SBM2 – Framework) implements the developed method and, as an example, is applied for the integrative analysis of the crop plant potato.The characterization of biological systems with respect to their behavior and functionality based on versatile biochemical interactions is a major challenge. To understand these complex mechanisms at systems level modeling approaches are investigated. Different modeling formalisms allow metabolic models to be analyzed depending on the question to be solved, the biochemical knowledge and the availability of experimental data. Here, we describe a method for an integrative analysis of the structure and dynamics represented by qualitative and quantitative metabolic models. Using various formalisms, the metabolic model is analyzed from different perspectives. Determined structural and dynamic properties are visualized in the context of the metabolic model. Interaction techniques allow the exploration and visual analysis thereby leading to a broader understanding of the behavior and functionality of the underlying biological system. The System Biology Metabolic Model Framework ( At least 54 bacterial, 6 archaeal, and 16 eukaryotic reconstructions are available to-date while many others are under development  includes detailed quantitative descriptions on the biochemical processes and therefore requires often difficult to obtain kinetic rate equations and parameters. Due to this, kinetic modeling is generally limited to smaller models, but leads to quantitative predictions and reveals dynamic behavior of the underlying biological system , which are related to each other through edges.specific graphs representing qualitative and quantitative metabolic models . Using our method, the analysis results from different formalisms are visualized in the context of the metabolic model through data assignment functions  is defined as follows.With the aim to formally represent qualitative and quantitative metabolic models a directed, attributed, bipartite graph . The unified graph GUnified =  is a directed, attributed, bipartite graph consisting of two finite, non-empty sets M of metabolites and R of reactions, whereby both sets are disjoint M ∩ R = ∅. Other finite sets are directed edges E ⊆ (M × R) ∪ (R × M) and attributes A = {type, stoichiometry, localization, label, concentration, capacity, rate, boundaries, objective function}, which are assigned to nodes and edges using the following functions:type: E → {ci, pi, cr, pr, i} is a function, which assigns a type to each edge . A directed edge from a metabolite to a reaction is of type ci, cr, or i  and a directed edge from a reaction to a metabolite is of type pi or pr . To easily distinguish between reversible and irreversible edges, reversible edges are illustrated using a double-headed arrow, with the black arrow-head denoting the main direction from substrate (consumed metabolite) to product (produced metabolite) of a reaction.stoichiometry: E′ → ℝ>0 is a function, which assigns a positive real number greater than 0 to each edge of type ci, cr, pi, or pr out of the set E′ = {e ∈ E| ¬ (type(e) = i)}.label: M ∪ R → Σ* is a function, which assigns a word over the alphabet to each metabolite and each reaction.localization: M → Σ* is a function, which assigns a word over the alphabet to each metabolite.capacity: M → ℝ≥0 ∪ {∞} is a function, which assigns a positive real number or infinity {∞} to each metabolite.concentration: M → ℝ≥0 is a function, which assigns a positive real number to each metabolite. Additionally, the concentration of a metabolite has to be less than or equal to the capacity of the metabolite, ∀m ∈ M: concentration(m) ≤ capacity(m).rate: R →{{h, j}, h, j, {}} is a function, which assigns a kinetic rate equation j ∈ J, whereby J is a set of all kinetic rate equations or a positive real number (stochastic rate) h ∈ ℝ≥0 or the empty set to each reaction.boundaries: R → , with lower, upper ∈ ℝ≥0, and lower ≤ upper is a function, which assigns an ordered pair of positive real numbers to each reaction, whereby the lower bound has to be smaller than or equal to the upper bound.objective function: R → {0, 1}, with ∀r, r′ ∈ R: objective function (r) = 1 ∧ objective function (r′) = 1 ⇒ r = r′, is a function, which assigns 0 or 1 to each reaction, whereby only one reaction receives the value 1 (for optimization).Furthermore, the following requirements must be fulfilled:r ∈ R applies: (1) there exists at least one incoming and one outgoing edge (whereby the incoming edge is not of type i) and (2) if one incoming or outgoing edge is reversible (irreversible) than all incoming and outgoing edges are reversible (irreversible). With this rule a reaction is either connected to reversible edges or irreversible edges but not a combination of them.For all reactions m ∈ M and a reaction r ∈ R there are at most two edges e, e′ ∈ E of different types. If two edges e and e′ connect m with r the type of e is ci and the type of e′ is i. This case describes a substrate inhibition at high substrate concentrations, whereby a metabolite is substrate and inhibitor at the same time.Between a metabolite e connects r with m and another edge e′ connects m with r the type of e is pi and the type of e′ is i. In this case, a product inhibition is modeled with a metabolite as product and at the same time inhibitor of a reaction.If one edge An explicit formulation of both cases for reversible reactions is not needed because the reaction mechanisms already provide implicit substrate- and product inhibition.unified graph into specific graphs for analysis. The edge set E is composed of three subsets, E = iE ∪ irE ∪ rE. The subset of inhibitory edges is iE = {e ∈ E|type(e) = i}, the subset of irreversible edges is irE = {e ∈ E|type(e) = ci ∨ type(e) = pi} and the subset of reversible edges is rE = {e ∈ E|type(e) = cr ∨ type(e) = pr}. The set of metabolites M consists of a subset of metabolites cpM, which are either consumed or produced in reactions cpM = {m ∈ M|∃r ∈ R:  ∈ rE ∨  ∈ irE} ∪ {m′ ∈ M|∃r ∈ R: ∈ Er ∨ ∈ Eir}.Moreover, the following sets are defined to simplify the transformation of the unified graph, data assignment functions that integrate calculated structural and dynamic data are used (this will be described in detail in the Transformation section).To assign analysis results to nodes and edges of the unified graph with a rich attribute set qualitative and quantitative metabolic models can be represented and additionally visualized using standards. Figure unified graph and the corresponding visualization in SBGN-PD.Due to the definition of the 2.2unified graph the Systems Biology Graphical Notation  is utilized. SBGN has been developed to interpret biological models easily without the need for extensive descriptions using three sub-languages. SBGN-PD  are visualized using simple chemicals ∈ entity pool nodes (process ∈ process nodes). All elements of the edge set e ∈ E are visualized using arcs of the set connecting arcs based on the assigned type. Edges of type ci are visualized using consumption arc, pi using production arc, cr using production arc in the opposite direction, pr using production arc and i using inhibition arc, respectively.The translation of the stoichiometry is visualized using cardinality and the metabolite attribute localization is visualized using compartment, which is a container for metabolites defined for this location. The localization of reactions is independent of a compartment, hence, a reaction could be located within, outside or on top of the border of a compartment. Import or export reactions in SBGN-PD are defined using the additional symbol source and sink ∈ entity pool nodes, see Figure The edge attribute Furthermore, interaction techniques allow the exploration and subsequent visual analysis leading to a broader understanding of the behavior and functionality of the underlying biological system (which will be described in detail in the Results and Discussion section).2.3unified graph (GUnified) into the specific graphs  have to be performed as a prerequisite to analyze a metabolic model using different modeling formalisms. The different models, modeling formalisms and the transformation from GUnified into GStoichiometric are described in the following. The transformations from GUnified into GKinetic, GPetri net, and into both of the topological graphs GMetabolite, GReaction are defined in the Supplementary Material.Overall, five transformations from the 2.3.1kinetic graph (GKinetic), which is transformed from the unified graph (GUnified), see Figure A kinetic metabolic model (ODE model) consists of a structural description of relations between metabolites and reactions and is extended with detailed kinetic data including rate equations, metabolite concentrations, and additional kinetic parameters. The kinetic model is represented by the kinetic graph is converted in ODEs, which are numerically solved  and extended quantitative stochastic Petri nets (eSPNs). The extension includes continuous tokens (to model metabolite concentrations), continuous arc weights (to model non-integer stoichiometry), continuous place capacities (to model limited resources), and inhibitor arcs (to model inhibition). An inhibition is modeled using an inhibitor arc from a place to a transition meaning that the transition can only fire if no token is on that place. The transition may only fire when the place is empty.A Petri net metabolic model can be defined using different Petri net types. Here, we refer to extended qualitative place/transition Petri nets  assigned to transitions. For further details on Petri nets for modeling metabolic models the reader is referred to Baldan et al. (Petri net graph (GPetri net), which is transformed from the unified graph (GUnified), see Figure Both Petri net types share the same structure, but n et al. . The PetPetri net graph is converted into a linear equation system, which can be solved to derive invariants describing main pathways (T-invariants) or metabolite conservation (P-invariants) of a metabolic model [more details in Murata . The unified graph GUnified is transformed in a directed, attributed, bipartite stoichiometric graph GStoichiometric =  with a metabolite set SM = cpM, which is a subset of the set M in GUnified. Metabolites with only inhibitory interactions to reactions are not considered. The reaction set in GStoichiometricSR = R equals the reaction set R set in UnifiedG and the edge set in GStoichiometricSE = irE ∪ rE is a subset of the set E in UnifiedG. Edges of type i are excluded. The attribute set in GStoichiometricSA ⊆ A is a subset of the set A in GUnified with SA = {type, stoichiometry, localization, label, boundaries, objective function}.GUnified into GStoichiometric and thereby detailing the difference between both graphs. This transformation results in structural differences (no inhibitions) and a reduced attribute set. Thereby, all regulatory information and quantitative data are lost.Figure stoichiometric graph, a metabolic model can be validated utilizing the Dead-End analysis or Gap-Finding analysis revealing blocked reactions or dead-end metabolites. To examine the flow of metabolites through a metabolic model the stoichiometric graph is converted into a system of mass balance equations at steady-state, which are solved by minimizing or maximizing an objective function. This optimization can be conducted using a linear optimization instead of a non-linear optimization to handle the problem of alternate optimal solutions. Applicable optimization-based methods are FBA, flux variability analysis (FVA), robustness analysis (RA), and knockout-analyses (KA) resulting in a flux distribution, minimal and maximal fluxes, sensitivity curves, and sensitivity values, respectively. For a detailed description of optimization-based methods the reader is referred to  and reaction graph  are predominantly used for topological analysis . This transformation results in structural differences (unipartite graphs) and a reduced attribute set. Thereby, all regulatory information and quantitative data are lost.Metabolic models are analyzed according to topological properties in order to understand the importance of key elements, structure, and robustness against disturbances. Since the metabolite graph or reaction graph is conducted using the corresponding adjacency matrix. A shortest path analysis results in paths (subgraphs which could be the graph itself). Furthermore, centrality analysis with different centrality measures leads to a ranking of graph elements according to different importance concepts. For further details on different centrality measures the reader is referred to Koschützki and Schreiber  are assigned to elements of the unified graph  using the assignment function zn: M, R, E → NV, whereby the vector could comprise numeric values , pairs of numeric values , and a set of time, step, and flux value dependent numeric values .Numeric values of the vector  of independent modeling formalisms as explained in the Introduction section and leads to major (+) or minor (±) improvements of an integrated analysis as already depicted in Figure In detail, using the integrated approach it is not required to define detailed kinetics to derive quantitative predictions and reveal dynamic behavior of the underlying biological system. Instead, using some parameters the Petri net simulation or stoichiometric modeling method FBA could be performed to approximate kinetic simulations. Thus, larger models are applicable in the integrated approach leading to analysis results, which could be again integrated to analyze the model further. Additionally, qualitative analysis can be conducted for extended Petri nets using another integrated formalism such as Dead-End analysis or centrality analysis. Quantitative predictions can be revealed for a qualitative model with a static description using stoichiometric analysis.Dead-End metabolites. Additionally, Petri net T-invariants correspond to flux modes, which could be directly calculated using the stoichiometric analysis method elementary flux modes (not presented here).Hence, different modeling formalisms complement each other even through, overlaps between the introduced metabolic modeling formalisms exist. For example, the stoichiometric matrix used in the stoichiometric modeling formalism to derive mass balance equations corresponds to the incidence matrix of the Petri net formalism used to derive an equation system solved for, e.g., invariant analysis. In the case of structural analysis, both the stoichiometric and the Petri net formalism could be utilized to reveal, for example, VANTED system . It utilizes and extends VANTEDs functionality for the interpretation of experimental data and for analyzing metabolic models with different modeling formalisms.The described method is implemented as an Add-on for the Solanum tuberosum) an integrative analysis is performed using the described method. Due to its main component, starch in the potato tuber, potato is of great importance as food and in industry, for example, for the production of fuel. Therefore, a major aim of plant breeding is to improve the distribution of biomass within the plant in favor of harvestable plant parts. Based on the homogeneous tissue of the potato tuber the main flux of metabolites is from sucrose to starch  is converted into hexose phosphates  utilized in glycolysis (Glyc) and as precursors for starch synthase (StaSy). The pathways Glyc, starch biosynthesis, and energy consumption (ATPcons) are modeled as summarized reactions. This is a necessary simplification to avoid unknown transport processes into additional compartments. To describe the environment the model is extended through sucrose import (Imp) and starch export reactions (Exp).Using a kinetic model representing the sucrose breakdown in the developing potato tuber Junker,  the inteInv) as described in Junker . The results indicate the production of starch and the utilization of reactions with different probabilities.Glyc and starch biosynthesis.Additionally, the invariant analysis reveals beside 3 P-invariants (reflecting substance conservation) 19 T-invariants, which can be grouped in trivial and non-trivial T-invariants. Each of the seven trivial T-invariants corresponds to a reversible reaction. The non-trivial T-invariants can be differentiated in a group of nine representing the cleavage of sucrose by invertase and another group of three where the sucrose is cleaved by sucrose synthase. These T-invariants reflect the main processes that are pathways taking place in the metabolic model in reality  to constrain the fluxes for these reactions, results in a flux distribution, which is comparable to the kinetic analysis results. In Figure AdK is not utilized as can be seen in results of the kinetic and Petri net analysis.The stoichiometric analysis (irrespective regulatory processes), using only three steady-state reaction rates  centrality analysis is conducted. As a result, the table in Figure Suc and G6P, which are selected to be highlighted in Figures Glyc could not be processed.Using the In summary, using the integrative analysis allows different modeling formalisms to be investigated in one workflow. An integrated and interactive visualization of the analysis results leads to an advantage over the use of each modeling formalism independently. This helps to compare analysis results from different formalisms within one metabolic model and allows for the investigation of analysis results from one formalism in another, as mentioned in the use case.4unified graph, enabling graph transformations, and a visualization in a standardized and formalized way. The unified graph supports user interaction and thereby allows different analysis results to be explored in the context of the metabolic model. The application reveals structural and dynamic properties of the crop plant potato utilizing the integrative analysis. The method has been implemented as an extension of the VANTED system and could also be applied to other model types, but we have focused here on metabolic models as an application area.We described a method, which is able to bring together different metabolic modeling formalisms. The integration is realized by a unified graph, adding of appropriate modeling formalisms, and corresponding transformations. Furthermore, the visualization has to be adapted to represent different types of models in SBGN using, for example, the sub-language SBGN-AF for gene regulatory models.Combining different modeling formalisms opens many possibilities for future research. Additional analysis algorithms can be added to study metabolic models in more detail. We plan to extend the method for different types of models such as gene regulatory models to investigate further cellular processes. This extension requires the adaptation of the SBM2 – Framework software. Falk Schreiber supervised the project and gave conceptual advice. Both authors wrote the manuscript.Anja Hartmann developed the theoretical framework, the use case, and implemented the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.http://www.frontiersin.org/Journal/10.3389/fbioe.2014.00091/abstractThe Supplementary Material for this article can be found online at Click here for additional data file.


Chlamydomonas reinhardtii have elucidated some novel metabolically important genes and this enhances the lipid production in this microalgae. Adding to it, various other aspects in metabolic engineering using OptFlux and effectual bioprocess design also gives an interactive snapshot of enhancing lipid production which ultimately improvises the oil yield. This article reviews the current status of microalgal based technologies for biofuel production, bioreactor process design, flux analysis and it also provides various strategies to increase lipids accumulation via metabolic engineering.The current scenario in renewable energy is focused on development of alternate and sustainable energy sources, amongst which microalgae stands as one of the promising feedstock for biofuel production. It is well known that microalgae generate much larger amounts of biofuels in a shorter time than other sources based on plant seeds. However, the greatest challenge in a transition to algae-based biofuel production is the various other complications involved in microalgal cultivation, its harvesting, concentration, drying and lipid extraction. Several green microalgae accumulate lipids, especially triacylglycerols (TAGs), which are main precursors in the production of lipid. The various aspects on metabolic pathway analysis of an oleaginous microalgae i.e.,  Identification of genes involved in enzyme and integrating with complex metabolism is really difficult without base/model system. This difficulty has led to development of The commercially viable biofuel recovery from microalgae is not realistic due to the (i) little biomass recovery (ii) cost of downstream processing and validation. Furthermore, the viability of microalgae based biofuel can be achieved only by the (i) designing advanced photobioreactors (ii) developing low cost technologies for biomass harvesting, drying, and oil extraction (iii) development of biorefinery approach.The overall production of such biofuel from microalgae can be enhanced by the genetic engineering approaches and adopting metabolic pathways engineering for augmented lipid production. Besides above techniques for improvement in the possibility to harnessing microalgae for biofuel a new emerging technologies i.e., biotic or algal-bacterial interactions for enhancement of microalgae growth and lipid production are also explored  and Acetyl CoA Carboxylase (ACCase) are found to be linked with fatty acid synthesis pathway. These enzymes are present in chloroplast and ACCase is a rate limiting pathway for fatty acid biosynthesis. ACCase, carboxylate Acetyl CoA to form malonyl CoA and FAS elongate the fatty acid chain by two units  and then finally to triacylglycerol (TAG).In this pathway acyl group is transferred from phospholipids by phospholipid:diacylglycerol acyltransferase  production is not restricted to specialized cells but in microalgae it can be triggered by stress. (a) specific lipid like betaine lipid which was not reported in plants  and mevalonate (MVA) pathway Engineering different enzymes toward lipid biogenesisIdentifying rate limiting enzymes/committed stepCarbon partitioning/captureMathematical modelingOver expression of a gene/multiple geneTranscription factor engineeringMetabolic engineering strategy is defined as tuning of metabolic pathways in a cell to trigger the target metabolite production. Achieving such targets various strategies can be adopted which are listed below:Chlamydomonas reinhardtii . Whole genome sequencing of model as well non model microalgae is required in order to reconstruct the metabolism. Reconstruction of metabolic fluxes using stoichiometric model i.e., S.v = 0; Where v is a vector of fluxes and S is matrix, and matrix is constructed by balancing the masses in each of the cell compartment of Pseudomonas fluorescens  formation. In this facet the major disadvantage are slow growth rate and low photosynthetic activity. Since lipid productivity is directly proportional to cell number therefore two stage cultivation approach may be employed to circumvent the above stated problem but photosynthetic one still remains. Metabolic flux analysis using GC-MS and LC-MS/MS under photoautotrophic growth in Brassica napus from Arabidopsis in order to increase the fatty acid content  as a promoter will also increase TAG accumulation under phosphorus starvation , genome editing through modern tools have been described and is established in Advantages of microalgae for biofuel application over the other fuel crops have been thoroughly reviewed , so more focus has to be put on genome sequencing, and rigorous genome scale metabolic flux based analysis is required toward TAG accumulation in different lineages of microalgae. In our opinion, first we should target the key genes responsible for lipid biosynthesis. As we had genome sequenced for plant as well for algae the methods that are belong employed in plant can easily be replicated in microalgae also.The metabolic engineering of microalgae is an significant area of research due to enormous interest on generating efficient biofuel. As algae has shown the highest divergence (67.7% distinct) so these findings regarding genes divergence will through a light toward a positive hope for lipid bio-genesis. A survey of literature above has established the idea that research has been carried out only in model microalgae (Certainly, the various type of stress responses on microalgae is a key tool for increasing the lipid droplets. Moreover, the accessibility of nutrients during stress condition will have a definite role in lipid productivity. It is well documented now that during stress conditions the microalgal metabolism will shift toward the storage of energy rich essential molecules in form of triacylglycerol are which could be efficient source of lipid. But definitely, writing these aspects in research article and to get it done practically is not impossible but it's tough in case of complex lipid biogenesis pathway.All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.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.


Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curated reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. The model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom  Diatoms are unicellular photosynthetic eukaryotes ubiquitous in marine and freshwater habitats and are responsible for about 20% of the photosynthetic carbon fixation on Earth . DiatomsPhaeodactylum tricornutum is an emerging model diatom because of its relatively small genome (27.4 megabases) [P. tricornutum may be the most advanced in microalgae, with the recently developed ability to assemble whole chromosomes in yeast [The marine diatom in yeast , knock-oin yeast ,10, and in yeast . Previouin yeast  and genein yeast . The devP. tricornutum is the metabolic network reconstruction, which enables computational analysis of systems-level responses. Genome-scale metabolic network reconstructions are derived from the annotated genome and contain information about all known metabolic reactions in an organism including the stoichiometry, subcellular localization, and the gene products by which they are catalyzed. The reconstruction process itself is laborious and iteratively and described, for example, in detail in [One promising strategy that investigates the yet unexplored metabolic capabilities of distinct organisms such as etail in . The recetail in ,16.P. tricornutum which provides a comprehensive insight into yet unexplored metabolic capabilities. We constrained the model with organism-specific biomass equations generated by Fourier transform infrared spectroscopy. The model predicts the presence of a surprising chloroplast glutamine-ornithine shunt that transfers reducing equivalents generated by photosynthesis to the mitochondria. Our findings demonstrate the utility of whole genome metabolic reconstructions to uncover unexpected biochemistries and to provide an important in silico template for directing future metabolic engineering efforts.Here, we report the reconstruction of a detailed and compartmentalized genome-scale metabolic model for Phaeodactylum tricornutum was obtained from JGI (http://genome.jgi-psf.org/Phatr2/Phatr2.home.html). We used the “finished chromosomes” (Phatr2) and “unmapped sequence” (Phatr2_bd) protein sequences to generate the draft reconstruction. While working on the reconstruction, an updated genome annotation, Phatr3, with refined gene models and improved functional annotation became available and exploited as well. Using a Phatr2 to Phatr3 gene ID mapping table provided by the JCVI, the Phatr2 gene IDs in the reconstruction were replaced by their corresponding Phatr3 IDs. Phatr3 is available at Ensembl Protistis (http://protists.ensembl.org/Phaeodactylum_tricornutum/Info/Index).The genome annotation of P. tricornutum genome annotation contains many putative enzymes with unknown function. To facilitate the manual curation of our draft reconstruction we used protein BLAST (with default settings) to reannotate the predicted proteins. Using the BLAST command line tool we created a local BLAST database containing all reviewed UniProtKB/SwissProt sequences having protein evidence at the protein or transcript level [P. tricornutum proteins and the created UniProt BLAST database.The pt level . Using aTo predict a subcellular localization for each protein we used a refined version of a previously developed pipeline. We used the updated Phatr3 protein sequences as input for TMHMM 2.0 , MitoproWe extended the pipeline by i) removing nuclear targeted proteins using predictNLS , ii) scret al. [P. tricornutum biochemical composition  as done by Mayers et al. [Based on the experimental approach of Mayers et al.  and [26–et al. , we detes et al. .By conducting these measurements over a growth curve including samples from nitrogen replete during exponential growth phase to nitrogen starved during stationary phase, we were able to achieve large changes in the cellular contents for all of these cellular components in smooth gradients , and tw (iJN678  and Knoo (iJN678 ). Before Toolbox . We usedP. tricornutum genome annotation Phatr2 (Phatr3 was not yet available) and the reconciled reference networks we obtained a draft reconstruction based on homology using the RAVEN Toolbox [Chlamydomonas or Synecchocystis for which no homologs in P. tricornutum were found and verified whether these reactions are present in P. tricornutum or not, ii) merged expanded reactions, iii) removed compartments not relevant for P. tricornutum, e.g., the eyespot, iv) removed duplicated metabolites and reactions which were introduced due to incorrectly reconciled information, and v) edited annotations.Starting from the  Toolbox . Before We manually curated the draft reconstruction pathway-by-pathway and verified the given information and added any missing information using the COBRA Toolbox . BesidesP. tricornutum reconstruction, the involved metabolites were characterized according to their chemical formula and charge determined at a pH of 7.3 using MarvinSketch . The pH was presumed to be constant across all compartments due to missing information for P. tricornutum. All reactions were elementally and charge balanced. Reaction reversibility was chosen based on published reconstructions such as iRC1080 or according to databases such as BIGG [™ .For each reaction in the  as BIGG  or SimPhProtein subcellular localization was assigned based on the prediction pipeline and indirect physiological evidence. If available, protein localization data from experiments with transgenic diatoms expressing protein-fluorescent protein fusions was exploited. Gene-reaction associations were identified from the literature, genome annotation, or genome sequence using BLAST and formulated as Boolean logic statements. Based on the biological evidence found we assigned a confidence score to each reaction reflecting the available information and evidence for its inclusion . Here, tSince naming might be ambiguous, different identifiers were used to annotate the reactions and metabolites. Reactions were annotated with EC numbers and KEGG reaction identifiers, metabolites were annotated with KEGG compound, ChEBI, and InChI identifiers.Each reaction was associated with at least one subsystem similar to the subsystem naming convention used in the KEGG database . Exchang2 and photon uptake. To validate that NAD(P) production did not occur without nutrient uptake we introduced an artificial reaction NAD(P)H → NAD(P) + H and again closed CO2 and photon uptake. If we found ATP production in the absence of nutrients, we identified all reactions contributing to the flux and produced a metabolic map using Escher [Quality control was performed during the reconstruction process. We ensured that ATP could not be produced without inputs. This was tested according to established standards  by optimg Escher  in orderS (m x n) where m is the number of metabolites and n is the number of reactions. The entries in the stoichiometric matrix, sij, represent the stoichiometric coefficients for the participation of the ith metabolite in the jth reaction. A negative value indicates consumption of metabolite i in reaction j whereas sij > 0 represents production of metabolite i. Flux balance analysis  was useTo constrain the space of possible solutions, the biomass objective function accounting for the ratios of biomass components  and biomass precursors  as well as energetic requirements to produce 1 g of biomass, is optimized for.2 exchange reaction to the experimentally determined total organic carbon  solver in MATLAB  with the COBRA Toolbox .2 and NO3 uptake. The ATP maintenance reactions were set to a range of 0–1 mM based on experimental results indicating negligible maintenance requirements [Dark period culture measurements were taken after the cells completed division; evidenced by consistency in the cell counts between dark and light period samples. Therefore, we hypothesized all biomass increases during the light period resulted from assimilation of extracellular nutrients. Elemental analysis indicated the culture fixed 1.57 mM C and assimilated 0.535 mM N during the light phase on culture day 5 , and C > 0 represents the minimal fraction of total photosynthetically fixed electrons that have to be directed to the mitochondria.However, unlike the traditional biomass function where the stoichiometry is pre-determined, dynamic allocation of fixed carbon was possible through the implementation of demand reactions for a β-1,3-glucose molecule representing the diatom storage glycan, chrysolaminarin, and TAG(16:1Δ9/16:1Δ9/16:0), the most abundant storage TAG observed during nutrient replete growth in cornutum . Additiox values . To simuGenome-scale network reconstructions are biochemically, genetically and genomically structured knowledge-bases which provide a framework to analyze and predict genotype-phenotype relationships. The reconstruction process is divided into four main steps  and summP. tricornutum genome annotation and protein homology to template organisms having reconstructions [P. tricornutum, attractive candidates for the analysis of cellular processes at a systems level, as they add to the biochemical diversity of microbes in a biotechnology setting, thereby increasing available production systems. Second, the draft reconstruction was manually curated and refined using additional resources such as the genome annotation, subcellular localization predictions and external databases . DMSP levels are known to increase with light intensity or nitrogen starvation but its metabolism is not well understood in diatoms and while the biosynthetic pathway is currently unknown [First, we generated a draft reconstruction based on the ructions –41. Diatructions . Althougases see . Once thases see . Qualitaammonium ) and orgammonium ). Theref results . Histidi unknown , a sensiiLB1027_lipid, see P. tricornutum.Leveraging a genome-scale model in the exploration and contextualization of lipid metabolism requires an accurate representation of the metabolic pathways and intermediate metabolites. To this end, a lipid module was developed  was constructed. The reduced lipid metabolism subsystem accounts for 1,029 reactions compared to 3,325 reactions involved in lipid metabolism in iLB1027_lipid. The core model yields comparable flux distributions and is suitable, for example, if detailed data on the lipid composition under the simulated condition are missing.The curated genome-scale metabolic network for One challenging aspect of eukaryotic reconstructions is the subcellular localization prediction of proteins. Due to their endosymbiotic origin, photosynthetic heterokonts including diatoms possess chloroplasts that are surrounded by four membranes. This complex structure concurs with distinct plastid targeting signals in diatoms that restrict the use of available subcellular prediction tools for other eukaryotes. We enhanced a previously developed pipeline which combined different bioinformatics programs to predict the subcellular localization of proteins in diatoms  see Fig.et al.’s results and experimentally validated subcellular protein localizations taken from [To evaluate the accuracy of the improved pipeline, we compared our predictions to Sunaga ken from ,12,58–64in silico pipeline; original refers to the version as published by Sunaga et al. [The table compares predictions of protein localizations to experimental data. For all considered proteins, Phatr2 and Phatr3 IDs and the status of the gene model in Phatr3 are given. If the gene models were modified, the pipeline predictions for both gene models are given. We distinguish between two versions of the a et al.  and the In order to mathematically solve the genome-scale model using FBA, the observed cellular phenotype is manifested as a biological objective function . This obP. tricornutum, biomass changes in the light period is dominated by the generation of carbon storage compounds, while the dark period is dominated by the anabolic processes necessary for cell division [The biomass composition used in heterotrophic genome-scale models is typically fixed based on experimentally derived values at a given culture condition . Howeverdivision . There idivision .P. tricornutum’s biomass composition over a growth curve that resulted in nitrogen deprivation after the high accumulation of biomass  than the median of all models (6%). B. napus (bna572+) has a comparable ratio of genes in the reconstruction (996) to predicted genes in the genome (9873) but contains far fewer reactions (671). The only model with a higher ratio belongs to the well-studied model organism S. cerevisiae, though this model iTO977 also contains fewer total reactions.In order to assess musculus . The conP. tricornutum involve environmental stress, such as nitrogen or phosphorous limitation [Recently, there has been a focus on using diatoms for biotechnological applications such as biofuel production, because of their high rate of neutral lipid accumulation ,81. Maximitation . Howevermitation .2 uptake  accounted for approximately 30% of total electron flow at low irradiances and as low as 5% at high irradiances [in silico, 5% of electron flow through the cytochrome b6f complex (CBFC_u) was routed to elemental oxygen mimicking the electron drain to PTOX while 5% of the electron flow through photosystem I (PSI_u) was committed to a Mehler-like reaction. Combined, these accounted for the 10% of electron flow to water-water reactions. Independent PTOX and Mehler reactions in the model are blocked by default but the boundaries can be adjusted to fit experimental results that deviate from the 10% value. In accordance with Bailleul et al.’s findings, the model predicts the use of mitochondrial oxidative phosphorylation to balance ATP and NADPH ratios.A recent, in depth characterization of photosynthetic electron flux in tosystem . Resultsadiances . Fixing et al. suggest that this ratio is fixed over a range of low to moderate light intensities. To simulate the observed energetic coupling between the mitochondria and plastid, an inequality constraint was added to the model. This constraint forced a minimum amount of the photosystem flux to be routed to the mitochondrial electron transport chain. Upon adding energetic coupling, the model predicted AOX was a primary electron sink at high irradiances. Additionally, the energetic coupling affected accumulation of neutral lipid biomass. Storage of lipid biomass was inversely proportional to energetic coupling with TAG accumulation being abolished when at least 35% of photosynthetically fixed electrons were redirected to the mitochondria at super-saturating photon uptake  to vent excess reducing equivalents. Our results predicted that flow of reductant from the plastid to the mitochondria was dependent on the ATP needs of the cell; however the results of Bailleul n uptake . Since let al., our reconstruction uncovered a previously undescribed plastid ornithine biosynthetic pathway  and two via oxidation of NADPH by AGPR_h. Ornithine is then proposed to be shuttled from the plastid to the mitochondria. The activity of 1-pyrroline-5-carboxylate dehydrogenase  and glutamine dehydrogenase  produce NADH further suggesting that this novel ornithine-glutamate pathway coupling these two organelles is possible.The mechanism by which reducing equivalents are shuttled to the mitochondria during energetic coupling is still unknown. In addition to the malate shuttle as proposed by Bailleul  pathway  that mayStorage of metabolites such as glutamine and ornithine could serve a photoprotective role by sequestering reducing equivalents as well as assimilated nitrogen. Indeed when intermediates of this ornithine shuttle were allowed to accumulate during simulations, the model predicted they were preferred over TAG biosynthesis. Ornithine concentrations were previously investigated in the context of the diatom ornithine-urea cycle (OUC) . AlthougP. tricornutum and contains information such as reaction stoichiometry and associations between genes and reactions. We especially focused on lipid metabolism since diatoms are attractive candidates for industrial-scale lipid production [P. tricornutum for biotechnological applications.Our assembled reconstruction represents the current, comprehensive biochemical, genetic, and genomic knowledge about oduction ,84. The S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(ZIP)Click here for additional data file.S4 File(ZIP)Click here for additional data file.


High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype–phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted. Cardiovascular disease (CVD) includes acute and chronic conditions, such as stroke and coronary heart disease . CVD resA vascular surface that normally is thromboresistant, anti-inflammatory, vasodilatory, and antiproliferative can turn into a surface that is thrombogenic, proinflammatory, vasoconstricive, and stimulatory of smooth muscle cell proliferation. Often this change is reactive and transient restoring vascular homeostasis. However, in diseases such as atherosclerosis, hypertension, and diabetes mellitus (DM) such changes, known as endothelial dysfunction, may be prolonged and critical for disease progression. The extent of pathological metabolic perturbation is determined by an interaction of lifestyle factors, such as diet and exercise with underlying genetic factors –12. ConsMetabolic modeling offers insights into cellular metabolism . Below, Endothelial cell metabolism has been investigated in multiple contexts including angiogenesis, hypoxia, shear stress, glycemia, and response to perturbations with mediators of vascular health including thrombin, sphingosine-1-phosphate, and more –19. The via the transcription factor Kruppel-like factor 2  is an intracellular fatty acid chaperone protein that impacts the peroxisome proliferator-activated receptor transcription pathway . Circulavia the tricarboxylic acid (TCA) cycle. When CPT1A was knocked down in vitro, vessel sprouting was impaired due to low levels of deoxy ribonucleotides. CPT1A knockdown in mice produced impaired retinal vessel formation (Fatty acid oxidation (FAO) accounts for roughly 14% of ATP production in cultured EC . Carnitiormation .via the TCA cycle  encourages NOS uncoupling leading to endothelial dysfunction. ADMA levels, and the ratio of ADMA to arginine, have been connected to several aspects of CVD risk –80.Genetic variation in eNOS affects some measures of recovery of blood flow control in acute myocardial infarction . Inhibitl-lysine, l-homoarginine, l-glutamine, l-leucine, or l-serine – decreases NO and increases endothelium-dependent vascular resistance. This effect is reversible by adding arginine to the medium and was shown to be dependent on y+L and y+ family amino acid transporters . While reconstructions afford a mechanistic description of genotype–phenotype relationships, they are not context specific. However, when constrained with cell or context-specific data, for example gene expression information of ECs, reconstructions afford GEMs that are descriptive of the biological event and cell of interest. Gene expression data of a HUVEC cells at normoxia vs. hypoxia would for instance generate two GEMs based on the same reconstruction thereby providing two snapshots descriptive of metabolic flux through reactions as defined by the two expression datasets. Essentially, reconstructions define the biochemical components of an organism, while context-specific polyomic data are required to generate a GEM of a particular cell or cellular event. Genomic, proteomic, and/or metabolomic fingerprints can thus be analyzed and compared within the context of GEMs .in silico and affords realistic hypotheses of biochemical mechanisms  from ECs could be hypothesized and related to alterations in core energy metabolism induced by global metabolic expression profiles. The release of sphingosine-1-P from ECs and its contribution to individual vascular health could thus be proposed on biochemical alterations on the systems level as opposed to mutations in sphingosine kinase alone.Computational modeling can contribute to decisions regarding the suitability of a treatment for individual patients. GEMs could be produced for individuals based on genomics and subsequently used to stratify patients and personalize medical interventions for CVD. GEMs maybe based on generalized transcriptomic data from a pool of samples from a cell type  or a setIndividualized hepatocellular carcinoma models have been used to predict patient outcomes based on the predicted production of acetate, identified as a key metabolic pathway for survival . Twenty-Developing personalized CVD therapeutic interventions relies on the ability to account for genotypic and phenotypic variation. Variability in disease phenotypes can be captured and understood in the context of GEM’s to facilitate this process.Genome-scale metabolic models provide an integrated approach in studying EC metabolism. They allow analysis of the multiple factors affecting ECs in the body, facilitating the exploration of the relationship of genotype to metabolic phenotype. This offers the possibility of producing personalized predictions of CVD risk and treatment, that account for both genetic and lifestyle factors. Currently, GEMs are the only biochemical model type that can account for both of these factors within a predictive modeling framework .2+ signaling (Genome-scale metabolic models are only one type of model used to account for EC function. Focused and mechanistic computational models of various aspects of vascular biology have also been made. These address some important biophysical parameters that are currently outside the scope of GEM modeling. This includes assessing the effects of shear stress on blood vessel reactivity and growth as well as the effects on blood cell/endothelium interactions of flow –122. Modignaling  along wiignaling  and mechignaling  signalinignaling –132.in silico querying of the effect of LDL deposition on global endothelial metabolism. Indeed, computational analysis of LDL metabolism has already proposed novel approaches to combat CVD (Integrating biophysical and signaling parameters with GEMs would generate a more complete understanding of the role of endothelial metabolism for CVD. In addition, these future GEMs would allow retrospective analysis of biophysical and genomic data that have been generated in the last few decades from population studies , 133, whmbat CVD –136.in silico in a data-driven manner, to optimize future clinical interventions.Realistic computational predictions of the effects of genetic and environmental perturbations on endothelial metabolism are possible and beneficial. There has been some exploration of CVD with GEMs and analysis of EC metabolism with GEMs; however, the full potential of this technique is only just beginning to be explored. Existing and future models will allow clinicians and researchers to investigate variable endothelial function SM and OR wrote the manuscript and conceived the ideology. HH, SP, and PJ conceived the ideology and contributed to writing of the manuscript.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.


Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model  are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host's nutrient-based mechanism for bacterial partner selection. Metagenomic studies of microbial communities yield enormous numbers of gene sequences that have a known enzymatic function, and thus have potential to contribute to community-level metabolic activities. Ecologically divergent microbial communities are presumed to differ in metabolic repertoire and function, but detecting such differences is challenging because the required analytical methodology is complex. Here, we present a novel Bayesian model suitable for this task. Our model, BiomeNet, does not assume that microbiome samples of a certain type are the same; rather, a sample is modeled as a unique mixture of complex metabolic systems referred to as “metabosystems”. The metabosystems are composed of mixtures of subnetworks, where subnetworks are mixtures of reactions related by function. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among IBD patients. We inferred that this metabosystem is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an important antioxidant, possibly contributing to gut inflammation associated with IBD. PLOS Computational Biology Methods article.This is a e.g., the Earth Microbiome Project e.g., the Human Microbiome Project Microorganisms comprise up to one-third of the Earths' biomass, and those living on a human can outnumber their cells by a factor of ten High-throughput shotgun sequencing yields enormous numbers of environmental DNA sequences, some being homologous to genes known to encode an enzyme and thus assignable to a metabolic function. Development of dedicated analytical methods for these data lags behind the capacity to generate it. The more popular strategies, although yielding promising results, highlight the analytical challenges. First, functional differences among microbiomes often stem from differing abundances of shared metabolic functions rather than presence-absence polymorphisms e.g., sets of reactions related by function). In order to discover metabosystems and subnetworks, and group microbial communities according to differential usage of the metabosystems and subnetworks, we propose BiomeNet, a novel mixed-membership statistical model for metabolic network data. The model takes the reaction abundance data for each metagenome sample as its input. Reactions and their abundances are derived from the abundance of enzyme-encoding gene sequences found within shotgun metagenomic datasets. Suitable datasets are now readily available from public resources such as MGRAST e.g., a subnetwork might be comprised of parts of different KEGG pathways), (iv) differential usage of subnetworks is modelled through metabosystem composition, and (v) samples with the same typological labels  need not be identical; rather, the full community metabolism of a sample can exist as a mixture of different metabosystems.Here, we introduce a Bayesian modelling approach for inferring differential usage of metabolic networks among microbial communities. We approach the problem by considering metabolic structure and function as arising from overlapping metabolic phenotypes referred to as “metabosystems”. A metabosystem is further characterized by a mixture of overlapping metabolic “subnetworks”  and its variants. BiomeNet takes advantage of dependencies between reactions encoded by metabolic networks without any data transformation and reduction. Moreover, BiomeNet is specifically designed to provide explanatory capabilities. Differentiating metabosystems discovered by BiomeNet can be explained by their differential usage of metabolic subnetworks, which in turn are interpretable as a small set of strongly connected metabolic reactions. Because interpreting principal components discovered by PCA is not trivial We validate our inference algorithm via simulation and apply it to datasets from two studies of functional divergence among gut microbiomes e.g., reactions) to contribute to other structures to different degrees . Aside from avoiding the need to place arbitrary boundaries on the components of a biochemical network, this framework reflects how community-wide metabolic activities of a microbiome arise from mixtures of organisms having different metabolic repertoires e.g., stool samples are comprised of mixtures of the epithelium and luminal niche communities BiomeNet is a hierarchical mixed-membership model, where metabolic reactions are mixed to form subnetworks, subnetworks are mixed to form metabosystems, and environmental samples are treated as potential mixtures of metabosystems. We explicitly model metabolism as a hierarchy because biochemical networks are widely considered to be organized in this way. For example, the KEGG pathways database arranges biochemical reactions into sub-pathways (consecutive reaction steps within curated pathways), which are arranged into “pathway modules” that are intended to represent functional units Within BiomeNet, each enzymatic reaction is decomposed into substrate-product pairs . DependeL subnetworks each contributing to different degrees. We model their composition with a mixture variable φ. For example, if we assume that we have 10 subnetworks (L = 10), we can look at metabosystem k and see it has mixing probabilities for each subnetwork kφ  =  . We can see from this example that metabosystem k is mainly comprised of 20% subnetwork 3, 10% subnetwork 6 and 70% subnetwork 9.Metabosystems are modeled as a mixture of θ. For example, if we have 3 metabosystems (K = 3), and a microbiome sample has a metabosystem mixture of θ = , then θ indicates that this sample consists of 20% metabosystem 1 and 80% metabosystem 3.Finally, each microbiome sample is modeled as a mixture of metabosystems. These metabosystems can be thought of as different facets of community-level metabolic activities. Considering the case of gut microbiome samples from diseased and healthy individuals, it is unlikely that the community metabolism of all diseased individuals will be exactly the same. Individuals with a more severe case of the disease might have a larger contribution from a “dysbiotic metabosystem” to their microbiome. Thus, we do not assume that samples are necessarily comprised of just one metabosystem. We denote the metabosystem mixture for a sample as Our model is completely unsupervised; the membership of reactions to subnetworks, the contribution of subnetworks to metabosystems, and the mixture of metabosystems within a sample are learned from the data. Collapsed Gibbs sampling N microbiome samples in the data, and those data are comprised of a total of nI reactions in the nth microbiome sample, and a total of niJ substrate-product pairs in the ith reaction of the nth sample. Because we expect that a microbiome sample could be a mixture of partially overlapping assemblages of microbes with varying types of ecological interaction, we model each sample as a mixture of K metabosystems, where K is assumed to be known and fixed in advance. The relative contribution of each metabosystem to the microbiome associated with the thn sample is modeled through latent variable nθ, a probability vector of K values summing to one. Thus, we haveSuppose we have a total of niZ denotes the metabosystem assignment for the ith reaction in the nth sample. The θ variables will be inferred from the data. We assume an independent and identical (iid) sparse symmetric Dirichlet prior on nθ.θα is a positive parameter. The mean of K, for i = 1,…, K, and the variance of θα is closer to 0, in which case, the probability that a randomly drawn point from this distribution is a sparse vector is larger. The sparsity introduced by a prior can be viewed as equivalent to the penalty added to the likelihood in the penalized likelihood method L) of metabolic subnetworks. Therefore, metabosystems differ with respect to their mixture probabilities of the different subnetworks. The contribution of each subnetwork to the thk metabosystem, kφ, is modeled by a vector of L mixing probabilities that sum to one. For K metabosystems, there will be K probability vectors of L mixing probabilities. Thus element klφ in K × L matrix φ represents the relative contribution of subnetwork l in metabosystem k. Let niY denote the subnetwork assignment for the ith reaction in the nth sample. Then we model niY as:Next, we assume each metabosystem is comprised of a fixed number  and product group (R). The compounds in the substrate group associated with a subnetwork are used as substrates in reactions that belong to that subnetwork. The product group associated with a subnetwork consists of products that are produced by at least one of the reactions in the subnetwork. Note that a compound can belong to both the substrate and the product group. Such a compound will be involved in the intermediary reactions of the subnetwork. In general, membership of a compound to a substrate or product group is considered to be a “soft”  membership.Reactions within a subnetwork are linked through their shared chemical compounds. We model subnetworks as a subset of compounds that are converted to another subset of compounds. It is reasonable to assume that the substrate and product sets cannot be arbitrary sets of compounds. Therefore, we assume that compounds are grouped together and make up substrate groups and product groups. Each subnetwork has its own substrate group  and subnetwork (Y) assignments for each reaction conditional on the assignments of all other reactions.The complete likelihood of the data given the hyper-parameters of the prior distributions is:-niZ and -niY denote the metabosystem and subnetwork assignment for all reactions in all samples except only reaction i in sample n. The individual terms in the above equation can be analytically derived  within the model. Thus, each microbiome sample is interpreted as a mixture of such structures. In unsupervised applications such as those presented in this paper, trying several different K values to find the value that shows separation of classes is often considered a natural way to choose K. Although this tactic requires some degree of heuristic judgment, typically it is difficult to avoid at least some heuristics in the context of unsupervised learning methods The purpose of BiomeNet is to learn the structure of the data. It is an unsupervised learning method. As with most other unsupervised learning methods, the validity of the inference is mainly judged through the scientific background of the problem and the interpretability of the results L as long as L is not too small. If L is larger than needed, there will be redundant subnetworks in BiomeNet, but they will carry very little weight for any of the metabosystems, and their reactions will have only a trivial impact on the reaction composition of each metabosystem. Alternatively, the informative subnetworks should make consistent contributions to each metabosystem, and their reactions should be highly influential on the reaction composition of each metabosystem. Thus, BiomeNet should be able to infer a consistent reaction profile for each of the K metabosystems, as long as the L value is not too small. This can be visually verified by using a heat map that portrays differences in reaction composition among metabosystems. The details for computing the reaction composition of a metabosystem, and for comparing the composition of different metabosystems are given in L value, the approach can be used to identify a minimum value for L.Ideally, inference under BiomeNet should be largely robust to the choice of K according to biological criteria. This approach avoids the computational burden associated with assessing many values of both K and L. However, we can use the same heat map described above to check for divergence among the K metabosystems. If there is no signal for divergent metabosystems , differences between metabosystems will be similar to those observed for the same metabosystem over different values of L. If at least some of the metabosystems are divergent, differences in reaction composition will be large as compared to those within the same metabosystem. In this way, we can assess the chosen value of K. Further details are given in In our analyses of real data, we chose a value of i.e., θ, αφ, αδα, and γα). The values of θα and φα control the extent by which subnetworks and metabosystems are mixed, while δα and γα control the size of the subnetworks. If very small values are chosen for δα and γα, then a relatively larger value of L should be chosen. Because we want (i) only a few subnetworks to contribute significantly to each metabosystem, and (ii) each subnetwork to consist of relatively few reactions, we chose a value for the concentration parameters close to zero (0.01). Users can control the extent of mixing for their data by resetting the concentration parameter values.The hyper-parameters of our model are the concentration parameters of the symmetric Dirichlet distributions . We compared the estimated mixture weights for metabosystems in each sample with the corresponding weights used for simulation. For each simulation, the inference algorithm was able to recover mixture weights that were very close to the actual metabosystem and subnetwork composition despite fixing value of the concentration parameters at 0.01 . Althougi) verify that observed differences in gut communities of carnivores and herbivores indeed reflects differences in community metabolic function, and (ii) gain insights into the differences in metabolic functions of those communities.Carnivorous, omnivorous and herbivorous mammals are well known for diverse digestive physiologies. Metagenome sequencing of fecal samples from 33 mammal species revealed that 16S communities and metagenomes within the mammalian gut differ according to dietary niche of the host http://sourceforge.net/projects/biomenet/. This dataset yielded 2,824 unique reactions between 2713 compounds. Reaction abundances for samples, as input into BiomeNet, range from 12,626 to 174,103 (see K = 3). The number of metabosystems was initially chosen to match the number of categories for dietary niche . However, because the model does not assign a diet status to a particular metabosystem, they are simply designated as metabosystems 1, 2 and 3, and the uniqueness of their reaction composition was assessed for these data.We obtained metagenomic data for 38 samples of mammals L = 100 subnetworks, we obtained an estimate of the contribution of different metabosystems to each gut community sample. As seen in the simplex plot within 1θ<15%). Interestingly, omnivores did not form a separate cluster; some omnivore communities (black dots) had mixtures more similar to carnivores and others more similar to herbivores (a priori (and possibly incorrect) assignment of one metabosystem as unique to omnivores.Using BiomeNet with rbivores . This hii.e., L = 50, 100, 150 & 200). Even though the labeling of a metabosystem is arbitrary, they can be distinguished if they have a characteristic composition of reactions. For these data, the metabosystems identified by BiomeNet had different reaction compositions that were largely robust to L. This is illustrated by the heat map in L = 50, JSD is consistently lower in the diagonal matrices as compared to L>50. Also when L = 50, some comparisons between metabosystems 2 and 3 have JSD scores similar to those observed for comparison within the same metabosystem. Taken together, the results indicate that these metabosystems have characteristic reaction compositions, with stable mixture weights in each sample when K = 3 and L≥100. Consequently, it was easy to coordinate metabosystems across the different analyses . Recall that the model will not assign a health status to a particular metabosystem.We illustrate the value of our approach to human microbiomics by applying it to a sample of adults who are classified either as healthy or as having IBD. The dataset consists of K = 3. Again, the three metabosystems are not equally divergent; while metabosystems 2 and 3 are divergent from each other, they are even more divergent from metabosystem 1. We also evaluated a model having K = 2 metabosystems, and found that no separation of samples was possible. Because each metabosystem has a characteristic reaction composition, it was easy to coordinate metabosystems across the different analyses when K = 3 . We found that 15 of the 22 principal subnetworks (mixture weight>2/L) in these data make a large contribution to the divergence among metabosystems. Three of those 15 subnetworks  are diagnostic of metabosystem 2 due to their very large relative contribution to that metabosystem  within subnetworks 38, 64 and 73. Subnetworks 38, 64 and 73 were comprised of 29, 19 and 18 principal reactions, respectively. Within each subnetwork, the principal reactions had a cumulative probability density>0.99  are abundant in subnetwork 38, and hence metabosystem 2. Reactions responsible for transport of N-acetylglucosamine and fucose are potentially meaningful because they suggest that metabosystem 2 is more dependent on glycans derived from host-associated substances, like mucin and shed epithelial cells, as a source of energy. Although these transporters do not point directly at the cause of gut inflammation associated with IBD, it does suggest that establishment of community metabosystem 2 could explain the resistance of IBD to moderate dietary intervention because the endogenous glycans provide a persistent source of nutrients for the community. Interestingly, extreme intervention whereby an exclusive liquid diet is administered up to 12 weeks  can sometimes induce remission of IBD in pediatric cases Subnetwork 64 contains reactions associated with ascorbate metabolism. Two reactions that convert ascorbate to dehydro-gulonate-6P have high relative-abundance in IBD patients suggesting the potential for reduced ascorbate levels within the gut due to microbiome metabolic activity. Remarkably, direct measurement reveals reduced ascorbate levels in IBD patients Subnetwork 73 is enriched in genes involved in the modification and transport of sugars and the metabolism of benzoate , our analyses with the current implementation were informative. For example, we found that samples of IBD patients have a high prevalence of a community metabosystem that we hypothesize is resistant to moderate dietary intervention, is closely associated with the human gut epithelium, and reduces the availability of an important antioxidant. Our finding that reactions within the most discriminatory subnetworks, although being highly connected, do not map to contiguous subsets of the main KEGG pathways illustrates the importance of learning the structures of the networks from the data.A wide variety of problems in computational biology have been addressed by using probabilistic graphical models e.g., scaling counts, mapping reads to EC numbers, etc.). Decisions must be made during the processing of any metagenomic dataset, and these could impact downstream analysis of community level metabolic capacity. Clearly, further simulation-based research is needed to investigate the sensitivity of various analytical methods (including BiomeNet) to the upstream details of data collection and processing. Such a comprehensive investigation is beyond the scope of this study.To validate the BiomeNet inference algorithm, we simulated hierarchical network data over many different combinations for the number of compounds, reactions, subnetworks and metabosystems. Reliable estimates of the mixing probabilities under this design, even when there was a large discrepancy between the fixed value of the concentration parameter and the true value for the generating process, confirmed the reliability of the algorithm. We note, however, that there are other steps involved in the processing of real metagenomic samples that are not included in our generative process . Indeed, microorganisms within a microbiome sample are expected to show metabolic interactions that range from negligible to obligate. We followed Boon et al. e.g., via antibiotic treatment) and assessing the extent to which that system returns to the same pre-disturbance composition of subnetworks.We chose to use the terms “subnetwork” and “metabosystem”, rather than “pathway” and “community”, so as to clearly delineate our model-derived structures from those entities having more biology-centred definitions . Our modelling approach is therefore suited to investigating questions about how the level of an individual's health status might be determined by mixtures of different metabolic phenotypes.It is important to note that we are working with a microbiome's latent capacity to process metabolites, as inferred from its metagenome. Thus the structures referred to as “metabosystems” reflect a latent aspect of metabolic phenotype. We modelled each sample as a mixture of metabosystems because microbial communities are thought to be composed of partially overlapping assemblages of microbes with varying types of ecological interaction BiomeNet provides the capability to focus attention on the fundamental structure of a complex metabolic network, which is necessary if researchers want to improve their understating of microbial community function. We note that this will also be the case when seeking a better understanding of the taxonomic composition of community structures. Along these lines, Arumugam and coauthors BiomeNet is suitable to investigating the latent metabolic structure of any microbial community. As long as abundance data can be obtained for sequences with assigned metabolic function, the model can be applied to samples from any environment  and over any scale . BiomeNet can easily accommodate the abundance data generated from high-throughput sequencing of RNA transcripts of uncultivable microorganisms. Thus, it could be employed to investigate the temporal dynamics of community metabolic structure within serially-sampled metatranscriptomic data.Text S1Network generation under the model, and validation of the inference algorithm. An overview of the generative process under BiomeNet and a detailed description of how we used simulation to verify that our sampling algorithm can recover the parameter values used to generate reactions for simulated microbiome samples.(PDF)Click here for additional data file.Text S2Analytical solutions for the individual terms of the posterior distribution of the latent variables. For inference under BiomeNet we sample from the posterior distribution of latent variables given the data. We use collapsed Gibbs sampling by integrating out the latent variables θ, φ, δ, γ and sample from the posterior distributions of the metabosystem (Z) and subnetwork (Y) assignments for each reaction conditional on the assignments of all other reactions. This is a high dimensional distribution, and the analytical solutions for the individual terms are given in these notes.(PDF)Click here for additional data file.Text S3Characterizing the reaction composition of a metabosystem, and measuring compositional differences between metabosystems. A detailed description of the method for characterizing the composition of a metabosystem at the level of its metabolic reactions, the method for measuring compositional differences between metabosystems, and the method for assessing robustness of metabosystems to the L value. The criteria for classifying principal subnetworks and discriminatory subnetworks are also provided.(PDF)Click here for additional data file.Text S4PCoA analysis of mammalian microbiome samples. PCoA analysis of Enzyme Commission assignments and KEGG Orthology assignments for the mammalian dataset reveals a separation between carnivore and herbivore gut microbiomes.(PDF)Click here for additional data file.Text S5Source and processing of metagenomic data. The source of the two gut metagenome datasets and a description how those data were processed to obtain abundance values for substrate-product pairs that were input into the model.(PDF)Click here for additional data file.Text S6Robustness of metabosystem composition to number of subnetworks. Evaluation of alternative numbers of subnetworks (L) in the model reveals that the reaction composition of the metabosystems is relatively stable to the specified value of L.(PDF)Click here for additional data file.Text S7Subnetwork composition of mammalian metabosystems. A presentation of the inferred contribution of 100 subnetworks to three metabosystems in the model, and the identification of the most discriminatory subnetwork for metabosystem 1.(PDF)Click here for additional data file.Text S8Subnetwork composition of the human IBD/healthy metabosystems. A presentation of the inferred contribution of 100 subnetworks to three metabosystems in the model, and the identification of the most discriminatory subnetworks for the metabosystem that was most associated with IBD (metabosystem 2).(PDF)Click here for additional data file.Text S9Core subnetworks. Definition and identification of core subnetworks under the BiomeNet modeling framework.(PDF)Click here for additional data file.Table S1Composition of subnetwork 49 inferred from the mammalian dataset. The table gives the KEGG reaction numbers, substrates and products for the principal reactions in mammalian subnetwork 49. Because the model does not rigidly define subnetworks, each reaction in the dataset will have an estimated mixing probability. As the majority of reactions make only a trivial contribution to this subnetwork (nearly zero), we filtered out any reaction with a contribution less than 2/R, where R is the count of unique reactions summed over all the samples in a dataset. This resulted in a subset of 25 reactions having a posterior density>0.99.(PDF)Click here for additional data file.Table S2Composition of subnetwork 38 inferred from the human dataset. The table gives the KEGG reaction numbers, substrates and products for the principal reactions in human subnetwork 38. Because the model does not rigidly define subnetworks, each reaction in the dataset will have an estimated mixing probability. As the majority of reactions make only a trivial contribution to this subnetwork (nearly zero), we filtered out any reaction with a contribution less than 2/R, where R is the count of unique reactions summed over all the samples in a dataset. This resulted in a subset of 29 reactions having a posterior density>0.99.(PDF)Click here for additional data file.Table S3Composition of subnetwork 64 inferred from the human dataset. The table gives the KEGG reaction numbers, substrates and products for the principal reactions in human subnetwork 64. Because the model does not rigidly define subnetworks, each reaction in the dataset will have an estimated mixing probability. As the majority of reactions make only a trivial contribution to this subnetwork (nearly zero), we filtered out any reaction with a contribution less than 2/R, where R is the count of unique reactions summed over all the samples in a dataset. This resulted in a subset of 19 reactions having a posterior density>0.99.(PDF)Click here for additional data file.Table S4Composition of subnetwork 73 inferred from the human dataset. The table gives the KEGG reaction numbers, substrates and products for the principal reactions in human subnetwork 73. Because the model does not rigidly define subnetworks, each reaction in the dataset will have an estimated mixing probability. As the majority of reactions make only a trivial contribution to this subnetwork (nearly zero), we filtered out any reaction with a contribution less than 2/R, where R is the count of unique reactions summed over all the samples in a dataset. This resulted in a subset of 18 reactions having a posterior density>0.99.(PDF)Click here for additional data file.Data File S1Posterior mixing probabilities of the principal reactions in all subnetworks (L = 100). Posterior probabilities are given for both the mammalian and human datasets. Because many reactions will have trivially small mixing probabilities for many subnetworks (nearly zero), we filtered out any reaction with a contribution less than 2/R, where R is the count of unique reactions summed over all the samples within the given dataset. The data are presented in a spreadsheet document, with the mammalian dataset and the human IBD/healthy dataset provided within separate sheets. Each sheet has 100 rows, one for each subnetwork in the model. Each row provides the KEGG reaction ID and mixing probabilities for a particular subnetwork. The data in each row is sorted according to its mixing probabilities.(XLSX)Click here for additional data file.


Ambiguity in malignant transformation of glioma has made prognostic diagnosis very challenging. Tumor malignant transformation is closely correlated with specific alterations of the metabolic profile. Exploration of the underlying metabolic alterations in glioma cells of different malignant degree is therefore vital to develop metabolic biomarkers for prognosis monitoring.1H nuclear magnetic resonance (NMR)-based metabolic analysis on cell lines  developed from gliomas of different malignant grades (WHO II and WHO IV). Several methods were applied to analyze the 1H-NMR spectral data of polar extracts of cell lines and to identify characteristic metabolites, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), fuzzy c-means clustering (FCM) analysis and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). The expression analyses of glial fibrillary acidic protein (GFAP) and matrix metal proteinases (MMP-9) were used to assess malignant behaviors of cell lines. GeneGo pathway analysis was used to associate characteristic metabolites with malignant behavior protein markers GFAP and MMP-9.We conducted Stable and distinct metabolic profiles of the five cell lines were obtained. The metabolic profiles of the low malignancy grade group  were clearly distinguished from those of the high malignancy grade group . Seventeen characteristic metabolites were identified that could distinguish the metabolic profiles of the two groups, nine of which were mapped to processes related to GFAP and MMP-9. Furthermore, the results from both quantitative comparison and metabolic correlation analysis indicated that the significantly altered metabolites were primarily involved in perturbation of metabolic pathways of tricarboxylic acid (TCA) cycle anaplerotic flux, amino acid metabolism, anti-oxidant mechanism and choline metabolism, which could be correlated with the changes in the glioma cells’ malignant behaviors.Our results reveal the metabolic heterogeneity of glioma cell lines with different degrees of malignancy. The obtained metabolic profiles and characteristic metabolites are closely associated with the malignant features of glioma cells, which may lay the basis for both determining the molecular mechanisms underlying glioma malignant transformation and exploiting non-invasive biomarkers for prognosis monitoring.The online version of this article (doi:10.1186/1476-4598-13-197) contains supplementary material, which is available to authorized users. Gliomas, which are the most aggressive type of brain tumors, show high morbidity, a high recurrence rate, and high mortality. Survival from gliomas depends on the tumor type and grades of malignancy. Accordiet al. performed metabolic profiling of glioma tissues and their normal counterparts. t1 was 6.6 μs. Water suppression was achieved by irradiation of the water resonance during the recycle delay ( RD ) of 4 s and the mixing time (τm) of 120 ms. The spectral width was 10 kHz with an acquisition time per scan of 1.64 s, and 256 transients were collected into 32 K data points for each spectrum. The free induction decay (FID) was zero-filled to 64 K and an exponential line-broadening function of 0.3 Hz was applied to the FID before Fourier transformation. Both phase and baseline corrections were carefully performed. The 1H NMR spectra were referenced to the methyl group of TSP (δ 0.00).All NMR spectra were reduced to 2587 integrated regions with a width of 0.003 ppm (bin) corresponding to the region of δ 9.5-0.8 using the MestRova6.5 software . The region of δ 5.5-4.5 was removed to eliminate artifacts related to the residual water resonance. .Before multivariate statistical analysis, the integral values were mean-centered and pareto-scaled. To checFCM is a clustering method that allows one piece of data to belong to two or more clusters and is extensively used in pattern recognition, 47. Theik is the degree of membership of xi in the cluster k, xi is the i-th measured data, vk is the center data of the k cluster. We used the FCM clustering algorithm through the Fuzzy Logic Toolbox in MATLAB .Where m is any number greater than 1, uTwo criteria were used to identify the characteristic metabolites. One was the VIP score of the OPLS-DA model and the For relative quantification of characteristic metabolites, the relative integrals of metabolites were used for comparison between two groups. The average changes and standard error were calculated.Pearsons’s correlation coefficients of cell samples in two groups were further calculated to display the relationships between the relative integrals of spectral peaks in a certain biological profile, as described previously. A heatmThe network of the metabolites with significance correlations was displayed by MATLAB Bioinformatics toolbox. Significant positive correlations were shown in red, while significant negative correlations were in blue. In addition, GeneGo MetaCore was used to analyze the network that describes the interaction between the characteristic metabolites and GFAP/MMP-9.Additional file 1: Figure S1: The proliferation ability of five glioma cell lines. Differences in the 490 nm absorbance among five cell lines were compared by One-way ANOVA. Values represent the mean ± SD in triplicate. (TIFF 945 KB)Additional file 2: Figure S2: Network of the shortest directional paths leading to characteristic metabolites related to GFAP/ MMP-9 regulation. Small molecules appear as purple hexagons, reactions as grey boxes, enzymes as orange shapes, transcription factors as red stars, ligands and extracellular peptides as green shapes, and transporters as purple "X" shapes. Metabolites with available data points are tagged with blue circles. Arrows denote mechanisms of interaction, in which green signifies activation and red indicates inhibition. Grey interactions represent transport or consumption/production of metabolic intermediates. (TIFF 3 MB)