl CoA synthetase, acting as two separate branches - the "oxidative branch" and the "reductive branch." This behavior was first established as thermodynamically feasible in S. cerevisiae [2 and NADH reoxidation were turned off. Finally, ergosterol, lanosterol and zymosterol upper bound constraints, UB, were set to infinity (assumed to be non-limiting) in anaerobic conditions [In our approach, we bounded to zero the oxygen uptake-related flux. In addition, we included a known transcriptional regulation, revisiae , and therevisiae . Later, revisiae  experimenditions -71.Furthermore, to ensure the presence of inorganic compounds in every condition studied, phosphate and sulphate concentration in the medium were also set to infinity (UB assumed to be non-limiting).Sugar transport is the main rate-limiting step in N-limited fermentations . Known rYeast Assimilable Nitrogen) consumption. We incorporated normalized time-varying fluxes for each nitrogen-compound (amino acids/ammonia) into a previously reported equation for total nitrogen consumption [An empirical function was developed to describe total YAN  [ATP consumption is set by an empirical function that assigns maintenance costs according to fermentation conditions [Yeast faces changing conditions during fermentation, triggering metabolic stress responses and increasing ATP consumption by maintenance (mATP) ,76,77, aATP) . High teATP) ,80. In aATP) . Therefonditions   . These wIn silico detrimental gene deletions lead to lower growth rates compared to wild-type simulations, yielding lower biomass and/or longer fermentation times.The impact of the gene modifications on simulations of batch fermentations, as measured in final product concentrations, was compared with literature data. To simulate the effect of a single gene deletion, the fluxes through the corresponding reactions were constrained to zero during the whole fermentation ,42. In sWe assessed the predictive performance of the idFV715 model by comparing simulation results with 35 different environmental and genetic modifications reported in the literature. These include overexpressions -85, deleThis expression compares experiments and simulations in terms of the relative changes in the final concentration of given metabolites. Here, D is the modeling (M) or experimental (E) concentration of the metabolite "i" when it is genetically modified (GM) or wild type (WT). Hence, values of R close to zero mean good model predictions.Fermentation conditions that were not given in the literature were estimated to reproduce the respective experimental results as closely as possible. For example, environmental conditions such as temperature, oxygen, sugars, amino acids, ammonia, ethanol and glycerol, were normally given in the literature, but vitamins and inorganic compounds of the medium were not. In addition, deletions and insertions were well described in the literature references; however, the levels of overexpression were not given. Therefore, due to the nature and diversity of information sources, estimating fermentation conditions was often difficult and an additional source of error.S. cerevisiae metabolism [In this study, we expanded a small and reliable 39-equation DFBA model of tabolism ,47 to geTo assess the new model, first we analyzed the impact on simulation performance of key model improvements. Then, we validated model results with experimental data.Proper choice of the biomass equation is normally considered a critical component in FBA-based modeling -40,82, aFermentations were simulated anaerobically in N-limiting medium; the variables were low and high temperatures, as well as sluggish and normal conditions. In each case, four maintenance models were assessed: i) mATP constrained according to eq. (4)  [The most significant difference among model simulations was glycerol prediction. When maintenance flux is constrained according to our maintenance equation  ,63, the Ethanol prediction also showed significant differences among models. A decrease in glycerol synthesis results in increased carbon fluxes through glycolysis. Hence, unbounded, zero maintenance and constrained-to-previous-value simulations result in higher ethanol contents than those obtained with our maintenance equation , compared to lean-nitrogen cultures. Underestimation of cell growth under low-nitrogen conditions strongly suggests that cells maximize the use of nitrogen compounds under nutrient limitation, a fact that is not accounted in idFV715.To assess the impact of anaerobic constraints on model predictions, we performed simulations by varying the activation or inhibition of the following specific constraints: i) oxygen uptake constrained to zero, ii) unconstrained sterol and unsatured fatty acids uptake, iii) TCA cycle split up into two branches, and iv) quinone-mediated reactions constrained to zero Table .S. cerevisiae cannot synthesize these compounds (ergosterol and zymosterol) under anaerobic conditions [Simulations in the absence of oxygen were only feasible when the availability of unsaturated fatty acids and sterols is not constrained Table : model 6nditions -71, thisnditions ,30.However, restrictions to TCA and quinone-reactions have received little attention and few studies have incorporated transcriptional regulatory information to respond to these environmental conditions . We foun2 and NADH play a fundamental role in aerobiosis (not shown); however, under anaerobiosis their effect worsens model predictions. When these reactions are turned off, predictions of fermentation time, biomass, glycerol, and ethanol synthesis are closer to measured values  batch fermentation kinetics; and ii) final concentration of metabolic products under genetic/environmental disturbances. These analyses highlight the differences with our previous model while maintaining model performances.wt yeasts were used. The first set corresponded to 20 laboratory fermentations under different temperature and initial concentrations of nitrogen and sugars. Ten industrial wine fermentations with different initial glucose concentrations and a time varying temperature profile comprised the second set.Two sets of batch fermentations with Table i.e. ammonia and amino acids kinetics, at several initial nitrogen conditions  regarding ethanol and acetate were different; they obtained twice the amount of ethanol and almost 7 times more acetate was consumed.Instead of predicting exact final concentration values, the model is able to reproduce the relative effects and trend of given genetic engineering strategies on final metabolites concentrations. For example, our model reproduced the experimental results of Guadalupe et al. (2010) . These aS. cerevisiae under anaerobic fermentation. Here, we showed that not only biomass expression is a critical component of FBA-based modeling but also ATP maintenance and anaerobic constraints. Furthermore, it is noteworthy that a detailed description of nitrogen-compounds uptake helps to improve results. Therefore, under the conditions studied here, idFV715 closely agrees with final concentrations as well as fermentation profiles of the main metabolites involved in alcoholic fermentation, especially ethanol, glycerol and biomass. Minor compounds such as acetate are not well predicted though, mainly due to unaccounted genetic regulations and model sensitivity. A careful examination in the definition of lipids and cell-wall components could help to improve results.In this paper, we described the first experimentally validated GS-DFBA model for alcoholic fermentations. Model validation included fermentation profiles and final concentration of fermentation products under different environmental and/or genetic disturbances. We implemented a set of constraints that resulted in key improvements in the physiological response of S. cerevisiae under different environmental conditions as well as different genetic backgrounds. Given the close predictions of idFV715 regarding ethanol synthesis, this model is especially suitable to design yeast strains for optimum ethanol management in batch fermentations. This model and its future improvements can be used to design new metabolic engineering strategies to optimize fermentation, as well as product synthesis.Consequently, idFV715 could be useful to predict the evolution and concentration of the main metabolites from DFBA: Dynamic Flux Balance Analysis; GSMM: Genome-Scale Metabolic Model; GS-DFBA: Genome-Scale, Dynamic Flux Balance Analysis; MFA: Metabolic Flux Analysis; MoMA: Minimization of Metabolic Adjustments; NMR: Nuclear Magnetic Resonance; LP: Linear Programming; ODE: Ordinary Differential Equation; TCA: Tricarboxylic acid; YAN: Yeast Assimilable Nitrogen.The authors FAV, FP, JRP and EA, conceived and designed the experiments. FAV performed the experiments and analyzed the data. All authors have read and approved the final manuscript.Sensitivity analyses of the idFV715 model. This file includes sensitivity analyses of the effect of time step integration and acetate production in the idFV715 model.Click here for fileKinetic expression used in the idFV715 model. This file includes kinetic expression used in the idFV715 model. Sugar, nitrogen and maintenance expressions are detailed.Click here for fileModel validation. This file includes tables where results using previous DFBA model can be directly compared to current model results of Table Click here for file