Saccharomyces cerevisiae glycolysis pathway. Metabolic fluxes were quantified by the COPASI tool using the kinetic models of Hynne and Teusink at varied concentrations of external glucose. The enzyme sequences were taken from the UniProtKB and the average amino acid (AA) properties were computed using the set of Georgiev's uncorrelated scales that satisfy the VARIMAX criterion and specific AA indices that show the highest correlations with those. Multiple linear regressions  were found between the values of metabolic fluxes and the selected sets of the average AA properties. The hydrophobicity, α-helicity, and net charge were pointed out as the most influential characteristics of the sequences. The results provide an evidence that metabolic fluxes of the yeast glycolysis pathway are closely related to certain physical-chemical properties of relevant enzymes and support the view on the interdependence of catalytic, binding, and structural AA residues to ensure the efficiency of biocatalysts and, hence, physiologically adequate metabolic processes.Metabolic fluxes are key parameters of metabolic pathways being closely related to the kinetic properties of enzymes, thereby could be dependent on. This study examines possible relationships between the metabolic fluxes and the physical-chemical/structural features of enzymes from the yeast  Saccharomyces cerevisiae glycolytic enzymes and the AAC of corresponding sequences has been found [The general concepts and methods of systems biology become increasingly important in modern microbiology research. In terms of “systems microbiology,” this approach allows to analyze and describe as a whole the molecular interactions that occur within a microbial cell or community –3. The cen found , as wellen found . At the en found .However, the possible solutions have been defined as rather difficult task, arising from the sequence metrics problems , 15. In  Saccharomyces cerevisiae glycolysis pathway and the physical-chemical/structural features of enzyme sequences.Therefore, following our previous line of research, the goal of the present study is to test the possible relationship (QSAM) between the flux distribution through the yeast Saccharomyces cerevisiae glycolysis pathway: low-affinity glucose transporter , hexokinase , glucose-6-phosphate isomerase , 6-phosphofructo-2-kinase , fructose-biphosphate aldolase , triose-phosphate isomerase , glyceraldehyde-3-phosphate dehydrogenase , 3-phosphoglycerate kinase , phosphoglycerate mutase , enolase , pyruvate kinase , pyruvate decarboxylase , and alcohol dehydrogenase , as well as the main branches that include glycogen synthase  and glycerol-3- phosphate dehydrogenase  together with enzymes involved into the turnover of ATP: plasma membrane ATPase 1 , adenylate kinase 1 .The data set consisted of the amino acid (AA) sequences, representing the enzymes/carriers for the core reactions of the yeasthttp://www.uniprot.org) database under accession numbers as indicated in the brackets above. The AA composition (frequencies of AA occurrence) of sequences (AAC) was computed using ExPASy/ProtParam (http://web.expasy.org/protparam/) tool.The protein AA sequences were taken from the UniProtKB ( Saccharomyces cerevisiae glycolysis pathway were estimated using the kinetic models of Hynne [http://www.ebi.ac.uk/biomodels/)—BIOMD0000000061 and BIOMD0000000064, respectively. Simulation experiments were performed for both models using the COmplex PAthway Simulator tool  at perturbed initial concentrations of external glucose .Metabolic fluxes within the yeastof Hynne  and Teusof Hynne  from thePave(i) is the average AA property for each sequence and P(j) is the property value for jth residue and the summation over N, the total number of residues in a protein. The transformation of sequences into fixed-size numerical feature vectors was performed in two steps. First, the average AA property for each sequence was estimated using the generalized numerical scores in respect of individual AA [http://dx.doi.org/10.1155/2014/817102. Second, the average AA property for each sequence was computed using the narrower sets of specific AA indices from generalized AA property groups with a highest correlation to the VARIMAX-derived scales [The enzyme AAC was converted into a feature-based numerical representation using the average AA property for each sequence and computed according to the standard formula  (see 1)1):(1)Paidual AA . Appropridual AA . This led scales . The relThe accession numbers (AAindex database), description, and designation of specific AA indices which were used for this purpose are represented in R-square value besides keeping the variance inflation factor (VIF) below the threshold value of 3.3 [R-square criterion of regression models. This process was repeated to obtain four-variable and larger models until no variables could increase the adjusted R-square value.The data representing metabolic fluxes, as the dependent variables and the sets of numerical property vectors for respective enzymes, as the independent variables  were processed by correlation analysis (parametric and nonparametric) using the Statgraphics Plus  and SPSS 11.0 for Windows  and subjected to the multiple linear regression analysis using the same software. Explanatory variables in the models were subsequently checked by stepwise forward selection procedures thus finding the significant one-variable models as well as significant two-variable models to arrange all the variables in groups of 2 at a time for each model. The best three-variable models were formed by adding another variable one by one from the remaining variables, and the variables that yielded the greatest increase in the adjusted e of 3.3  or, in eF-test for analysis of variance (ANOVA) was employed to evaluate the statistical significance of regression models and Student's t-test was used to check the significance of regression coefficients. The leave-one-out cross-validation (LOOCV) procedure was used to validate developed regression models [R-square values.In addition, the corrected Akaike's (AICc) information criterion  was usedn models . The linP values < 0.05 were considered to be statistically significant for parametric and nonparametric tests.The  Saccharomyces cerevisiae glycolysis pathway and the values of individual average amino acid (AA) properties for respective enzymes expressed according to VARIMAX scales [Statistical analysis of the data set  revealed a number of significant pair correlations  between X scales  and, theR-square values  caused a marked decrease in actual R-square levels, as well as an increase of AIC c, which indicates a certain decline in the quality of extended models , and the measure of linker propensity (group 5) [Thus, in the present case seven (model I) or eight (model II) variables proved to be adequate to provide statistically robust multiple linear regression models linking the values of metabolic fluxes predicted by different kinetic models , 21 withgroup 5) .R-square values of corresponding Ridge regression, as described above, led to statistically robust multiple linear regressions  of the metabolic fluxes estimated by kinetic models against those predicted by regression models Tables . The higs Tables  adequateR-square values (Tables P < 0.00001). Besides, the observed values of the variance inflation factor (VIF)   showed o) Tables  and 3 in Saccharomyces cerevisiae glycolysis pathway appear as closely related to the sequence-derived properties of implicated enzymes.The obtained results indicate that the metabolic fluxes determined by Hynne's and Teusink's full-scale models for the yeastThe relevant multivariate regression models Tables  and 3 shOn the other hand, such an approach meets the general lines for the multiscale nature and, consequently, the scale-space representation of real-world objects , which iDifferent sets of the predictor variables in the regression models Tables  and 3, aThis study in the most direct way continues our previous research , 12 therThe results thus provide further evidence that the enzyme operation and hence the metabolic fluxes are directly dependent on the general physical-chemical and structural properties of the full enzyme sequences. Therefore, it might be useful for structure/function studies to look more beyond the active centre composition of the enzyme but carefully assess the overall physical and chemical properties of biocatalysts. The more so that the modest success of creating artificial enzymes also points to currently unknown, probably crucial, parameters that could significantly affect enzyme catalysis . In part Saccharomyces cerevisiae kinetic model [It is clearly understood that the estimates of metabolic fluxes currently obtained by the kinetic models represent an approximation, albeit realistic enough, of their “true” values which could involve multiple regulatory mechanisms including gene expression and posttranslational modification , 25, 35.The obtained sequence-activity relationships in the form of quantitative models (QSAM) will allow to assess the contribution of individual AA properties to the overall compositional features of proteins and thus to specify potential targets within enzyme sequences in order to attain a purposeful modification of biocatalysts and, hence, the metabolic fluxes in microorganisms, particularly if there is a need to include or replace  any additional enzyme currently not represented in a given metabolic pathway, which is essential for metabolic engineering and synthetic biology.Supplementary information 1:Table S1-1: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average amino acid (AA) property for each enzyme sequence .Table S1-2: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Table S1-3: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Table S1-4: The dataset used in multivariate analysis which comprises metabolic fluxes and numerical vectors representing the average AA property for each enzyme sequence .Supplementary information 2:Figure S2: The linear plots for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression models I-IV and by the leave-one-out cross-validation (LOOCV) of the models.Table S2-1: The variance analysis of the regression models.Table S2-2: Standard errors and confidence intervals for the linear regression models.Supplementary information 3:Table S3: Elements and the statistical indices for multiple linear regression model which links the values of metabolic fluxes and the average AA properties of the yeast Saccharomyces cerevisiae enzyme sequences expressed according to the specific AA indices.Figure S3: The linear plot for the metabolic fluxes of the yeast Saccharomyces cerevisiae glycolysis pathway estimated by kinetic models against those predicted by linear regression model.


Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic  Clostridum difficile is an opportunistic pathogenic bacterium that causes potentially life-threatening intestinal inflammation and diarrhea and frequently occurs after antibiotic treatment, which wipes out the normal intestinal bacterial community. We use a mathematical model to identify how the normal bacterial community interacts and how this community changes with antibiotic treatment and C. difficile infection. We use this model to identify bacteria that may inhibit C. difficile growth. Our model and subsequent experiments indicate that Barnesiella intestinihominis inhibits C. difficile growth. This result suggests that B. intestinihominis could potentially be used as a probiotic to treat or prevent C. difficile infection.The community of bacteria that live in our intestines  is important to normal intestinal function, and destruction of this community has a causative role in diseases including obesity, diabetes, and even neurological disorders.  The achieved bacterial density—area under the growth curve (AUC)—in a culture was calculated by integrating over the growth curve in each experiment using the “trapz” function in Matlab. It can be thought of as representing the total biomass produced over time. The simply additive null model was calculated by fitting a Lotka-Volterra model [B. intestihominis and C. difficile. The null model of co-culture (assuming zero interaction between species) was simulated by using the parameters from single culture, and summing the predicted OD870 values. chamber . Single  chamber . Maximumra model  to the shttps://bitbucket.org/gutmicrobiomepaper/microbiomenetworkmodelpaper/wiki/Home.All scripts used to analyze the data are available at To capture the dynamics of inter-genus interactions in the intestinal tract we employed a pipeline  which trLachnospiraceae nodes and Barnesiella is strongly influenced by clindamycin whereas the other subnetwork is largely independent of the first, except for the single edge between Barnesiella and C. difficile  in the clindamycin treatment group, but they do not appear in the final states of any of the treatment groups (see Firmicutes (Lachnospiraceae) and Bacteroidetes (Barnesiella) are devastated by antibiotic administration [C. difficile colitis: loss of normal gut flora, which normally suppresses opportunistic infection (clindamycin cluster), and the presence of C. difficile at a minimum inoculum (C. difficile cluster) [Barnesiella and C. difficile.Boolean rules  were infifficile . In factoups see . This nestration ,52. Furtcluster) ,53. The Barnesiella on C. difficile is in agreement with recently published findings in which Barnesiella was strongly correlated with C. difficile clearance [Barnesiella as an inhibitor of another pathogen (vancomycin-resistant Enterococci (VRE)) has been shown in mice [Barnesiella and Enterococcus  to determine all the possible steady states of the microbiome network model. In a 12 node network, there are 212 possible network states. We employed model simulations using the synchronous updating scheme to visit all possible network states and identify all fixed points of the model. Exploration of the steady states of this network reveals 23 possible fixed point attractors  independent of the clindamycin subnetwork. The third grouping has the clindamycin treated steady state (Attractor 7) and genera in the C. difficile subnetwork that can survive in the presence of the clindamycin. Group 4 contains the clindamycin plus C. difficile steady state (Attractor 12) and its subsets in which one or both of the source nodes Mollicutes and Enterobacteriaceae are absent. Group 5 contains attractors in which clindamycin is absent and C. difficile is present. Even if clindamycin is absent, our model suggests that C. difficile can thrive if Lachnospiraceae and Barnesiella are absent, i.e. these states represent a clindamycin-independent loss of Lachnospiraceae and Barnesiella. Lastly, group 6 attractors have both clindamycin and C. difficile as OFF. Blautia and Enterococcus are always abundant in these attractors. Indeed, because of the mutual activation between Blautia and Enterococcus they always appear together. Attractors in this group may also include the abundance (ON state) of the source nodes Mollicutes and Enterobacteriaceae.The attractor landscape can be divided into six groups based on abundance patterns they share . Group 1C. difficile treatment after clindamycin treatment was removed. Thus, we considered the clindamycin-treated steady state (Attractor 7 in C. difficile treated steady state (Attractor 12) as initial conditions and assumed that clindamycin treatment was stopped. Our simulations, employing asynchronous update (see Methods), indicate that for both initial conditions, only the state of clindamycin changes after the treatment is stopped; these steady states become Attractor 1 and Attractor 19, respectively .We next explored the perturbation of genera in the gut microbiome network model. We considered the clinically relevant question of which perturbations might alter the microbiome steady states produced by clindamycin or clindamycin+ectively . In otheectively , top rowdy state . From thEnterobacteriaceae contained the most reactions on average (1335), while those from Mollicutes contained the least (485) (Barnesiella and Enterococcus reconstructions contained the most unique reactions (Lachnospiraceae and Barnesiella had the next highest degree of overlap (424 reactions). Mollicutes and Coprobacillus had the least degree of overlap (363 reactions) (Species-level reconstructions from the genus st (485) . The Bareactions  and, inteactions . Lachnosactions) . Note thC. difficile is highly enriched for reactions in cyanamino acid metabolism compared to all other genera. Lipopolysaccharide (LPS) biosynthesis and cyanoamino acid metabolism subsystems are differentially enriched between C. difficile and both Barnesiella and Lachnospiraceae. Between Barnesiella and Enterococcus, Barnesiella is more highly enriched for d-glutamine and d-glutamate metabolism, pantothenate and CoA biosynthesis, LPS biosynthesis. With respect to Enterococcus, Barnesiella is less highly enriched in pyrimidine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis.Enrichment analysis was performed for the 99 unique subsystem annotations that were observed in the community. 22 subsystems displayed interesting enrichment patterns with respect to the structure of the interaction network . The subLachnospiraceae genera were treated as metabolically identical, and the “Other” group was excluded. We grouped pairs of genera into five groups based on being connected by a positive or negative edge, a negative or positive path (meaning an indirect relationship), or no path. A positive relationship was found between competition score and edge type in the interaction network (i.e. positive edges tend to have a higher competition score), which was not statistically significant, perhaps due to the small sample size  . The two um test) . The mutum test) . All paium test) . Barnesicile see . Barnesi network .The positive relationship between edge type and competition score suggests that more metabolic similarity between genera tends to foster positive interaction. The converse is also true, where less metabolic similarity tends to foster negative interactions . Here, “Barnesiellaintestinihominis was chosen as a representative species for the genus Barnesiella for the in vitro experiments. C. difficile grew more slowly in B. intestinihominis spent media   (C. difficile area under the growth curve (AUC), a measure of the achieved bacterial density over the experiment, was not statistically different between growth in fresh media and B. intestinihominis spent media . However, the co-culture displayed a much lower AUC than expected under a null model of interaction (in which the two species do not interact) (um test) . The co-um test) . C. diffnteract) . Examininteract) .Here we have developed a novel strategy for generating a dynamic model of gut microbiota composition by inferring relationships from time series metagenomic data . To our C. difficile infection on gut microbial community structure. We generate a microbial interaction network and dynamic model based on time-series data from a population of mice. We validate a key edge in this interaction network between Barnesiella and C. difficile through an in vitro experiment. Consistent with the literature, our model affirms that solely inoculating a healthy microbiome with C. difficile is insufficient to disrupt the healthy intestinal tract microbiome. Additionally, our results demonstrate that clindamycin treatment has a tremendous effect on the microbiome, greatly reducing many microbial genera, and that during the time C. difficile is present, a certain subset of bacteria come to dominate the microbiome  is unknown, but unlikely given the AUC data.We experimentally validated a key edge in the interaction network, and showed that nificant . The facC. difficile AUC did not significantly change between fresh media and spent media  to a state highly similar to the original healthy state (loss of C. difficile). This result is particularly interesting from a therapeutic design standpoint. In this case, the model results indicate that Barnesiella may serve as an effective probiotic. Model-driven analysis can be used to identify candidate organisms for probiotic treatments. Recent work by Buffie et al. performed a proof-of-concept study in which they used statistical models to identify candidate probiotic organisms, which were then tested on a murine model of C. difficile infection [et al., the integration of computational models in probiotic design has been shown to be a feasible, effective approach. Improved tools, such as the perturbation analysis presented here, will surely accelerate the probiotic design process and shorten the path to the clinic.Computational perturbation analysis showed that forced overabundance of nfection . This monfection . The modGenome-scale metabolic network reconstructions can be used to estimate the interactions between microbes in a complex community based purely on genome sequence data. Our use of genus-level metabolic network reconstructions  may not reflect the unique, species-level interactions and heterogeneity within a community. This higher-level model will only capture broad trends and the possible extent of metabolic capacity within a genus. Furthermore, the draft status of these models precludes the effective application of flux balance analysis (FBA) to estimate interactions among genera. This is due to the established lack of precision in draft reconstructions in predictions of growth rates and substrate utilization patterns , and theThe application of competition scores demonstrated here  could poC. difficile, Barnesiella showed the second lowest , but rather to some non-metabolic mechanism. The similarity in reaction content between Barnesiella and Enterococcus indicates similar network structure (Enterococcus does not inhibit C. difficile in the inferred interaction network (Barnesiella (65 unique reactions) and Enterococcus (36 unique reactions) are hints at the mechanism of interaction, or metabolism does not play a significant role in C. difficile inhibition in the environment of the gut. For example, enrichment analysis showed that that, with respect to Enterococcus, Barnesiella is more highly enriched for d-glutamine and d-glutamate metabolism, pantothenate and CoA biosynthesis and LPS biosynthesis. With respect to Enterococcus, Barnesiella is less enriched in pyrimidine metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. The possible role of LPS is discussed further on. The possible roles of these other metabolic pathways in C. difficile inhibition is unclear.Understanding this relationship between competition score and interaction type leads to the conclusion that negative interactions are probably not caused by metabolic competition. Of all the genus competition scores with d lowest . In othetructure , and yet network . Either Barnesiella  may combat pathogen overgrowth through non-metabolic mechanisms. As a first step, it has been shown that VRE can grow in sterile murine cecal contents—indicating the presence of sufficient nutrition to support VRE—but is inhibited in saline-treated cecal contents—indicating that live flora are needed to suppress VRE growth, and that this suppression is not through nutrient sequestration [B. intestinihominis has been demonstrated to prevent and cure VRE infection in mice [C. difficile infection in mice [Barnesiella plays a key role in pathogen inhibition, and pathogen inhibition can be caused by mechanisms other than nutrient competition.There is experimental evidence that stration . Further in mice , and is  in mice . Clearlyin vitro that B. intestinihominis can inhibit C. difficile growth rate  may prevent pathogen overgrowth by stimulation of a host antimicrobial response [Barnesiella can activate host toll-like receptor TLR signaling, which activates host antimicrobial peptide production. For example, LPS and flagellin have been shown to stimulate the host innate immune response through toll-like receptor (TLR) signaling and production of bactericidal lectins [Barnesiella shows enrichment for LPS biosynthesis pathways Barnesiel lectins ,69. Barnpathways . Howevernesiella . An indinesiella . Regardlin silico perturbation, which leads to fast candidate selection for probiotic design. We further describe the use of genome-scale metabolic network reconstructions to explore the metabolic potential attributed to community members, and to estimate metabolic competition and cooperation between members of the microbiome community. Analysis of genome-scale metabolic network reconstructions indicates that Barnesiella likely inhibits C. difficile through some non-metabolic mechanism. We present empirical in vitro evidence that B. intestinihominis does in fact inhibit C. difficile growth, likely by a non-metabolic mechanism, and our findings are in good agreement with published results. We present this work as a demonstration of the use of dynamic Boolean models and genome-scale metabolic reconstructions to explore the structure, dynamics, and mechanistic underpinnings of complex microbial communities.We demonstrate that dynamic Boolean models capture key microbial interactions and dynamics from time-series abundance data in a murine microbiome. We show that this computational approach enables exhaustive S1 FigC. difficile (at t = 0 days) and did not undergo observable microbial changes, Population 2 received a single dose of clindamycin (at t = -1 days), and Population 3 received a single dose of clindamycin (at t = -1 days) and, on the following day, was inoculated with C. difficile spores (at t = 0 days). Genus abundances were measured at 0, 2, 3, 4, 5, 6, 7, 9, 12, 13, 16, and 23 days; however, not all samples had measurements at all the time points. B) Cubic spline interpolation of data points was performed such that all the same time point measurements of bacterial abundance occurred in all samples and that single day intervals were present in all datasets.A) Genera abundance information for the nine samples. The “Healthy” population received spores of (TIF)Click here for additional data file.S2 FigIterative k-means binarization was completed on all the samples 1000 times and average binarization is shown for each genus at each time point in each of the nine samples. If a node (genus) is binarized as 0 (OFF) at a time step, then it is colored blue, and if a node (genus) is binarized as 1 (ON) at a time step, then it is colored yellow. This figure represents the average of 1000 replicates of IKM binarization. Intermediate cell colors represent cases where a genus abundance at a time point was binarized to 1 (ON) in a fraction of the replicates.(TIFF)Click here for additional data file.S3 FigThe most probable binarized state of each genus at each time point. If the average genus abundance binarization  was grea(TIFF)Click here for additional data file.S4 FigC. difficile steady state. These three steady states are directly corroborated by experimental metagenomic data.There are 23 predicted steady states in the Boolean model of the gut microbiome. Each attractor is a column in the heatmap and is made up of the state of each genus in the network model (rows). Each genus can be present above an activity threshold  or below an activity threshold . The steady states in the model are grouped based on their similarities to other steady states in the same group. The first steady state of group 2 (Attractor 2) is the healthy steady state, the first steady state of group 3 (Attractor 7) is the clindamycin treated steady state, and the first steady state of group 4 (Attractor 12) is the clindamycin + (TIFF)Click here for additional data file.S5 FigBarnesiella and C. difficile corresponds to the highest mutualism score.A) Competition score values for all classes of paths through the network, including direct edges, directed paths, and no directed path. A positive relationship was found between competition score and direct edge type in the dynamic network (self-edges were excluded), which was not statistically significant, perhaps due to the small sample size , but is worthy of note. B) Mutualism score values for all classes of paths through the network, including direct edges, directed paths, and no directed path. C) Competition and mutualism score plot for the interaction edges in the network. All the interactions reflect moderate to high competition scores and relatively low mutualism scores. All the interactions have a higher competition score than mutualism score. The two negative interactions (red circles) do not have higher competition scores, nor lower mutualism scores, than the positive interactions. In fact, the negative interaction between (TIF)Click here for additional data file.S1 TableThe ruleset inferred from metagenomic sequencing information using Boolnet.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableAkkermansia is represented by a single species-level reconstruction, while several genera are represented by 10 species-level reconstructions. The average network overlap within a genus refers to the average number of shared reactions between any two pairs of species within the genus. Similarly, the average fraction of unique reactions refers to the average subset of reactions in a given species that are unique within the genus.In this table we characterize the genus-level metabolic network reconstructions. Average model size refers to the average number of reactions in the component species reconstructions within each genus. (DOCX)Click here for additional data file.S4 TableBarnesiella contains 167 reactions that the reconstruction for C. difficile does not. Conversely, the reconstruction for C. difficile only contains 30 unique reactions that the reconstruction for Blautia does not already contain.The genus in each row has n reactions that the genus in the columns do not have. For example, the genus-level reconstruction for (DOCX)Click here for additional data file.S5 TableThe upper portion of the table contains the number of shared reaction content between all genus-level metabolic network reconstructions.(DOCX)Click here for additional data file.


The increased demand and consumption of fossil fuels have raised interest in finding renewable energy sources throughout the globe. Much focus has been placed on optimizing microorganisms and primarily microalgae, to efficiently produce compounds that can substitute for fossil fuels. However, the path to achieving economic feasibility is likely to require strain optimization through using available tools and technologies in the fields of systems and synthetic biology. Such approaches invoke a deep understanding of the metabolic networks of the organisms and their genomic and proteomic profiles. The advent of next generation sequencing and other high throughput methods has led to a major increase in availability of biological data. Integration of such disparate data can help define the emergent metabolic system properties, which is of crucial importance in addressing biofuel production optimization. Herein, we review major computational tools and approaches developed and used in order to potentially identify target genes, pathways, and reactions of particular interest to biofuel production in algae. As the use of these tools and approaches has not been fully implemented in algal biofuel research, the aim of this review is to highlight the potential utility of these resources toward their future implementation in algal research. Biofuel production from microalgae has been receiving attention as an alternative energy source due to its high biomass productivity and minimal land resource requirement. However, there is still a need to improve algal productivity in order to make algal-based bioproducts economically viable. Metabolic network reconstructions of algae can offer insight into genetic modification strategies that can be used to improve microalgal strains. A large number of computational tools have been developed, allowing a range of analyses and predictions, based on genetic and thermodynamic constraints embedded in in the network, to identify bioengineering strategies that can result in enhanced biofuel production of the engineered algal strain. Although a fair number of algal genomes have been fully sequenced, only a few metabolic network models have been reconstructed for these species, hampering algal bioengineering progress . E. coli, provides strategies for algal biofuels optimization with major enhancement potential.The utilities of metabolic network models span over several types of applications. On one hand, these models help contextualizing high throughput experimental data, for example, integrating gene expression data with metabolic pathways under different growth conditions . MetabolMetabolic network reconstruction from genomic and large-scale experimental data can help understand and predict metabolic processes and pathways. A number of tools and databases have been developed specifically to facilitate metabolic network reconstruction. In addition, new analysis tools and approaches are being developed along with the expansion of relevant databases and resources. Metabolic network reconstruction requires information on gene-protein-reaction associations to reconstruct evidence-based, species-specific networks. Protein database resources and tools help to link information between enzymes, EC numbers, genes, proteins, pathways, and substrates. These include BRENDA , ExPASy  Thalassiosira pseudonana, Nannochloropsis gaditana, Acaryochloris marina, Anabaena cylindrica, Anabaena variabilis, Synechococcus elongatus, and Chlamydomonas reinhardtii. None of the aforementioned algal PGDBs are well-curated with most of them having had slight validation. One of the intensively curated PGDBs is MetaCyc [ Homo sapiens (HumanCyc), E. coli (EcoCyc), and Arabidopsis (AraCyc) are some examples of curated, species-specific knowledge bases that can be found in BioCyc (http://biocyc.org/). Kbase (http://kbase.us/) and Biomart [Pathway Tools , 18 is a MetaCyc –21, whic Biomart  are otheGenome-scale metabolic reconstructions have continued to expand along with the increased availability of sequenced, annotated genomes. Recent reviews describe the timeline of the appearance of publicly available metabolic models since 1999 for eukaryotes, prokaryotes and archea, and the algorithms that were used , 24. TheThe process of network reconstruction, starting from genome sequences to the finished reconstructed network, is generally time-consuming and labor-intensive. Therefore, automation of the process has been of interest. A limited number of software tools for automated reconstruction are currently available (some examples are given in http://www.biocarta.com/), ExPaSy , and KEGG (Kyoto Encyclopedia of Genes and Genomes) can be named; however, most are static pages with only a few resources allowing authorized users to edit the pathways. The advantages that BioCyc/MetaCyc offer compared to KEGG include the ability to carry out pathway analysis, operon prediction, or comparative pathway analysis  and visCytoscape , 37 is aMost recently, MetDraw , a new tAlthough the generation of metabolic network models has been gaining momentum, these models may not provide a complete or accurate representation of metabolism. Particularly, automated modeling has allowed the faster generation of network models, yet reconciliation between the model itself and the biochemical and genomic data is invariably needed. Such model refinements lead to a more accurate reconstruction, allowing more accurate downstream analyses. A common step in such reconstruction refinements is filling reaction gaps to decrease the numbers of dead-end metabolites and enhance the network connectivity. Several tools and algorithms have been set in place to address gap finding and gap filling in metabolic network reconstructions. Some of these tools include, but are not limited to, Gapfill, MEP, GrowMatch, BNICE, and the hole filler in Pathway tools.These tools have been developed using two distinct algorithms that initially identify (Gapfind) what the authors have called a “no production” or “no consumption” metabolites  through On the other hand MEP and Pathway Tools hole filler represent an alternative approach that tackles the gap filling issue identifying missing genes rather than missing reactions, and these tools achieve this goal using expression data and species homology, respectively. As such, this will eventually lead to the expansion of the reconstructed model to include more genes and enzymes and possibly rewire the connectivity of the network , 48. in silico and growth data in vivo. This computational tool can suggest suppression of specific genes to resolve what is referred to as Growth No Growth (GNG) inconsistencies and alternatively adds functionalities to genes to resolve No Growth/Growth (NGG) inconsistencies [This tool has been developed as a model refinement tool rather than a gap filler tool where the aim of such an application would be to reconcile inconsistencies between metabolic model predictionsstencies . per se, the outcome of the pathway optimization can ultimately lead to provisional addition of compounds to the metabolic model and subsequent searches (independently from the tool) for the corresponding genes to provide genomic evidence for the pathway. This approach is similar in outcome to the Gapfind/Gapfill approach.It is a framework that considers specific pathways rather than the full-scale model and allows for the optimization of the pathways. It identifies all possible chemical compounds that can be produced by the reactions and enzymes of the pathway . AlthougAll of the above and many more tools are of critical importance in the manual curation of metabolic network models. Although the above-mentioned tools ultimately lead to a similar outcome, each may present unique advantages and has specific requirements . The cho in silico using flux balance analysis (FBA) [ in vivo experiments.Subsequent to generation of well-curated metabolic network models of organisms, several downstream applications can be used to explore the emergent system's properties. Having a network set in place, the fluxes of each of the component reactions can be evaluated and moreover modified in an attempt to increase or decrease the production or consumption of key metabolites. In the case of algal biofuel optimization, it is of high interest to achieve directional overproduction of lipids that constitute the basis for algal biofuels. Making use of the known metabolic networks and via a constraint based modeling approach, the identification of genes, pathways, and knockout strategies, that interfere or alter, the expression profiles relevant to production of enzymes related to lipid synthesis and metabolites involved in lipid synthesis pathways is readily achievable. This can be done through a number of computational tools with the outcomes evaluatedis (FBA)  and furtFBA constrains the metabolite fluxes and their biochemical reactions by four main parameters: mass conservation, thermodynamics (reaction reversibility), steady state assumption for internal metabolite concentrations, and nutrient availability. Based on these constraints, reaction boundaries are set, and a system of linear differential equations is solved with a biologically relevant objective function optimized. The solution space for an FBA can be reduced in size by more constraints and boundaries imposed on reactions and fluxes where the optimal flux distribution achieving the optimized function is a feasible solution for the problem.Some of the available tools and algorithms that are able to perform such tasks include (but are not restricted to) Optknock, Optstrain, Optflux, MTA, iMAT, BioMet toolbox, PROM, GIMME, E-Flux, MADE, SIMUP, and TIGER, with some allowing the integration of expression data to the metabolic model. These tools are described below.Gene inactivity moderated by metabolism and expression (GIMME)  is a tooWhile the above-mentioned tools allow the incorporation of expression data to metabolic model reconstructions and subsequently allow optimization of these models towards a set objective function by suppressing reactions categorized as inactive or of low activity, E-flux allows for this optimization through constraining the upper bounds of the metabolite fluxes based on the expression data by imposing tight constraints on metabolites and reactions where the fluxes will not reach a set value and vice versa .These tools have been used to identify gene knockout strategies (Optknock)  that leaIt is a web-based resource that can be used to perform stoichiometric analyses and integration of transcriptome and interactome data to a metabolic network. It also allows performing linear programming simulations, optimizing for an increased or decreased growth rate, as well as substrate consumption and production. Single or double knockout simulations can also be achieved as well as the detection of key metabolites around which high transcriptional activity is noted .Metabolic transformation algorithm  is an alIt is a toolbox that can be used to integrate expression, metabolic and regulatory information into a genome scale model. It also accounts for gene-protein-reaction associations and couples it with its regulatory profile. One of its added values is its ability to identify model inconsistencies and thus it allows for a modification of the reconstructed network above and beyond being an integration tool .Most recently, this algorithm was reported offering one unique feature with respect to all of the above introduced tools. The algorithm aids in identifying metabolic engineering strategies that can force the cell to coutilize two different sugar substrates thus, in effect, placing the cell in a “synthetic survival” state in a way that the cell is now forced to metabolize two different sugars simultaneously instead of preferentially consuming one. The net effect can be to simplify the fermentation cycle , 65.In the context of biofuels, all of the above algorithms and tools present huge potential for achieving higher production of the desired bioproducts in microorganisms. The preferential use of one tool over the other may depend on the nature of data available rather than the ultimate goal . The ideBeyond the integration of expression data to network models, a deeper understanding of the functional model requires further integration of proteomics, metabolomics, fluxomics, and phenotypic data with transcriptomics data. Computational tools and algorithms have been recently set forth to achieve the aforementioned integrations. IOMA, MASS, and MBA are examples of such endeavors.Integrative omics-metabolic analysis is an algorithm that allows the integration of metabolomics and proteomic data to the metabolic network model and also evaluates the kinetics of the reactions included .Mass action stoichiometric simulation  achievesModel-building algorithm  has beenWith respect to phenotypic data, one interesting tool that may generate such type of data and can be used in conjunction with MBA, for example, is the Biolog phenotype microarray technology , 72. The in vitro assay that measures the respiration of cells as a function of time in hundreds of microwells simultaneously. Each PM plate contains 96 wells seeded with different metabolite and monitored automatically over time via the OmniLog machine. Metabolite utilization within the cell is determined by the amount of color development produced by a tetrazolium-based redox dye. Various 96-well metabolite plates (or PMs) can be used to measures carbon source, nitrogen, sulfur, and phosphorus utilization phenotypes. Some plates were used to test for osmotic/ion and pH effects. Data analysis is performed using the opm software package [The phenotype microarray (PM) technology developed by Biolog  can be used for the phenotypic analysis. Biolog is an package . The Bio package . in vivo. This is where the contributions of synthetic biology approaches are of crucial importance and significance. Once the target pathways have been identified, the parts forming those pathways, in engineering terms, are to be made available in order to mimic the cell metabolic circuitry and alter it. Parts are defined as genes and ribosomal binding sites, promoters, terminators, and polymerases [ Dunaliella salina, transforming it witha bioengineered plasmid comprising specific parts, genes, and inducible promoters, driving the cellular carbon flux into the fatty acids biosynthesis pathway [With all of the above tools readily available to use and many others currently in use but not described in this review, the identification of new pathways and reactions has been made easier than ever before. In the context of bioengineering, the significance of these computational tools is in guiding wet-bench experimental design as opposed to providing solely theoretical insight into the system as a whole. More specifically, with regard to biofuel production, the identification of knockout strategies or differential expression of genes or enzymes that might lead to overproduction of biofuels would be only of theoretical value if not coupled with more applicable approaches to achieve the targetsymerases . Most re pathway .http://partsregistry.org/). A more in-depth review on the tools and applications that lead to the design of circuits was published by Marchicio et al. and can be referred to for more details [Biological circuits are furthermore defined as a designed device made out of a set of parts and engineered in a way to confer an added functionality to a system.  details .Optimizing algae for biofuel production requires a deep understanding of algal metabolic networks with genomic, fluxomics, proteomics, and metabolomics data integration.  Chlamydomonas reinhardtii and Chlorella NC64A [Furthermore, a better understanding of the biological system through functional modeling using data generated from the sequencing technologies is still one of the research challenges. Functional modeling requires gene ontology (GO) annotation for enrichment analysis. GO enrichment analysis tools identify GO terms with statistical significance in the reference set. Algal Functional Annotation Tool is the algae-specific genome annotation tool that uses gene lists from AUGUSTUS, JGI, or phytozome gene models forla NC64A  to perfo C. reinhardtii thus making available to the scientific community a full library of TF and CF parts that can easily be introduced as part of a new design.  C. reinhardtii has been previously generated and the reconstruction of its central metabolic network has been done [ C. reinhardtii were released accounting for around 2000 reactions and their associated genes and metabolites [ C. reinhardtii has been made available as ChlamyCyc [http://chlamycollection.org/), has served the algal community offering a library of Chlamydomonas strains amongst other parts and tools, which provide needed resources for experimental protocols targeting various aspects of algal biology, including the metabolism of lipids and biofuels in this organism.Interestingly, a new approach in bioengineering, transcription factor engineering approach (TFE) , is regaeen done –84. Follabolites , 85. AddhlamyCyc  making uThe above reviewed computational tools and approaches in conjunction with the high interests of the scientific community in synthetic biology offer a new perspective in accelerating biofuel production and microalgal optimization research. The pressing economical and environmental challenges of the use of fossil fuels will furthermore lead to a positive selective pressure towards the use of these strategies aiming at the optimization of biofuel producing strains. A large set of biofuel types can serve as alternative energy sources which currently include ethanol, n-butanol, iso-butanol, short chain alcohols, short chain alkanes, biodiesel (FAMEs), and fatty alcohols. These tools and applications are promising yet much more optimizations need to be achieved in order for biofuel production to compete with available fossil fuels. With the “green revolution” and the more environmentally conscious population, we expect this field to expand significantly in the coming years, building on the available resources for systems and synthetic biology and achieving the generation of strains optimized for biofuel production.


SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches  where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and  In this article we present SteatoNet, the most comprehensive computational network of hepatic metabolism and the interaction of the liver with extra-hepatic tissues. Generation of the SteatoNet involved the application of engineering strategies to resolve prevalent drawbacks in biological modelling and thus effectively understand basic biology. SteatoNet does not require detailed kinetic parameters, behaves representatively of biological observations and portrays systemic interactions. SteatoNet is simple and flexible, which is an important advantage over current computational models of complex metabolic disorders associated with the liver. To demonstrate the utility of SteatoNet as a hypotheses-generation tool, we studied candidate mechanisms of hepatic fat accumulation, the key characteristic of non-alcoholic fatty liver disease (NAFLD), a common but poorly understood metabolic disorder influenced by genetic and lifestyle factors. Our data describe NAFLD as a network disease, with deregulated pathways such as inter-tissue transport of glucose, lipid and ketone bodies, cholesterol metabolism and “stiff” focal points in the fat tissue. These results indicate the involvement of systemic metabolic deregulations in the transformation of healthy to fatty liver in NAFLD. The SteatoNet highlights the utility of engineering approaches in systems biology to aid in solving complex biological questions. Mathematical modelling provides an intuitive tool to investigate complex diseases that display multiple causal mechanisms and a complex pathobiology. In particular, liver-associated pathologies that have a high prevalence in Europe and other Western populations face poor prognosis, and the lack of adequate molecular understanding and management in vitro experiments are at present poorly documented, display variability and are frequently incompatible with molecular behaviour in vivoMathematical models describing hepatic metabolic pathways have been constructed, including models of cholesterol synthesis Taking into consideration the currently prevalent drawbacks, we describe the SteatoNet, the first multi-pathway and multi-tissue model including key hepatic metabolic pathways, their interaction with extra-hepatic tissues and hierarchical feedback regulation at the gene expression and signal transduction levels. The kinetic parameters of the network are computed for a specified normalised steady state by utilizing user-defined values for the reversibility of reactions, the distribution of fluxes at pathway branches, and the metabolic influx into the network. Thus, the challenging task of accurately selecting kinetic parameters for large metabolic networks is bypassed. The estimated parameters are semi-quantitative and provide insights into the global system behaviour rather than accurate estimations of model variables.In order to illustrate the utility of SteatoNet in investigating liver-associated pathologies, we describe a model analysis to identify candidate mediators involved in the initiation of non-alcoholic fatty liver disease (NAFLD). NAFLD, the hepatic manifestation of the metabolic syndrome, is the most common chronic liver disease in western populations with a prevalence of 25–30% r, the distribution of the metabolic influx f into alternative pathways, the total influx Iφ and the ratio between bound and free enzyme, w. This methodology transforms classical Michaelis-Menten kinetic parameters into a notation that differentiates static  and kinetic parameters . The model notation used in SteatoNet reduces the number of model parameters that must be derived from data or prior knowledge. In the presented model, 1046 or 25% of the total model parameters that describe the network must be manually set, the rest are calculated from the steady-state relations. A systems biology library of objects corresponding to biological entities was utilised to compile the SteatoNet (Steatosis Network) in a systematic workflow  to form The SteatoNet comprises 194 reactions with 159 metabolites, 224 enzymes and 31 regulatory proteins. It includes the glucose, fatty acid, cholesterol and amino acid metabolic pathways, the inter-tissue transport of metabolites between the liver and extra-hepatic tissues and regulation at the transcriptional and post-translational levels by transcription factors, hormones, cytokines, adipokines and other regulatory factors. The hepatic compartment accounts for 70% of the SteatoNet model , the adipose compartment for 15% , other extra-hepatic tissues for 9%  and components in the blood and macrophage for the remaining 6% .To validate the SteatoNet, we simulated metabolic conditions that have been well studied, including the response to fasting, the absence of stearoyl-CoA desaturase (SCD), a crucial lipogenic enzyme, overexpression of adiponectin, an insulin-sensitising anti-inflammatory cytokine released by the adipose and hepatic steatosis triggered by a high-fat diet, which can be subsided on treatment with a peroxisome proliferator-activated receptor alpha (PPARα) agonist. The fold-change in variable values in each simulation is with respect to their normalized value of 1.0 at the initial steady state. Hence, at steady state the observed changes are the net result of model perturbation. In the case of inconsistencies between experimental observations and model simulations, a series of simulations was implemented to identify the network components that display erroneous behaviour. Additional expert-based literature searches were performed to identify regulations that have been established experimentally but were absent in the network.Fasted state is characterised by low glucose and insulin levels and increased glucagon in the blood. Glucagon activates glycogen phosphorylase for the breakdown of stored glycogen to glucose and inhibits glycogen synthase. It upregulates gluconeogenesis by increasing the expression of phosphoenolpyruvate carboxykinase (PEPCK) and downregulates fatty acid synthesis by deactivating acetyl CoA carboxylase (ACC1). Fasting induces peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor that regulates mitochondrial and peroxisomal fatty acid oxidation To simulate fasting, the glucose influx into the network was reduced by 10-fold compared to the initial steady state. As illustrated in de novo lipogenesis and lipid metabolism SCD−/− mice fed a high fat diet display protection against diet-induced obesity and increased insulin sensitivity due to an upregulation of fatty acid oxidation and downregulation of lipogenesis de novo lipogenesis, SCD−/− mice fed a lipogenic diet still display low levels of triglycerides, indicating the crucial role of SCD-1 in regulating triglyceride synthesis Stearoyl CoA desaturase (SCD) is a rate-limiting delta-9 desaturase enzyme involved in 5 units and on a high-fat diet between time 105 and 2×105 units. Simulations of the response of triglyceride concentration, fatty acid oxidation and lipogenesis enzymes indicate a decrease in hepatic triglyceride accumulation and increase in CPT-1 and ketone bodies on both diets. While the lipogenic enzyme glycerol-3-phosphate acyltransferase (GPAT) and its transcriptional regulator SREBP-1c were upregulated by the lipogenic diet, their concentrations decreased on a high-fat diet, in concordance with experimental observations in rodent models. The spike in triglyceride concentration observed at time = 1×105 units results from the switch between the lipogenic diet (high glucose) to the high-fat diet. de novo synthesis of triglycerides. Hence, excess lipids are either oxidized or distributed to maintain triglyceride homeostasis.To simulate the SCD knockout condition, the rate of SCD enzyme degradation was increased by 1000-fold, resulting in SCD enzyme concentration approaching 0 . A lipogAdiponectin is an adipose-secreted cytokine that correlates negatively with insulin resistance, plasma triglycerides and low-density lipoprotein (LDL) – cholesterol, hepatic fat content and progression to NASH in NAFLD patients To simulate the overexpression of adiponectin under fasting conditions, the rate of adiponectin degradation was decreased by 10-fold, resulting in increased levels of adiponectin, and the glucose influx was reduced by 10-fold. In concordance with experimental observations, increased adiponectin concentration results in downregulation of serum fatty acids, hepatic triglyceride, TNF-α, ceramide, hepatic lipogenic genes SREBP1c, GPAT and SCD1 and increased expression of adipose CPT-1, indicating an upregulation of β-oxidation in adipocytes . The simFenofibrates, PPARα agonists, have been investigated in-depth for their beneficial effects on various features of the metabolic syndrome. Treatment with a specific PPARα agonist, Wy 14,634, in various mouse models with metabolic deregulations such as lipoatrophic diabetes 5 units). Simulation of a high fat diet by upregulating the triglyceride and cholesterol influx by 5-fold and 4-fold respectively, and downregulating the glucose influx by 2.5-fold results in hepatic triglycerides accumulation by 1.5-fold (5 units) diminishes hepatic triglyceride accumulation .The branch-points were classified according to their concentration control coefficients with respect to hepatic triglycerides as ‘high’  but not qualitatively i.e. no branch-points were identified where a change in f altered the direction of fold-change in variable values. Interpretations from the SteatoNet are qualitative in nature; hence, the set/assumed values of f do not affect the conclusions drawn from the model simulations. However, in real biological systems, the values of f at the highlighted branch-points significantly impact the concentration of biological entities whereby even subtle fold-changes may result in phenotypic changes. The mentioned concentration control coefficients are estimates calculated from SteatoNet variables and have been presented to illustrate the relative degree of variation resulting from changes in flux distribution. Branch-points that significantly impact hepatic triglyceride concentration and their regulators are discussed in detail in the following section.The sensitivity analysis indicated that a range of values of h points , the fluA majority of the branch-points that significantly impact hepatic triglyceride concentration occur in the inter-tissue transport of lipid, glucose and ketone bodies  as discuux range .via chylomicrons) and very-low density lipoprotein (VLDL) breakdown to serum fatty acids; and transport of serum cholesterol to the liver (reverse cholesterol transport via high-density lipoprotein), adipose and other extra-hepatic tissues  rodent models and patients ntration , wherebyThe exchange of glucose between the serum and liver, transport of glucose to the adipose tissue and contribution of dietary glucose to serum glucose level, display a significant influence on hepatic triglyceride concentration . SteatoNThe synthesis and transport of acetoacetate and β-hydroxybutyrate (BHB) display a significant negative impact on hepatic triglyceride accumulation , 7i–7k. de novo lipogenesis , SteatoNet provides an effectively regulated system-wide virtual space for generating scientifically sound hypotheses. The omission of details in the model may have an important impact on the small-scale cell metabolism but is completely masked at a network-wide scale. The SteatoNet is thus the first integrated model of human metabolism that represents the multiple layers of metabolic regulation.iHepatocytes2322, a comprehensive genome-wide network reconstruction of hepatocyte metabolism was utilised to analyse transcriptomic data from NASH patients and identify diagnostic biomarkers Experimental evidence has indicated the systemic nature of NAFLD pathogenesis, involving the role of the adipose tissue Sensitivity analysis of the SteatoNet was aimed to identify small changes in pathway fluxes that significantly influence hepatic triglyceride concentration. Such focal points in the network have a significant effect on NAFLD onset, as small disturbances in the metabolic network can result in large effects on triglyceride concentration. To-date, a limited number of polymorphisms have been associated with NAFLD populations indicating the absence of dominant genetic aberrations that are causal of this disorder. Taking into consideration the complexity of NAFLD, it can be expected that the disease is initiated as a result of multiple subtle changes in the metabolic homeostasis ultimately resulting in hepatic steatosis.We provide evidence that NAFLD is not solely the hepatic manifestation of the metabolic syndrome but arises as a result of network-wide perturbations in flux distributions at the organism-level, ultimately resulting in hepatic steatosis. The critical dependence of hepatic triglyceride concentration on inter-tissue transport reactions highlights the multi-compartmental nature of NAFLD and thus, disruption in the homeostatic balance of lipid distribution is at least partially causal of the disease state. The accumulation of hepatic triglycerides may initially be triggered due to a deregulation in pathway branch-points that are “intolerant” to flux alterations. The enrichment of these focal points in the adipose compartment for maintaining a balance in triglyceride synthesis, storage and hydrolysis, may indicate the initial role of adipose malfunction in triggering hepatic steatosis. The “threshold effect” displayed by these branch-points may be specific to individuals and may potentially explain the variability in NAFLD pathogenesis. This trigger may elicit functional changes in regulatory factors, such as FXR, which in turn activate a cascade of alterations in the rest of the network. In addition to confirming hepatic triglyceride sensitivity to previously identified deregulations in NAFLD, the SteatoNet identified novel candidate mechanisms, such as cholesterol transport, ketone body metabolism and regulatory functions of FXR, LXR and SREBP-2, that require experimental focus in the future. However, potential mechanisms that result in altered flux distributions remain an open question. Regulation of metabolic flux is a complex phenomenon that cannot be pinpointed to a single mechanism, as metabolic pathways are intricately connected and involve multiple enzyme isoforms with different substrates that are regulated by numerous factors at the transcriptional and translational level. Moreover, the inconsistency between the high prevalence of NAFLD and the small number of genetic variants identified in association with the disease indicates a critical role of environmental and dietary factors. The ability of SteatoNet to simulate the effect of the diet on metabolic homeostasis is a vital feature to investigate complex disorders and provides an advantage over currently available models. Dietary factors cause diverse changes in the organisms' metabolic status by triggering the activation or inhibition of regulators, such as hormones, cytokines and nuclear receptors In summary, we highlight the novelty of SteatoNet as a highly regulated multi-tissue platform, with the flexibility to systematically investigate genetic and dietary effects influencing hepatic metabolic homeostasis, in the absence of constrained kinetic parameters. SteatoNet computationally suggests that a single ‘hit’ is not sufficient to trigger hepatic steatosis. The concerted and cumulative action of various focal points in a systemic network are responsible to elicit significant phenotypic changes. SteatoNet thus promotes the application of basic engineering modelling strategies to solve complex biological questions in a simple and efficient manner.http://www.genome.jp/kegg/) and the Reactome (www.reactome.org) databases. Regulation at the transcriptional and post-translational levels has also been incorporated following expert-based manual inspection of the literature. The literature was manually scanned (>500 articles) to select studies that identify and confirm regulatory interactions in various metabolic pathways prior to the incorporation of regulation in the SteatoNet. Putative interactions that have not been confirmed were not included in the model. The inclusion of nuclear receptors in the SteatoNet was based on their confirmed role in the glucose, lipid and amino acid metabolic pathways portrayed in the SteatoNet. The included nuclear receptors were verified for their regulatory role and ligands by extensive manual search of the literature and databases such as NURSA . Hence, only receptors with known expression, endogenous ligands and specific targets in either the liver/adipose/muscle tissues were included in the SteatoNet. Regulatory interactions that were unintentionally omitted were identified during the model validation procedure and incorporated in the model. Thus, the incorporation of the regulatory layer in the model was done in an iterative manner to ensure correspondence with biological behaviour.An object-oriented modelling and simulation programme based on the Modelica language, Dymola  de novo lipogenesis, β-oxidation, lipolysis, amino acid metabolism, ketone body synthesis and the transport of metabolites between the liver, adipose tissue, pancreas, other extra-hepatic tissues and macrophages via the blood. The external nutrient sources  have been incorporated into the SteatoNet. These influxes represent the dietary intake and intestinal absorption of metabolites into the portal vein or secretion into the intestinal lymph (in the case of chylomicrons), which supply nutrients to the liver. The enzyme levels are governed by gene expression objects, which are regulated by transcription factors, such as PPARα, PPARγ, SREBP-1c, SREBP2, LXR, FXR, glucocorticoid receptor and PPARγ coactivators 1 alpha (PGC1A). The regulatory action of the hormones insulin and glucagon, the adipokines leptin and adiponectin and the cytokine TNFα has also been incorporated. The metabolites, enzymes and non-enzymatic regulatory proteins included in SteatoNet and the pathways they are associated with, are listed in The pathways included are glycolysis, gluconeogenesis, citric acid cycle, pentose phosphate pathway, https://openmodelica.org/openmodelicaworld/tools). An open-access version of the SteatoNet along with the systems biology library has been included in the supplementary materials . These ff, in each of the pathway branches is an additional independent parameter that must be specified in the model. Vo et alf at various SteatoNet branching points. The flux distribution proportion was calculated by summing the total flux at the branch-point and determining the proportion entering each branch as a fraction of the total flux. The choice of tracers in f, an arbitrary value was assigned that resulted in stable model simulations. For several low tolerance branch-points, the value of f was not completely arbitrary as these focal points can tolerate only a low range of flux distributions.The distribution of fluxes at pathway branch points is defined by additional model equations that set the initial ratio of flux distribution from the parent pathway into each branch. Thus, the distribution of the metabolic flux, et al study were utilized to approximate f at branch-points within SteatoNet in order to gauge a physiological value for parameter assignment, while taking into consideration that in vitro isotopomer studies are subject to large variances due to differing cell culture conditions, sampling, pre-analytical processing, etc. f is critical in terms of model identification rather than quantitative reproduction of hepatic function. Hence, the assignment of arbitrary flux distribution values is feasible, provided that the model generates stable simulations and can simulate biological phenomena for validation purposes. In addition, immortal cell lines such as HepG2 display alterations in dynamics compared to primary cells and do not realistically depict organism-level flux distributions The flux estimates from the Vo S, E, C and P denote the concentrations of the substrate, enzyme, enzyme-substrate (ES) complex and product, respectively. Constants Ck, Pk, CRk and PRk represent the rate constants of complex formation, product formation, ES complex dissociation into the enzyme and substrate and product reversion to the enzyme-product (EP) complex. Iφ, Oφ, EIφ and EOφ correspond to the substrate influx, the product efflux, the enzyme influx and the enzyme degradation flux. f denotes the proportion of the total substrate influx into alternative pathways.The modelling method based on DAEs and analysis in steady-state utilised to build the model have been described previously S, E, C and P at steady-state are represented by SSS, SSE, SSC and SSP. These variables can be converted into dimensionless quantities by normalising them with their steady-state counterparts. Thus,et alNS, NE and NP are equal to 1 and according to the relation described above, N = wC. The rate constants are now described with incorporation of w:r, the distribution of the influx f into alternative pathways, the total influx Iφ and the ratio between the bound and free enzyme, w. To derive the relations of the variables at the new steady-state, *f at which the system settles in an event of disturbance, we substitute these variables into the steady-state form of r, is an inherent property of the enzyme and the equilibrium constant between the species involved in the reaction. r represents the initial ratio of the reverse and forward metabolic flux, required to estimate the model parameters at the initial steady state. The parameter r was fixed based on literature searches for reactions with known reversibility ratios. For reactions with known reversibility but lack of specific defined ratios, r was fixed as 0.5 and for irreversible reactions r was fixed as 0. When the network is disturbed, a new steady state is reached where the reverse-forward flux ratio (when r>0 i.e. for reversible reactions) depends on the entire network dynamic properties and the type and magnitude of the disturbance. Most reactions in higher organisms have low reversibility, thus r is small, however, our simulation studies show that model performs well even for values of r above 0.5. Although, the squared form of r diminishes its influence on the concentration of the substrate, enzyme or product, it accounts for the thermodynamic constraints related to Gibb's free energy in the reactions. The flux distribution, *f, in metabolic pathways in the event of a disturbance adapts depending on the newly attained values of substrate influx, demand of downstream pathways and alterations in enzyme concentration in the acquired steady state and also on the initial network flux distribution. Thus, the value of *f is calculated from the variable values at the new steady state attained after the disturbance.Concentrations of 14)(15)16 illusmRNAQ represents relative concentration of mRNA, maxQ is the maximum relative mRNA expression, Oφ is the transcription flux, CQ represents the concentration of the regulatory molecule and dk depicts the rate of mRNA degradation. Similarly, positive expression control is described by the following equation:maxQ). The generation of protein is described as a linear relation between the relative concentration of mRNA and protein/enzyme quantity (PQ):tk represents the rate constant of mRNA translation and dk is the rate constant of protein degradation. The quantity of the regulatory molecule (CQ) is usually controlled by activation or deactivation of the molecule by some metabolite quantity (MQ), which is described in a linear manner. The rate equation for protein activation is:CIQ and CAQ represent the pool of inactive and active protein, ik and ak are factors describing the activation or inhibition of the protein and the dk term in In addition to the biological entities involved directly in metabolic reactions, feedback regulation was also incorporated into SteatoNet. mRNA transcription was modelled as a sigmoid function with separate object classes specified for positive and negative expression regulation. Negative expression control is described by the following equation:ation is:Where QCITo determine if the SteatoNet correctly depicts biological phenomena, simulations were compared to experimental data from the literature. The model was translated by the Microsoft visual studio 10.0 C compiler and simulations were generated by the default multi-step dassl solver in Dymola with a tolerance set to 1e-009. Model simulation is a multi-step numerical procedure that runs from time = 0 until arbitrary selected end time. In each step every equation of the model is evaluated. First, the derivatives of the model variables are calculated and next, the derivatives are used to change the values of the variables. The size of the simulation step is determined such that the derivatives of the variables are not too large since this would affect the simulation precision. In the case of the SteatoNet simulations, the dassl algorithm r, f, w and Iφ. The simulations depict relative changes in the network components in response to triggers causing a shift from the initial steady state. The large number of model parameters in the SteatoNet presents an additional facet for model validation. In order to obtain further credibility of the model simulations, a parameter sensitivity analysis was performed to observe the sensitivity of simulation results with respect to f, as detailed in section 1.2.1. The effect of r has been previously tested on smaller models showing a minimal effect on model variables than f. w only affects the dynamics of transition from one steady-state to another and hence, does not influence the variable values at a particular steady-state. For the various substrate influxes in the SteatoNet , several values of Iφ have been tested and these directly affect variable values, as observed in the fasting  is an extension of local sensitivity analysis to determine the extent of change in metabolic flux or other systemic properties achieved by a fractional change in enzyme activity. MCA is quantified by control coefficients, which is termed as the flux control coefficient if the change in flux is considered as the model variable, or as the concentration control coefficient if the change in concentration is considered as the model variable. Relevant to this article and NAFLD-associated triglyceride accumulation, we can define the concentration control coefficient as the partial derivative of the change in triglyceride metabolite concentration with respect to small changes in the distribution of fluxes at various pathway branch-points. Thus, the sensitivity/concentration control coefficient is calculated by:S1 FigureSchematic representation of object classes in SysBio library. a) Metabolite substrate source class, b) metabolite class, c) enzyme class, d) non-enzymatic regulatory protein class, e) mRNA class, f) mRNA translation class, g) enzyme-catalysed reaction class, h) post-translational protein activation class, i) post-translational protein inhibition class, j) transcriptional activation class, k) transcriptional inhibition class. A-active protein port, C- Transcriptional regulator port, E-enzyme port, I (in f)-input port, I (in h and i)- Inactive protein port, O-output port, P-product port, S- substrate port.(EPS)Click here for additional data file.S1 TablePathway branch-points with moderate (DOCX)Click here for additional data file.S2 TablePathway branch-points with low (DOCX)Click here for additional data file.S3 TableList of metabolites in SteatoNet and the metabolic pathways they are associated with.(DOCX)Click here for additional data file.S4 TableList of enzymes in SteatoNet and the metabolic pathways they are associated with.(DOCX)Click here for additional data file.S5 TableList of regulatory proteins in SteatoNet and the metabolic pathways they are associated with.(DOCX)Click here for additional data file.S6 TableList of transcriptional regulators in SteatoNet, their target genes and the type of regulatory interaction.(DOCX)Click here for additional data file.S7 TableList of post-translational regulators in SteatoNet, their target proteins and the type of regulatory interaction.(DOCX)Click here for additional data file.S1 DatasetSteatoNet model file.(RAR)Click here for additional data file.S2 DatasetSystems Biology library utilized to construct the SteatoNet.(MO)Click here for additional data file.S1 TextInstructions for accessing SteatoNet on Open Modelica Connnection Editor OMEdit.(DOCX)Click here for additional data file.


Carboxydothermus hydrogenoformans is a carboxydotrophic hydrogenogenic bacterium species that produces hydrogen molecule by utilizing carbon monoxide (CO) or pyruvate as a carbon source. To investigate the underlying biochemical mechanism of hydrogen production, an elementary mode analysis of acetyl-CoA pathway was performed to determine the intermediate fluxes by combining linear programming (LP) method available in CellNetAnalyzer software. We hypothesized that addition of enzymes necessary for carbon monoxide fixation and pyruvate dissimilation would enhance the theoretical yield of hydrogen. An in silico gene knockout of pyk, pykC, and mdh genes of modeled acetyl-CoA pathway allows the maximum theoretical hydrogen yield of 47.62 mmol/gCDW/h for 1 mole of carbon monoxide (CO) uptake. The obtained hydrogen yield is comparatively two times greater than the previous experimental data. Therefore, it could be concluded that this elementary flux mode analysis is a crucial way to achieve efficient hydrogen production through acetyl-CoA pathway and act as a model for strain improvement. Carboxydothermus hydrogenoformans Z-2901 is one of the most promising and potential acetogenic hydrogenogenic bacterium produces species which produces hydrogen by utilizing CO and pyruvate as a carbon source , where vp is flux rate of product and vs is flux rate of substrate. The increase of product yield can be achieved by maximizing the flux vp, constrained by the flux vs of the substrate. The flux balance analysis is described in a mathematical formulation by the following equation:Sij is the stoichiometric coefficient of the ith metabolite in the jth reaction and Vj is the flux of the jth reaction. Mi is the internal metabolites. The flux distributions of the metabolic network model in steady state expressed by  are the minimal sets of reactions catalyzed by enzymes that allow the network to perform at steady state , 37. Thesynthase . The inf ppdK, pepC, mhpF, acsA, and CooSV catalyze reactions with pyruvate and/or CO as carbon sources and they participated in all the eighteen elementary modes (EMs) in acetyl-CoA pathway. The genes aceA and acsB furthest down the pathway participate in only thirteen EMs. Next to this, genes such as fdoG, fwd, and fmdE were involved in only ten EMs and genes maeA, pykA, oadA, hydD, pta, ackA, and acdA participated in less than ten EMs. These findings suggested that the blocking of those gene sets or the reactions does not affect carbon flow during acetyl-CoA pathway. The genes acs and pyk are involved in many modes, which are predicted to have major role for maintaining the steady-state flux throughout the pathway.The occurrence of genes involved in hydrogen production during acetyl-CoA pathway was studied through elementary mode analysis. Malate is one of the important metabolites formed from pyruvate during CO metabolism. Oxaloacetate and acetate are other important metabolites formed by the decarboxylation of pyruvate . Thus, i C. hydrogenoformans for the purpose of augmentation of hydrogen production. By examining the elementary modes (EMs) of acetyl-CoA pathway, the key reaction steps for efficient hydrogen production have been identified by carrying out flux optimization using METATOOL option available in CellNetAnalyzer software [Elementary flux mode analysis of acetyl-CoA pathway helps to predict the promising gene deletion target necessary for increasing the product rate . This issoftware . As a resoftware , 42. TheBy generating stoichiometric matrix, the metabolite connections between reactions can be studied to identify the feasible steady-state metabolic flow. These EM studies help to investigate the important subnetworks, their gene participation, and their functionalities for obtaining hydrogen and other important metabolites through acetyl-CoA pathway. This study also help in identifying genes, when deleted through gene knockout would increase hydrogen production. For maintaining the robustness and its sensitivity to perturbation such as new reaction inclusion, gene knockouts were completely analyzed to optimize the modeled acetyl-CoA pathway. The sensitivity of hydrogen yield to the flux values of pyruvate, CO utilization, and acetyl-CoA formation was studied in this work. C. hydrogenoformans as well as to identify a set of multiple gene knockout which results in higher product yield. Gene knockouts were carried out by removing the enzymatic reaction corresponding to that gene (having zero constraints) from the stoichiometric matrix. Totally, 28 elementary modes were observed and their fluxes were measured. Among them, only two EMs 9 and 21 gene sets or reaction flow have maintained the maximum possible yield of hydrogen and retain a reasonable yield of of by-products such as acetate, malate, and oxaloacetate while the other possible number of elementary modes with minimal product fluxes was eliminated.The gene deletion or knockout studies could have either positive or negative effects on phenotypes of organisms. Essential genes and reactions in the EFM pathways could not be deleted . For the pyk, pykA, and mdh were knocked out during flux optimization of EM 21 ( CooS gene and decarboxylation of pyruvate by pyk gene [glcB), malate dehydrogenase (mdh), and pyruvate kinase (pyk) genes were knocked out  enzyme and formate are subsequently metabolized into hydrogen and carbon dioxide. In another case, CO can be directly fixed and converted into CO2 and hydrogen through water gas shift reaction and this reaction is being catalyzed by CO dehydrogenase enzyme complex [ C. hydrogenoformans. The gene knockouts of selected genes ( pepc and pyc genes encoding phosphoenolpyruvate (PEP) carboxylase and pyruvate kinase was disrupted to increase the pyruvate concentration in cellular system for acetyl-CoA synthesis [ Escherichia coli described that one mole of glucose has maximum hydrogen yields of approximately 14.9 mmols/mg dry cell mass [ fhl and fnr genes of E. coli, resulted in the yield of 34 mmol of H2/mg of dry cell mass [ E. coli simulation. The elimination of fdh and pyk genes in E. coli strains also results in increase of pyruvate metabolism towards hydrogen [ pyk and mdh genes would increase the flux rate of hydrogen during pyruvate dissimilation of acetyl-CoA pathway in C. hydrogenoformans. Thus, the analysis of the elementary mode based fluxes showed that the hydrogen yield through acetyl-CoA pathway model proposed here was validated with the experimental data. As acetyl-CoA is one of the precursors of hydrogen formation and the yield depends on the rate of acetyl-CoA and other intermediate produced [ pyk and mdh genes involved in both EMs 9 and 21 were obtained zero constraints, since their absence does not affect the carbon flux during acetyl-CoA pathway. This in silico elementary mode analysis and flux analysis of acetyl-CoA model indicated that the reactions available are feasible for the carbon flow from substrates pyruvate and CO to produce maximum amount of hydrogen. After flux optimization, EMs 21 and 9 , pyruvate kinase type-II (pykA), and malate dehydrogenase (mdh) enzymes could increase both the growth rate and yield of hydrogen [ in silico gene knockout and flux analysis model in this paper will help to improve the strain through metabolic engineering for obtaining the enhanced hydrogen production phenotype. complex . Our stred genes  obtainedynthesis , 46. A pell mass . Similarell mass . As a rehydrogen . Here, wproduced , it was  Figures  were prehydrogen , 39, 50.