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.


Bacillus subtilis by quantifying fluxes, transcripts, and metabolites in eight metabolic states enforced by different environmental conditions. We find that most enzymes whose flux switches between on and off states, such as those involved in substrate uptake, exhibit large corresponding transcriptional changes. However, for the majority of enzymes in central metabolism, enzyme concentrations were insufficient to explain the observed fluxes—only for a number of reactions in the tricarboxylic acid cycle were enzyme changes approximately proportional to flux changes. Surprisingly, substrate changes revealed by metabolomics were also insufficient to explain observed fluxes, leaving a large role for allosteric regulation and enzyme modification in the control of metabolic fluxes.One of the key ways in which microbes are thought to regulate their metabolism is by modulating the availability of enzymes through transcriptional regulation. However, the limited success of efforts to manipulate metabolic fluxes by rewiring the transcriptional network has cast doubt on the idea that transcript abundance controls metabolic fluxes. In this study, we investigate control of metabolic flux in the model bacterium  Saccharomyces cerevisiae  concentrations. For instance in the case of irreversible Michaelis–Menten kineticsJ represents the flux, E and S the enzyme and substrate concentrations, and k and v the binding constant and turnover rate, respectively. More generally, for almost any model of enzyme kinetics, we can writewhere M represents the metabolic state of the cell . To analyze the relative flux and enzyme levels between two conditions as a linear problem, we move to log spacewhere h, and that of the metabolic state ρm following the notation of and define the relative contributions of enzyme concentration, ρm directly is impossible without knowing the function f(M), we can directly estimate ρh by quantifying flux and enzyme levels. Such analysis has been performed previously using flux and enzyme measurements in two different conditions  in at least three conditions led to 46 pairwise comparisons where we could examine the relationship between flux changes and enzyme concentration changes. In order to restrict the influence of outliers on the linear fitting, we use the weighted Theil-Sen estimator  were in this pathway: SucC, SucD, Icd, and CitB  and compensates with higher vmax  . As such, a more direct test of control of flux by transcriptional regulation comes from analyzing the split ratio at a particular branch point and relating it to the concentrations of the enzymes catalyzing the reaction on each branch. To consider this quantitatively, we constructed a simple mathematical model to calculate this split ratio as a function of enzyme concentrations for the case when one substrate can be converted by one of two irreversible enzymes, and scanned a large range of values for the enzyme kinetic parameters to determine the relationship between the enzyme concentration ratio and the flux split ratio, again assuming Michaelis–Menten kinetics and ignoring product inhibition. We find that for a wide range of parameters, the change in flux ratio between two conditions will be exactly equal to the change in the enzyme concentration ratio. This relationship only breaks down for extreme cases, such as when one enzyme has orders of magnitude lower affinity (high ic rate) .B. subtilis central carbon metabolism are likely to be transcriptionally controlled. We considered branch points where we could correlate at least five data points and we eliminated the split involving malic enzyme because of the high uncertainty in the estimation of this flux  is ruled out.Because of the mostly linear relationship between flux ratio and enzyme ratio, we can apply the analysis developed in the previous section to determine whether flux splits in his flux . We findWe note that our simple model assumes only one enzyme for each branch of the split, whereas, for instance, citrate synthase occurs as two isozymes. While CitZ is thought to be responsible for the majority of the activity , we neveThe central result from the regulatory analysis above is that most reactions in central carbon metabolism are consistent with a minority contribution of transcriptional regulation to control of flux. To further elucidate the remaining contribution, which may come from allosteric regulation, post-transcriptional modifications, or substrate concentration changes, we quantified metabolite concentrations in all eight conditions using mass spectrometry. If changes in flux are driven by increased substrate concentration, then flux changes should correlate with changes in substrate levels. Furthermore, metabolites may regulate flux in distant reactions through allosteric interactions, and we can also investigate these distant correlations.B. subtilis, similarly to its recently proposed role in E. coli  or cooperativity (λ>1). This formulation is identical to the commonly used S-system approach for modeling metabolic networks (J)−log(E)=λ log(S). The remaining analysis paralleled the earlier analysis of the flux–enzyme relationship and we introduce the corresponding coefficient ρes to represent the combined contribution of enzyme and substrate concentrations to flux.To determine whether the observed metabolite changes could explain flux changes through modulation of substrate concentrations, we derived a simplified model for the quantitative relationship between the two. In contrast to the relationship between flux and enzyme concentration, which is linear under virtually any model of enzyme kinetics, the relationship between substrate concentration and flux is generally non-linear. As such, we allowed for a free parameter to represent the contribution of substrate concentration to flux, hypothesizing a relationship of the form Δnetworks . The opth=0.70, R2=0.41), but with mild outliers corresponding to the glucose and fructose conditions  as growth on glucose or fructose led to relatively high citrate concentrations. However, very surprisingly, for most reactions the effect of including substrate concentrations was virtually negligible, as demonstrated by ρes values far below one , likely due to highly growth rate-dependent σA-based transcriptional regulation (R>0.8 and none with ρh between 0.8 and 1.2). This is consistent with the results of a detailed analysis of a constitutively expressed protein in E. coli  despite 20-fold or more variation in fluxes.In this study, we provide evidence that most central metabolic enzymes are available in excess, meaning that changes in central metabolic fluxes are not primarily realized through transcriptional regulation by modification of enzyme concentration. This evidence comes from the fact that fluxes and enzyme concentrations (inferred from transcripts) in central carbon metabolism do not change proportionally across conditions. While previous analysis commonly showed a mismatch between flux and enzyme changes when considering two conditions at once , by consThis finding is somewhat surprising in light of the fact that cells clearly face constraints on the amount of protein they can produce , and alsE. coli and S. cerevisiae  between our conditions. The most obvious reactions falling into this category are those involved in substrate uptake. Almost all of these genes exhibited large (over 10-fold) changes in inferred protein concentration between conditions where they were required and others where they were not. Similar effects were seen for two enzymes, GapB and PckA, that were required under gluconeogenic but not under glycolytic conditions. However, among the three gluconeogenic conditions, there was virtually no change in enzyme expression despite significant changes in flux, suggesting that when these enzymes are necessary they are present in excess.With the exception of the two examples discussed above, even large changes in flux through enzymes in central metabolism were not caused by the corresponding changes in enzyme concentration. If central metabolic enzymes are generally in excess as we suggest, then the next most intuitive picture is that flux is regulated far upstream, such as at the level of uptake, and propagates through mass action through the available enzymatic reactions. In this scenario, substrate concentration changes would be the primary drivers of flux changes. Surprisingly, we find that including the effects of substrate concentration changes (based on a simplified kinetic model) is still, in general, insufficient to explain the changes in metabolic fluxes across conditions. The combination of substrate and enzyme changes was able to perfectly explain only a handful of fluxes in central metabolism, most notably the fluxes through the reversible glycolytic enzymes under gluconeogenic conditions, while the remainder of the reactions mostly showed flux changes that were much larger than the combination of enzyme and substrate changes.B. subtilis phosphoproteome showed that glycolytic enzymes are significantly enriched for in vivo phosphorylation  of these interactions have been characterized . It is lWe have introduced a framework for the analysis of transcriptomic, metabolomic, and fluxomic data sets to deduce the regulation likely to be responsible for the modification of cellular metabolism. Such data sets are becoming more commonly available due to advances in experimental techniques, but methods to integrate different types of data are lacking. By using an extended version of regulatory analysis, we are able to state with high confidence that most reactions in central metabolism are not controlled by transcriptional regulation in response to significant environmental perturbations. Future advances in understanding the regulation of metabolic fluxes are likely to come from more careful examination of the relationship between fluxes and metabolite levels, aided by metabolomics methods that can concurrently quantify large numbers of different compounds , as wellB. subtilis BSB168, a prototrophic derivative of B. subtilis 168 trpC2 : 1 ml 0.1 M CaCl2, 1 ml 1 M MgSO4, 1 ml 50 mM FeCl3 and 10 ml trace salts solution. The trace salts solution contained (per liter): 170 mg ZnCl2, 100 mg MnCl2·4H2O, 60.0 mg CoCl2·6H2O, 60.0 mg Na2MoO4·2H2O and 43.0 mg CuCl2·2H2O. Filter-sterilized carbon sources were added separately to the medium, pH neutralized with 4 M NaOH where necessary. For 13C-labeling experiments, the same final concentrations were used, but the carbon source was added directly to the shake flask as a mixture of 20% (w/w) uniformly labeled carbon source (>98% isotopic purity) and 80% (w/w) naturally labeled carbon source.The M9 minimal medium consisted of the following components (per liter): 8.5 g Na600  in combination with an Aminex HPX-87H polymer column . Sugars were detected with a refractive index detector and organic acids with an UV/Vis detector. Substrate or product yields were calculated by linear regression of external concentration against biomass, and specific rates were calculated as yield multiplied by the growth rate. At least five time points during the exponential growth phase were used for the regression analysis. Cell growth was monitored photometrically at 600 nm and cell dry weight was inferred from a predetermined conversion factor of 0.48 g cells/OD600 . All meaB. subtilis were previously established  (Sample collection and RNA purification were performed as previously described . Three rL009126) . SignalsL009126) . For eac600 between 0.8 and 1.2. In all, 2 ml of culture was vacuum filtered on a 0.45-μm pore size nitrocellulose filter (Millipore) and immediately washed with two volumes of fresh M9 medium containing the respective carbon source and adjusted to the pH of the culture at the time of sampling. Sampling was performed in a room kept at 37°C. After washing, the filter was directly transferred for extraction into 4 ml of 60% (v/v) ethanol/water and kept at 78°C for 2 min. The metabolite extract was separated from cell debris and nitrocellulose by centrifugation at 14 000 g at 4°C for 10 min. The supernatants were dried at 0.12 mbar to complete dryness in a speed vac set-up . Dry metabolite extracts were stored at −80°C until analysis.Two samples for intracellular metabolite quantification were taken within 5 min of each other from the shake flask cultures during exponential growth at an ODMetabolite concentrations were determined by using an ion-pairing ultrahigh performance liquid chromatography-tandem mass spectrometry method . Dry metxi,xj),} and the estimated slope is the weighted median of the pairwise slopes, using weights proportional to the distance (xi−xj)2. To estimate ρh while obtaining a confidence interval and P-value, we obtain a distribution of slopes by performing the fitting multiple times (N=2000), each time sampling a random subset of the data and concurrently perturbing the data by adding Gaussian noise with standard deviation given by the estimated measurement errors. The reported ρh is the median of this distribution, and the 95% confidence interval and the P-value for ρh>1 are obtained directly from the distribution. Additional analysis of ρh values under random permutation of conditions is shown in To calculate the best linear fit while allowing for outliers, we use the weighted Theil-Sen estimator . We calcSupplementary Figures S1–5


Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of  Flux balance analysis (FBA), a constraint-based modeling approach, is widely used in predicting metabolic fluxes based on stoichiometric metabolic models, in particular, large-scale or genome-scale metabolic models  and nucleotide triphosphates (NTPs) according to the fraction of DNA and RNA , TCA cycle, and mitochondrial electron transport chain (ETC)]. This is because these existing large-scale metabolic networks of plants provided mostly qualitative predictions of intracellular fluxes for primarily central carbon metabolism. Furthermore, previous work has shown that fluxes of central carbon metabolism dominate the FBA results, with little to no flux through the secondary metabolic pathways  were taken into account in the three models via representative metabolites or corresponding precursors, the biomass components included in these three models are not exactly the same. For example, xylose and γ-aminobutyric acid (GABA) were not considered in Poolman model, but were included in AraGEM and AraCore models. Similarly, soluble metabolites were only considered in AraCore model, but not in Poolman and AraGEM models. To keep the list of biomass components as consistent as possible between the three models, we added some additional biomass transporters to the models if required. It is noted that not all newly added biomass components can be produced by all models, e.g., maltose, xylose, and GABA cannot be produced in Poolman model, coniferyl-alcohol, coumaryl-alchol, sinapyl-alcohol, and xylose cannot be produced by AraCore model as these metabolites are not present in AraCore. Therefore, in this study, we only added new biomass components that can be produced by all the three Arabidopsis models. In total, we added nine new biomass transporters to Poolman model and seven for AraGEM , the biomass compositions were normalized to enable a fair comparison. These calculations were performed by weight, defining 1 unit of flux through the biomass equation equals to 1 g of biomass. The details of these calculations are provided in Supplementary Data http://www.gurobi.com, version 5.0.2) solver in combination with the COBRA toolbox were used to solve the linear programming problems. In our study, the biomass equation was maximized to obtain the optimal solution of the metabolic model as described elsewhere  and hydrogen sulfide (H2S) were constrained to be utilized as the sole N and S sources, respectively, because AraGEM and AraCore can only grow with H2S and NH3 as the S and N sources, respectively. Since SO2S transporter. We therefore added an H2S transporter to Poolman model, namely “H2S_tx”, enabling H2S as sole sulfur source in all models. Pi is the sole P source input in all three models. Additionally, non-growth associated maintenance (NGAM) was included in all simulations with a value of 2.02, which is a normalized flux unit referring to the value reported in Poolman model  is generated by defining all of the biomass constituents, in which all the precursor metabolites are assembled in one single reaction with corresponding coefficients  was conducted for the nine model-biomass combinations, which determines the range of possible solutions for each reaction while giving rise to the same optimal value for the objective function  in Poolman model operates in the opposite direction compared to other two models, which was corrected in a subsequent publication . In particular, we analyzed how central metabolic reactions listed in Supplementary Table +-dependent glyceraldehyde-3-phosphate dehydrogenase , a reaction belonging to the TCA cycle, whereas this reaction did not carry flux with PoolmanBOF and AraCoreBOF. The flux distributions through the TCA cycle reflect the function of the metabolic network, and its operation largely depends on the cell type and the considered physiological context , a measure of the linear dependence between two variables, for all predicted growth rates simulated by decreasing and increasing of each single biomass component by 30%  no maintenance, (ii) sole GAM, (iii) sole NGAM, and (iv) GAM and NGAM. We then compared the growth rates of scenario (i), (ii), and (iii) with that of scenario (iv), separately. The analyses showed that the influence of maintenance parameters varies with the used model. The exclusion of GAM and/or NGAM changes the growth rate by, at most, 29.6 and 1.3% in AraGEM model and AraCore model, respectively  were tested to cover multiple possibilities for biomass composition. Our results demonstrated that flux predictions of the central metabolic network are fairly insensitive to changes in biomass composition, regardless of the employed metabolic networks Figure . BiomassE. coli model that maintenance cost had larger impact on growth rate prediction (Feist et al., Our results also showed that 30% variations in individual biomass precursor had a minor effect on the growth rates with Arabidopsis models (Supplementary Tables E. coli model of Feist et al. (E. coli model of Varma and Palsson (Sensitivity analyses of biomass composition performed on different models often yields conflicting results. According to the sensitivity analysis on the t et al. , small c Palsson  noted th Palsson  found th Palsson . Moreove Palsson . These oAlthough, our analysis indicated the robustness of the growth rate and central metabolic fluxes in response to changes in biomass composition and maintenance cost, it does not suggest that it is sufficient to generalize biomass composition from any cell type within a plant or from other closely related organisms. This is because the biomass composition for different tissues and organisms may include metabolites specific to them, which need specific determination. Notably, variables other than model structure, such as the P/O ratio have considerable impacts on flux distributions according to previous reports (Feist et al., In this study, we reviewed the characteristics of published Arabidopsis metabolic models, concentrated on three particular models with distinct model structures and different compositions of biomass components. We examined the sensitivity of predicted fluxes to biomass composition, which is particularly relevant to plant metabolic models for which the biomass data is often collected from various sources. Our analysis showed that central metabolic fluxes as well as growth rates were insensitive to the variations in biomass composition, but were significantly affected by model structure. This work represents a thorough set of analyses performed in plants by means of constraint-based modeling, thereby providing relevant information about how critical FBA solutions can be affected by biomass composition, and more importantly by the structure of the models. Despite differences in several aspects such as model structure and number of metabolites and reactions included, each of the evaluated models has its own merits. Comparative analysis of the models paves the way for exploring the existence of principles that are relevant for the regulation and robustness of plant central carbon metabolism.HY, CC, PH, and NV conceived and designed the work. HY carried out all simulations and drafted the manuscript. CC, PH, and NV revised the manuscript. All authors read and approved the final version 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.


The morphology, crystal phase, and photoelectrochemical performance of the flower-like CdS and pure CdS nanocrystals are carefully investigated via various characterizations. Superior photocatalytic activity relative to that of pure CdS is observed on the flower-like CdS photocatalyst under visible light irradiation, which is nearly 13 times of pure CdS. On the basis of the results from SEM studies and our analysis, a growth mechanism of flower-like CdS is proposed by capturing the shape evolution. The imidazole ring of L-Histidine captures the Cd ions from the solution, and prevents the growth of the CdS nanoparticles. Furthermore, the photocatalytic contrast experiments illustrate that the as-synthesized flower-like CdS with L-Histidine is more stable than CdS without L-Histidine in the hydrogen generation.Morphology-controlled synthesis of CdS can significantly enhance the efficiency of its photocatalytic hydrogen production. In this study, a novel three-dimensional (3D) flower-like CdS is synthesized via a facile template-free hydrothermal process using Cd(NO Recently, morphology-controlled synthesis of semiconductor nano-/micro-crystals has attracted essential interests because it enables the development of the addition of flexibility to existing systems in many areas, such as catalysis, optics, magnetism, biology, and so onet al. has reported a facile L-cysteine-assisted method for synthesis of CdS in spherical nanostructuresControlling the shape of nanocrystalline materials is a crucial issue in the exploitation of novel properties in nanoscience research. CdS is one of the II–VI group semiconductor materials with a direct band gap of 2.4 eVXRD analysis is used to investigate the phase structure and crystalline size of the products. The morphology of the as-synthesized 3D CdS nanoarchitectures was examined by SEM and TEM. 2+ and thiourea might form Cd-thiourea complex because of the strong coordinating capacity of the thiourea. With increasing temperature during the hydrothermal process, the Cd-thiourea complex may tend to decompose to release active Cd2+ and S2–. Then the active Cd2+ reacts with active S2– to form CdS seeds. L-Histidine plays a key role in the formation of flower-like CdS. The nitrogen atom of the imidazol ring promotes its solubility in aqueous solution and is also an important element to produce hydrogen-bond interaction between particlesOn the basis of the SEM observation and the experimental process, a possible mechanism is proposed to explain the formation of flower-like CdS, as depicted in 172-production activity.2g–1, which is 1.7 times larger than that of pure CdS. Generally, the photocatalyst with a higher specific surface area is indispensable in the enhancement of photocatalytic performance, due to more surface active sites and photocatalytic reaction centersThe specific surface area and porosity of the samples CdS prepared without L-Histidine and with L-Histidine were studied by nitrogen sorption measurements. The BET specific surface area of the CdS prepared with L-Histidine is about 3.32 m2, whereas the value for the CdS with L-Histidine is 2 times higher than that of CdS without L-Histidine. This can be attributed to the fact that the photoinduced charge carriers of CdS with L-Histidine are separated more efficiently than in CdS without L-Histidine29The photoelectrochemical performance of semiconductors mainly depends on the generation of photoinduced electron, separation of electron–hole pairs and efficiency of charge carrier transferPhotoluminescence (PL) analysis is also employed to investigate the migration, transfer and separation efficiencies of photo-generated electrons and holes in semiconductors, since PL emission of semiconductor mainly arises from the charge carrier recombination313233342S and 0.5 M Na2SO3 as the sacrificial reagents. Expectedly, for CdS with L-Histidine, the photocatalytic H2-production rate is markedly enhanced to 376.7 μmol/h, nearly 13 times of pure CdS (29.2 μmol/h). Because CdS with L-Histidine has a higher specific surface area and possesses more surface active sites and photocatalytic reaction centers, it exhibits a better photocatalytic performance to produce H2. Meanwhile, the photoelectrochemical and PL spectra validate that photo-generated electron-hole pairs of CdS with L-Histidine can be separated more efficiently than pure CdS. These behaviors are beneficial for photocatalytic performance to produce H2 and are the mainly reason for higher photocatalytic activity of flower-like CdS, while the detailed mechanism study still needs further investigations. Additionally, platinum (Pt), as an excellent co-catalyst, is loaded onto the CdS samples via a photodeposition method. The experimental result  shows that the photocatalytic H2-production rate of dendritic CdS decorated with 0.3% Pt is 53.1 μmol/h, while the flower-like CdS is 731.1 μmol/h, having a high photocatalytic activity for H2-production. In order to check the reproducibility of photocatalytic material and remove the dissolved gases, the reactor was evacuated and the photocatalytic experiment was repeated in every 5 h of reaction  in L-Histidine can draw the photogenerated holes of CdS, so the introduction of L-Histidine into the system can remove holes efficiently and avoid the photocorrosion problem of CdS. The slight decrease in the rate of hydrogen evolution might be related to the deactivation of the photocatalyst or attributed to the consumption of the sacrificial reagents in the solutionreaction . The act2-evolution rate is 376.7 μmol/h under visible light irradiation, which is 13 times of that of pure CdS.In summary, we have successfully synthesized the novel three-dimensional (3D) flower-like CdS nanoarchitectures via a facile template-free hydrothermal process using L-Histidine as a chelating agent. The pure CdS and flower-like CdS samples present regular hexagonal and cubic phases structure. Based on the SEM studies, we propose the growth mechanism of flower-like CdS nanoarchitectures. The imidazole ring of L-Histidine captures the Cd ions from the solution, and prevents the growth of the CdS nanoparticles. The as-synthesized flower-like CdS sample slightly extends the photoresponse to visible light region and exhibits an excellent photoelectrochemical performance. The photocurrent of flower-like CdS is 2 times of pure CdS. Enhancement in photocurrent displayed by flower-like CdS is attributed to the reduction in the electron-hole pairs recombination process. The above factors are assumed to be the main origins that lead to the enhanced photocatalytic activity of the flower-like CdS sample in hydrogen generation under visible light illumination. The flower-like CdS photocatalytic H3)2.4H2O, 99.0%), Thiourea , L-Histidine (C6H9N3O2), Dehydrated Alcohol , Sodium sulfate anhydrous , Sodium sulfide , Sodium sulfite anhydrous  were purchased from Sinopharm chemical Reagent Co., Ltd. All the chemicals were used as received without further purification. Deionized (DI) water was used in all experiments.Cadium nitrate tetrahydrate (Cd(NO3)2•4H2O, and 3 mmol of thiourea were dispersed in 50 mL distilled water. Next, the homogeneous solution was transferred into a 100 mL Teflon-lined autoclave and held at 180 oC for 4 h after vigorous stirring and sonication. After that, the precipitates were collected by centrifugation and washed with distilled water and ethanol several times. The final product was dried in an oven at 80 oC for 12 h.In a typical procedure, 1 mmol of Cd(NO3)2•4H2O, 3 mmol of thiourea, and 0.3 mmol of L-Histidine were dispersed in 50 mL distilled water. Next, the homogeneous solution was transferred into a 100 mL Teflon-lined autoclave and held at 180 oC for 4 h after vigorous stirring and sonication. After that, the precipitates were collected by centrifugation and washed with distilled water and ethanol several times. The final product was dried in an oven at 80 oC for 12 h.In a typical procedure, 1 mmol of Cd. Scanning electron microscopy (SEM) images were obtained using Carl Zeiss Ultra Plus. Transmission electron microscope (TEM) study was carried out on a TECNAI TF20 instrument. UV–vis absorption spectra were measured using Shimadzu UV-3100 spectrophotometer. Nitrogen adsorption-desorption isotherms and the Brunauer-Emmett-Teller (BET) surface areas were collected at 77 K on a TriStar II 3020 system. Steady and time-resolved fluorescence emission spectra were recorded at room temperature with a fluorescence spectrophotometer .X-ray diffraction patterns (XRD) of the samples prepared were recorded on a Rigaku X-ray diffractometer D/MAX-2200/PC equipped with Cu K2 aqueous solution) to cut off the light in the UV region. The fabrication of the working electrodes refers to the reported literature2) and then dried slowly in room temperature until a layer of film formed on the surface. The electrolyte solution used for all measurements was 0.5 M Na2SO4. Pt electrode and Ag/AgCl electrode were used as the counter and reference electrodes, respectively. The photocurrent responses of the working electrodes were recorded by sudden light on and off under visible light illumination at the bias voltage of 0.5 V.The photocurrent of the photocatalysts were measured by electrochemical works station , using a 300 W Xe lamp which was equipped with an optical filter  which was equipped with an optical filter (0.1 M NaNO2 aqueous solution) to cut off the light in the UV region. The amounts of H2 evolution were measured by using a gas chromatography  with thermal conductivity detector(TCD) and Ar as carrier gas.The photocatalytic reactions were carried out in a Pyrex reaction cell. 0.15 g powder was dispersed in 100 mL aqueous solution containing 0.5 M NaHow to cite this article: Wang, Q. et al. Highly Efficient Photocatalytic Hydrogen Production of Flower-like Cadmium Sulfide Decorated by Histidine. Sci. Rep.5, 13593; doi: 10.1038/srep13593 (2015).