High-throughput experiments can bring to light associations between genes, proteins and/or metabolites, many of which will be explainable by existing knowledge. Our aim is to speed elucidation of such explanations and, in some cases, find explanations that scientists might otherwise overlook.Escherichia coli literature.We describe the MultiOmics Explainer, a new tool within the Pathway Tools software suite that leverages what is known about an organism’s metabolic and regulatory network to suggest explanations for the results of omics experiments. Querying a database such as EcoCyc, the MultiOmics Explainer searches the organism’s network of metabolic reactions, transporters, cofactors, enzyme substrate-level activation and inhibition relationships, and transcriptional and translational regulation relationships to identify paths of influence among input genes, proteins and metabolites. Results are presented in a combined metabolic and regulatory diagram. We present several examples of explanations generated for associations found in the The MultiOmics Explainer is a valuable tool that helps researchers understand and interpret the results of their omics experiments in the context of what is known about an organism’s metabolic and regulatory network. It showcases the rich set of computational inferences that can be drawn from a database such as EcoCyc that encodes a diverse range of biological interactions.The online version of this article (10.1186/s12859-019-2971-6) contains supplementary material, which is available to authorized users. Escherichia coli, many of these associations will be explainable by existing knowledge. However, finding such explanations can be time consuming for scientists, because, in some cases, long chains of interactions connect causes with their effects. Our aim is to speed elucidation of such explanations, and, in some cases, find explanations that scientists would otherwise overlook, to aid researchers in differentiating which effects can and cannot be explained by existing knowledge.High-throughput experiments can bring to light associations between genes, proteins and/or metabolites whose relationship is not immediately obvious to researchers. Some of these associations reveal new functions or pathways; but in a well-studied organism, such as Here we describe the MultiOmics Explainer, a new tool within the Pathway Tools software suite that leverages what is known about an organism’s metabolic and regulatory network to suggest explanations for the results of omics experiments. The MultiOmics Explainer is unique in that it synthesizes the wide range of knowledge contained within a Pathway/Genome Database (PGDB), including the organism’s metabolic network, transporters, cofactors, enzyme substrate-level activation and inhibition, and transcriptional and translational regulation. The MultiOmics Explainer is also unique in that its inputs can come from multiple types of omics experiments, including transcriptomics, proteomics, metabolomics, and combinations thereof. It should be noted that this tool does not attempt to infer any previously unknown regulatory or other relationships—it uses strictly what is already known and encoded in the PGDB. Thus, the failure of the tool to find a plausible explanatory relationship in any particular case is a likely indication of gaps in the metabolic or regulatory network, and suggests areas for new research or curation.E. coli sensor histidine kinase gene kdpD resulted in an increase in the level of 2-oxoglutarate in the cell , but itE. coli K–12 that has been curated extensively from the biological literature, integrating information from more than 37,000 publications. Because EcoCyc is the most complete and most accurate PGDB in the BioCyc collection [Pathway Tools  is a sofllection , with thllection  or RegTrllection .directed mode, the user specifies two sets of target entities, the conditions and effects, where an entity is any gene, protein, or metabolite of interest. The conditions are a set of one or more entities whose abundance or activity has been changed as part of the conditions of the experiment . The effects are a set of one or more entities whose abundance or activity has significantly changed as a result of the experiment. For every possible condition-effect pair, the tool will attempt to find the lowest cost paths through the combined metabolic and regulatory network that link the condition to the effect.The MultiOmics Explainer can operate in one of two modes. In undirected mode, the user specifies a single set of target entities, the effects set. The tool will attempt to find paths that link the effects together by identifying one or a small number of entities, influencers, that influence multiple targets. These influencers may or may not themselves be members of the effects set.In We treat both modes as network-search problems, using a weighted breadth-first search approach with a maximum cutoff to explore the combined metabolic and regulatory space. The weighting of a connection depends both on the connection type  and specificity .An additional challenge is to present the results of this analysis in a form that is easily comprehensible to biologists. This task is complicated by the diversity of relationship types that need to be communicated to the user. Our tool presents its results as a combined metabolic and regulatory network diagram, with mouseovers that more fully describe the various relationships. If, due to a large number of interconnections, this generated diagram becomes difficult to understand, the user can interactively select subsets of the original entities to examine different subsets of relationships, a few at a time.E. coli omics studies.Finally, we present some examples of the use of the MultiOmics Explainer to explain the results obtained from previously published Most prior work focuses on analyzing either gene/protein data and networks or metabolite data and networks, but not both together.Typical analysis of transcriptomics data involves statistical methods, such as enrichment analysis, sometimes combined with topological pathway data, to identify pathways or Gene Ontology terms that show significant differential expression (reviewed in ). CliPPEOn the metabolomics side, Acuna et al.  have devOur work differs from all the above techniques in that it considers multiomics data and draws on a much richer set of biological relationships, combining the metabolic network with multiple different types of regulation.The MultiOmics Explainer algorithm is divided into three phases, which can be roughly categorized as graph construction, graph search, and network visualization . Figure E. coli genes as effect entities and 1 condition entity, which required approximately 7 minutes to complete on a Linux desktop machine. Of the examples described in the “E. coli metabolites as effect entities and 1 gene condition entity required 45 min to complete.The input to the MultiOmics Explainer is either one or two target sets of genes, proteins, and/or metabolites of interest. In undirected mode, a single set of entities is specified—these are the effects observed in the experiment. In directed mode, the user must specify both a set of condition entities and a set of effect entities. The total number of supplied entities should be kept fairly small, both for the analysis to complete in a reasonable amount of time and for the resulting diagram to be comprehensible. The target entities could be those exhibiting the largest or most significant change over the course of an experiment, or they could be hand-selected by a researcher who wants to understand their particular relationships. Note that it is possible to run the Phase 1 analysis on a larger set of entities, and then interactively select smaller subsets for Phases 2 and 3 to view in the diagram, particularly if the effect entities are genes  or little-used metabolites. The largest input set on which we have tested the MultiOmics Explainer consisted of 88 A increases and B decreases and A directly activates B, then the increase in A does not explain the decrease in B). Information about direction of change of an entity may also be used during Phase 2, to reduce the number of included paths.For each entity (both conditions and effects), the user can optionally specify whether its abundance or activity is increased or decreased. If provided, this information will be used to color the resulting display, and to determine whether a given path is consistent with the observations or not , and edges correspond to influence types  that are computed from information stored in the PGDB. Note, however, that no direct one-to-one mapping exists between the objects and attributes in the PGDB and the nodes and edges in the MetReg graph. Although they are represented using different objects in the PGDB, for the purposes of this algorithm we consider a gene, its product, and any homomultimeric complexes of the product to be a single logical entity, so they are represented by a single node . A single edge in the MetReg graph can combine information across multiple PGDB objects and attributes. For example, we create a single edge linking a metabolite to the enzyme that catalyzes its production, but to generate that edge we must draw information from attributes connecting the metabolite to a reaction object, the reaction to its catalysis object, and the catalysis object to the protein, as well as the directional information stored for the reaction.The first phase of the algorithm is to build a weighted MetReg graph The types of influences on an entity that we consider, and which map to edge types in the MetReg graph, are presented in Table Q to choose the next node to process. Each node in the graph keeps track of its lowest-cost path from each root node.We build the MetReg graph in breadth-first order, maintaining a priority queue N has a cost CN, the cost of the minimum-weight path from N to any root, and one or more path costs PCN,R, the cost of the minimum-weight path from N to root node R. Each edge N→N′ has an edge cost R in G1 with path cost PCR,R=0, and add it to Q with cost CR=0.For each effect entity, create a corresponding root node N from Q.Remove the lowest-cost node N′ of N . All nodes and edges on the selected paths are copied to G2. Figure In the second phase, we create a new filtered graph N in G1 that has paths to two or more target entities, we compute its influence score as the sum of the minimum path length to each target, plus a penalty of 1.5 times the maximum depth limit for every target that N is not connected to. The penalty ensures that, all else being equal, entities that influence a greater number of target entities will have lower influence scores (where a lower score means greater influence) than entities that influence fewer target entities. We then sort the nodes in order of increasing influence score, and generate a simple covering set of influencers, via the following algorithm: examine the influencer nodes in order of increasing score. If an influencer node adds one or more targets to the list of covered targets, add the influencer node to the covering set, and its list of influenced targets to the list of covered targets. Continue until all targets are included in the covered target set, or until no influencer nodes remain. Note that this is not a minimal covering set—we consider it more useful to include the better-scoring influencers, even if that introduces some redundant coverage. The covering set may or may not include members of the target set.In undirected mode, in addition to looking for causal connections between one entity and another, we are also interested in finding PGDB entities not in the original target set that can influence multiple target entities, and so provide an explanation for why they are correlated. For each node G2 as for directed mode.Once we have determined the covering set of influencers, we have reduced the undirected-mode problem to the directed-mode problem, positing the covering set influencers as conditions, and can generate the filtered graph Once the filtered MetReg graph has been generated, we use it to generate a single diagram that combines and explains all mechanistic influences found by the algorithm. The diagram should be easy to read and intuitive for biologists to follow, which is a challenge, given the mixture of different types of interactions . There is not necessarily a one-to-one correspondence between the set of nodes in the filtered graph and the set of entities visible in the diagram. For example, a particular path of influence may include the impact of an enzyme on a reaction product but not the reactants of the reaction, whereas in order for the figure to be comprehensible to biologists, we will want to show the reaction as a link from reactants to products, and then show the enzyme associated with that reaction, drawn as if it were part of a typical metabolic pathway.G2 and the nodes in the diagram, so each of these sets are copied to G3 and laid out as a tree . Finally, we lay out all components relative to each other to produce the final diagram. We use different arrowhead styles to indicate different types of influence, and, for non-metabolic interactions, edge colors indicate whether an influence is activating or inhibiting. We also use color to highlight all entities in the supplied entity sets .Our approach is to lay out the diagram hierarchically. We divide the nodes in the filtered graph into two groups: those that are directly associated with metabolic reactions either as reactants, products, or enzymes; and all others. Each group is then further divided into its connected components, and each component is laid out individually in a third graphG3 (the Explanation Graph), the only graph that is visible to the user. For each metabolic connected component, we assemble the set of associated reactions into a temporary pathway, and then lay out the pathway using the automated metabolic pathway layout algorithms that are an integral part of Pathway Tools software. The non-metabolic components will have a one-to-one correspondence between the nodes in G3 generated from the example filtered graph G2 in Fig. G2, they must be included in G3 to show the reactions catalyzed by E1 and E3.Figure The user can mouse over any node or edge in the figure to see more information. Clicking on a node or edge causes the relevant entity to be displayed in the main Pathway Tools display window.G2 and G3 are recreated from scratch, but G1 is unchanged.In the graphical user interface for the MultiOmics Explainer, a control panel to the left of the pane containing the Explanation Graph provides additional information and options. It lists any entities in the target sets for which no connections to any other entities were found. It also provides a checklist of entities for which connections were found. Users can select a subset of the original set and regenerate the display to only show connections between the selected entities. This feature is particularly useful in cases where the size and interconnectedness of the filtered graph makes it impossible to generate a display that makes it easy to visually follow all the connections . Reducing the number of selected entities can result in a clearer diagram. When the display is regenerated with a new entity subset, G2 for a pair, that pair is listed in the control panel. A button will bring up a textual description of such paths to complement the diagram. An example is shown in Fig. In directed mode, if parity information (increase or decrease in abundance or activity) was supplied for both conditions and effects, then we can determine which, if any, of the paths connecting a condition/effect pair are consistent with their respective parities. When one or more consistent paths exist in In undirected mode, the list of all influencers in the top-scoring covering set are listed, as well as the top ten influencers in general. These influencers are presented as a selectable list, as shown in Fig. E. coli literature for example omics datasets that produced a relatively small number of interesting genes and/or metabolites in need of an explanation.To test the utility of the MultiOmics Explainer, we searched the E. coli K–12 single-gene deletions. For every metabolite, they were therefore able to identify a set of genes whose deletion significantly impacts that metabolite. We took some of the examples presented in their paper and attempted to explain them using the MultiOmics Explainer. In these cases, the deleted genes are the conditions, and the metabolite is the effect. Note that their paper speaks of these examples in the most general terms only, so does not propose specific paths that we could compare our results to.Fuhrer et al.  have conThe authors found that most significant changes can be explained by highly local paths within the network, with knocked-out genes most commonly affecting metabolites fewer than three steps away in the metabolic network. It is no surprise that the MultOmics Explainer is able to recapitulate these relationships. However, even at a relatively close metabolic distance, the inclusion of regulatory relationships enables us to detect paths of influence that would typically have required human inspection and inference. An example is the case of dihydroxy-methylbutanoate, shown in Fig. cis-aconitate, by virtue of an iron-sulfur cluster being a required cofactor of the aconitase enzymes AcnA and AcnB. Figure cis-aconitate, which seems counterintuitive given that the ferric enterobactin complex is clearly present in the graph. Further inspection revealed that currently EcoCyc contains no reaction that can convert enterobactin to ferric enterobactin, since this is a spontaneous reaction that occurs outside of the cell. Thus, this creates an inadvertent gap in the metabolic network. The same explanation likely applies to the failure to find a link to AroB, since chorismate is the precursor to the enterobactin biosynthesis pathway.However, the authors also point out cases where the metabolic distance between the genes and affected metabolites is quite large. In one well-understood case, the authors make mention of how several genes related to iron accumulation affect the production of Another case of distal effects cited by the authors is the impact of the aro and pur genes on malate levels. The authors state that there are no known regulatory relationships that can explain these relationships, and their cause is not well understood. Our tool suggests paths through the metabolic network by which these genes are connected to malate  were considered “obvious”, but 15 of them were not obvious and were therefore potentially interesting. Two routes, covering several of these genes, are shown in Fig. Traditional enrichment analysis  indicates that this gene set is over-represented in amino acid biosynthesis genes, particularly for synthesis of arginine and the aromatic amino acids, and, to a lesser extent, cell locomotion genes, but is unable to suggest specific mechanistic links to tryptophan. Any of the pathway-based refinements of traditional methods depend upon the existence of defined signaling pathways involving these genes, which do not exist in EcoCyc. Our tool was highly effective at explaining the link from tryptophan to the amino acid biosynthesis genes, without depending on defined pathways (though of course there is overlap between some of the suggested routes and known metabolic pathways), and the suggested routes typically include both regulatory and metabolic steps, something no other tool can do. Our tool was less successful at identifying plausible routes for the locomotion-related genes. This evaluation highlights the challenge of distinguishing between biologically relevant and non-relevant routes, but also shows the value of the tool both for identifying known routes and for hypothesis generation.E. coli K–12 was incubated with successively higher doses of benzylalkonium chloride (BC), a commonly used disinfectant that kills bacterial cells, to produce three adapted strains. Transcriptional and proteomic analyses were then conducted on the adapted strains, to produce a list of 25 genes/proteins that were significantly differentially expressed in the adapted strains relative to the control strain. The purpose of the experiment was to shed light on the mechanisms by which BC and related compounds disrupt the cell and by which resistance is acquired.In Bore et al. , E. coliBecause the mechanistic action of BC is unknown, no mechanistic link exists between BC and any other entity in EcoCyc . Thus, this study offers a useful example for testing out the MultiOmics Explainer in undirected mode. In this case, the input effects set is the set of 25 genes and proteins identified by the analysis, and the goal is to identify one or a small number of genes that can explain the observed set.Figure The MultiOmics Explainer is a valuable tool to help researchers understand and interpret the results of their omics experiments in the context of what is known about an organisms’s metabolic and regulatory network. In directed mode, the user supplies two sets of entities, those that are changed as conditions of an experiment, and those that change as effects of the experiment, and the tool searches the network to identify paths by which the conditions influence the effects. This mode is most appropriate for experiments whose conditions can be reduced to changes to one or a few genes or metabolites, such as a gene-knockout experiment or a nutrient-substitution experiment. Undirected mode is best suited to experiments that either include conditions that do not map to specific entities or involve entities whose function is unknown (and thus are not part of the existing network). In this case, users specify the set of effect entities, and the tool identifies a set of potential influencers that can explain as much of the input set as possible. In both modes, the tool produces a diagram that summarizes the paths of influence and provides links back to the PGDB for more information.E. coli literature demonstrates that paths of influence often involve multiple types of interactions, combining metabolism; transport; enzyme cofactors and substrate-level regulators; transcriptional and translational regulation; and protein-ligand interactions. The MultiOmics Explainer highlights the value of EcoCyc, not just as a reference but as a basis for computational inference.It must be noted that operation of the MultiOmics Explainer depends upon the rich and extensive set of metabolic and regulatory relationships encoded within the EcoCyc database. Running our software on examples of omics experiments taken from the The MultiOmics Explainer is a component of the Pathway Tools desktop application.Project name: Pathway ToolsProject home page:http://bioinformatics.ai.sri.com/ptools/Operating systems: Macintosh, Windows, and LinuxProgramming language: Common LispOther requirements: Pathway Tools version 22.5 or laterLicense: Freely available to academic and government researchers with signed license agreement. See https://BioCyc.org/download.shtml. Source code available upon request.Any restrictions to use by non-academics: A license fee applies to commercial use. See https://BioCyc.org/download.shtml.Additional file 1Supplementary Material. (PDF 79 kb).


Escherichia coli (E. coli) has shown remarkable capability for the biotransformation of 2,4,6-trinitro-toluene (TNT). Efforts to develop E. coli as an efficient TNT degrading biocatalyst will benefit from holistic flux-level description of interactions between multiple TNT transforming pathways operating in the strain. To gain such an insight, we extended the genome-scale constraint-based model of E. coli to account for a curated version of major TNT transformation pathways known or evidently hypothesized to be active in E. coli in present of TNT. Using constraint-based analysis (CBA) methods, we then performed several series of in silico experiments to elucidate the contribution of these pathways individually or in combination to the E. coli TNT transformation capacity. Results of our analyses were validated by replicating several experimentally observed TNT degradation phenotypes in E. coli cultures. We further used the extended model to explore the influence of process parameters, including aeration regime, TNT concentration, cell density, and carbon source on TNT degradation efficiency. We also conducted an in silico metabolic engineering study to design a series of E. coli mutants capable of degrading TNT at higher yield compared with the wild-type strain. Our study, therefore, extends the application of CBA to bioremediation of nitroaromatics and demonstrates the usefulness of this approach to inform bioremediation research.Microbial remediation of nitroaromatic compounds (NACs) is a promising environmentally friendly and cost-effective approach to the removal of these life-threating agents.  Nitroaromatic compounds (NACs) are major environmental contaminants with high toxicity to living systems and human health ,2,3. IntRecent evolution of microorganisms in the TNT-contaminated soils has led to the emergence of microbes with the ability to not only survive in presence of TNT but also to degrade it and/or metabolize it as a nutrient source. Along with continued discovery and characterization of such “specialist” microorganisms, the microbial remediation shows increasing promise as an environmentally sound and economic viable TNT degradation approach ,6,8,13.E. coli is one of the microorganisms that has shown remarkable TNT biotransformation capability [E. coli possesses multiple enzymes to attack TNT, including nitroreductases capable of reducing the TNT nitro groups and N-ethylmaleimide (NEM) involved in TNT denitration [E. coli is able to derive assimilatory ammonium from TNT, placing it among the limited number of microbes capable of growing on TNT as the sole nitrogen source [E. coli, highlight this organism as a promising biocatalyst for TNT bioremediation processes.The advantages realizable by microbial degradation of TNT have prompted extensive research on different aspects of the issue, including capabilities of degrader microorganisms , enzymespability ,28,29,30n source ,28. In an source ,32. ThesPseudomonas putida KT2440 (in degradation of aromatics) [Dehalococcoides (in detoxifying chloro-organic pollutants) [Geobacter metallireducens  [Rhodococcus erythropolis (in desulfurization) [An efficient bioremediation process requires the degrader microbe(s) to be improved for high yield and productivity. In the post-genomic era, development of microbial biocatalysts relies heavily on genome-wide knowledge of cellular metabolism obtainable from of holistic analysis of omic data ,34,35. Somatics) , Dehaloc metals) , and Rhoization) .E. coli in biotransformation of TNT. In doing so, we extended the most comprehensive high quality GSMM of E. coli, iJO1336 [E. coli.While the need for high-performance NACs biodegradation process persists ,9, the p iJO1336 , to acco iJO1336 ,30,44,45 iJO1336 , includi iJO1336 , robustnE. coli when all pathways are present and the flexibility of flux distribution under different growth conditions will be evaluated. Subsequently the extended model will be used to predict TNT depletion profile in the E. coli batch growth culture. Next we analyse the influence of bioprocess conditions, including aeration regime, TNT concentration, cell density, and carbon source on biotransformation yield/productivity. Ultimately, we introduce a series of in silico designed E. coli mutants potentially more efficient in TNT degradation compared with the wild-type strain. The implication of our study for future research in microbial remediation of NACs will also be discussed.We report our results accompanied by the relevant discussion as the following: firstly a detailed analysis of the efficiency of each individual TNT degradation pathway will be presented. Then, the capacity of E. coli has proven ability to transform TNT to ADNTs and DANTs under both aerobic and anaerobic growth conditions [E. coli grown anaerobically under a hydrogen atmosphere [Reductive transformation to nitroso and hydroxylaminotoluene is the primary step in most known TNT biodegradation pathways ,9. Initinditions ,32,47,48mosphere .E. coli in the light of CBA, we incorporated reactions of nitroreduction pathway (NRP) up to TAT formation  of 250 mg/gdw/h. Optimal aerobic growth as simulated by flux balance analysis (FBA) did not allow flux through TNT biotransformation. The result of FBA was confirmed by flux variability analysis (FVA). This result suggests that experimentally observed nitroreduction of TNT would have occurred under suboptimal growth conditions [E. coli in transforming TNT to reduced derivatives aerobically and anaerobically [To explore this capability of ormation  into thenditions . To applobically ,29,30,32obically ,9,13,30.As seen from Although a reaction flux can take any arbitrary value across FVA-determined range without affecting growth, the probability distribution of flux within the allowable interval is not uniform. In other words, the flux of a particular reaction may not take all feasible values with similar probability. The actual arrangement of flux through metabolic network reactions is highly influenced by the topology of the flux space. Uniform random sampling of the flux space thus can be used to obtain the probability distribution of a flux of interest within the feasible range, and thereby the most likely value of the flux  , is a CBA technique for estimating the amount of change in the optimal value of an objective function per unit of change in a relevant variable   (4-supplied culture (GR = 0.13/h). The lower GR during growth on TNT can be explained by the two reducing equivalents required for release of each NH4 molecule via DAP vs. free access to NH4 in ammonium-supplemented culture. Under suboptimal condition, at GR of 0.076/h, TNTUR was found to vary within the range of 0.8–3.36 mmol/gdw/h averaging at 0.93 ± 0.15 mmol/gdw/h (median: 0.89 mmol/gdw/h). Given that the GR is close to the corresponding value during NRP activity, the significantly higher mean TNTUR shows the greater efficiency of DAP relative to NRP.Apart from TNT nitroreduction, n source ,28. Base release ,28,53,54ay (DAP)  in the mE. coli to grow anaerobically on TNT. As predicted by FVA, at TNTUR ranging within 1.30–5.23 mmol/gdw/h an optimal GR of 0.011/h could be achieved, being ~10 fold lower than in the aerobic culture. While the maximum feasible TNTUR is 4.5 fold higher compared with aerobic cultivation, flux sampling identified an average TNTUR of 2.68 ± 0.22 mmol/gdw/h (median: 2.67 mmol/gdw/h), corresponding to a nitrogen assimilation of 1.4%. Based on these data, we concluded a generally lower efficiency of DAP under anoxic vs. oxic condition. Nonetheless, when comparison is made with non-assimilatory anaerobic activity of NRP (FBA and FVA results), DAP shows considerably higher efficiency.Although not explored experimentally, we also evaluated the potential capability of E. coli is accompanied by release of TNT-derived nitrite [E. coli’s assimilatory nitrite reductase is non-functional under an aerobic regime [E. coli model, followed by simulation of growth in a glucose-limited medium containing both NH4 and TNT (GUR = 250 mg/gdw/h). While under optimal aerobic condition (GR = 0.13/h), neither TNT uptake and nor DNP activity was observed, suboptimal condition , allowed a maximum TNTUR of 2.26 mmol/gdw/h with concomitant release of nitrite up to 3.94 mmol/gwd/h. Sampling of flux space determined the average TNTUR to be 0.025 ± 0.088 mmol/gwd/h (median 0.07 mmol/gdw/h), predicting an order of magnitude lower activity of DNP than DAP under aerobic regime. This activity however is 2.5 fold as high as that of NRP at corresponding cultivation condition.Two previous studies provided evidence that assimilatory utilization of TNT by  nitrite ,28. The  nitrite . Althougc regime  and thusE. coli possesses an active assimilatory nitrite reductase [2 as a nitrogen source. The potential contribution of this route to TNT degradation capacity of E. coli has not been explored experimentally, thus in silico analysis can yield useful insights. According to our simulation, by uptaking 3.84 mmol/gdw/h of TNT, cell is able to grow as high as 0.038 /h with concomitant ammonium exertion of 1.52 mmol/gdw/h. The observed GR is 10-fold that during anaerobic growth on ammonium and 3.45-fold of that during anaerobic TNT assimilation through DAP. TNTUR through anaerobic DNP activity is 1.4-fold higher than the corresponding average value during anaerobic DAP activity. Also a nitrogen balance revealed that 3.4% of TNT nitrogen is assimilated through DNP which is 2.4-fold of that possible through anaerobic DAP. These data consistently indicate that DNP has the highest anaerobic TNT biotransformation efficiency among other alternative routes, suggesting the potentially significant contribution of this pathway in E. coli nitroaromatic degradation capability.Unlike aerobic conditions, under an anoxic regime eductase  allowingE. coli individually. In practice, however, these pathways may function simultaneously and interact during metabolism of TNT as implicated from presence of TNT-derived NO2, NH4 and nitroaminotoluene products in E. coli cultures [E. coli could only be understood if simultaneous activity of all pathways and the potential cooperation/trade-off between them is taken into account. To address this issue, we incorporated the complete reaction sets of NRP, DAP, and DNP into the metabolic model simultaneously to construct an in silico strain representing the major TNT degradation mechanisms in E. coli. FVA and RA were then used to explore the pattern and flexibility of flux distribution among multiple degradation pathways.Above we analysed the efficiency of three major TNT biotransformation pathways of cultures ,28. TherE. coli replicates the GR and TNTUR when only DAP was present (see above), confirming preferred use of this pathway for aerobic degradation. However, at suboptimal condition, the flux structure gains flexibility to simultaneously activate DAP, DNP, and NRP as indicated by positive maximum fluxes through products of each pathway. This observation is supported by presence of NH4, NO2, and nitroreduction products in E. coli TNT-supplemented aerobic cultures [4 produced by nitrite reductase is excreted as a by-product of TNT degradation. Under suboptimal anaerobic condition at the expanse of lower GR, flux can partition between DAP, DNP, and NRP, as represented by feasible excretion of NH4, NO2 and ADNT, respectively. Accordingly, while the optimal flux distribution is not flexible enough to allow simultaneous functioning of all pathways, under suboptimal conditions limited co-activity of multiple pathways is feasible.As seen from cultures ,28. UndeTo gain a more dynamic insight into the trade-off between optimality and flux distribution flexibility, we performed robustness analysis between (1) GR and fluxes of DAP, DNP, and NRP; (2) fluxes of DAP, DNP, and NRP, pairwise, under both aerobic and anaerobic regimes. As As The relative behavior of optimal fluxes in anaerobic system shows remarkable differences. According to E. coli for efficient TNT biotransformation through the state-of-the-art systems and synthetic biology tools available for this strain [The detailed description of competition or cooperation between TNT degradation pathways provided here can inform engineering of s strain ,56.E. coli at steady state, we extended our simulations to batch mode which is more practiced experimentally. Batch growth culture was simulated by dynamic flux balance analysis (DFBA) . Sentuni et al. [E. coli’s ability to utilize TNT as the sole nitrogen source. To validate our in silico simulation, we conducted DFBA under the corresponding culture conditions. The study of Sentuni et al. [Having explored the TNT transformation capabilities of i et al.  and Gonzi et al.  conductei et al.  was carri et al.  were 0.0i et al.  and Gonzi et al.  who repoBased on TNTURs reported in E. coli when TNT is metabolized as the sole nitrogen source. To this end, we plotted the surface of optimal growth vs. OUR and TNTUR using the Phenotype Phase Plane function of the COBRA Toolbox [Oxygen uptake rate (OUR) is an important parameter in aerobic microbial processes ,60,61,62 Toolbox . AccordiDespite this potential, improvement of TNTUR by suboptimal aeration is not guaranteed because at similar GR, TNTUR has the room to approach the other edge of the phenotype plane, taking values lower than the optimal growth conditions. To alleviate the uncertainty concerning actual position of flux we randomly sampled the flux space at various OUR levels. As mentioned earlier, the low aeration regime, while possibly enhancing biotransformation rate, may negatively impact the productivity due to reduced GR. To examine whether the low-OUR-induced improvement in TNTUR would be followed by productivity, using DFBA we computed the productivity vs. relative OUR at the average  TNTUR values. As comparison of E. coli TNT tolerance have reported various multiple thresholds ranging from 66 to 200 mg/L [E. coli to variation in chemical loading rate. Both of these advantages however are constrained to the maximum toxic concentration threshold, highlighting the importance of improved TNT tolerance for viable biodegradation processes.The efficiency of microbial remediation of NACs is constrained by two major factors: (1) low rate of degradation hampering time-efficient clean-up of contaminants (particularly at high loading rates); and (2) the relatively low tolerance of microbes to chemical toxicity . Studies200 mg/L ,32. Howe200 mg/L , under b200 mg/L ,31), proE. coli generally grows slower under anoxic compared with oxic regimes [Pseudomonas sp. strain TM15 [E. coli. Therefore we examined in silico the impact of ID on biodegradation productivity. In doing so, DFBA was performed at various ID levels around the experimentally examined value of 0.02 and fixed TNT concentration of 0.588 mM. As illustrated in Although  regimes , we obse regimes ,69. Whilain TM15 , no suchDue to the advantages of aerobic cultures such as rapid growth rate and diverse metabolism, high CD aerobic processes have traditionally attracted more interest compared with anaerobic processes. Notwithstanding, aerobic cultivation has its own drawbacks when applied to TNT bioremediation, including: 1) the formation of more toxic and recalcitrant compounds than TNT (such as azoxytetranitrotoluenes) in the presence of oxygen; (2) limited biodegradation rate because of (1), and 3) low availability of TNT metabolites to biodegradation due to their binding to the organic soil materials [ the form low avaiTo avoid these limitations, in recent years much attention has been been focused on anaerobic biodegradation ,9,13,20,Rhodococcus erythropolis [Yarrowia lipolytica strain compared with CBA-independent culture optimization. In the present study, we applied CBA to identify CSs inducing the highest yield of TNT bioconversion in E. coli. For this purpose, compounds including glucose, fructose, galactose, arabinose, glycerol, ethanol, pyruvate, acetate, citrate, fumarate, succinate, malate, lactate, gluconate, and glutamate were separately set as the limiting substrate of E. coli TNT-supplemented growth culture, followed by FBA and FVA to calculate rate and thereby yield of biotransformation. The effectiveness of alternative substrates on yield of assimilatory TNT transformation is compared in Co-metabolic biotransformation of nitroaromatics requires the presence of primary substrate(s) serving as sources of carbon and electrons ,9,13. Gihropolis . Nambou hropolis  showed t+s for ethanol and a NAD+ and a NADP+ for glycerol. Under an aerobic growth regime where NAD+ is abundant due to the activity of oxidative phosphorylation, oxidation of ethanol will be more efficient than that of glycerol. Conversely, during anoxic growth where NAD+ is more limited than NADP+, glycerol oxidation which needs lower amounts of NAD+ is preferred.According to CBA predictions, therefore, ethanol and glycerol are identified as the most effective substrates for aerobic and anaerobic TNT degradation processes, respectively. The superiority of these substrates lies in generation of a comparatively larger pool of reducing equivalent per carbon flux, to be used in TNT degradation pathways. Oxidation of both compounds to central metabolites requires two oxidized equivalents: two NADSphingomonas strain UG30 (pcpB) to E. coli enabled the strain to degrade 4-nitrocatechol and p-nitrophenol concomitantly with nitrite release [E. coli of recombinant plasmids from Enterobacter cloacae and P. pseudoalcaligenes JS45 carrying nitrobenzene reductase (nfs1) and nitrobenzene nitroreductase (nbzA), respectively, resulted in rapid conversion of nitrobenzene to aminophenol (64%) [Metabolic engineering (ME) finds an increasing use in bioremediation process development ,73,74,75 release . In anotol (64%) .Rational design of microbial agents for bioremediation is boosted by the advent of GSMMs, enabling comprehensive analysis of the strains’ capabilities in degradation of toxic compounds ,41,42,782t2rpp shows high TNTUR (comparable with ethanol-medium-based designs) at the expanse of very low GR (5% of WT). Strains designed in glucose-limited medium show the least efficiency among all. However, the single KO (∆PGK) and double KO (∆PGK-∆ACtex) designs present comparable phonotypes with ∆TPI-∆GLYCDx mutant in glycerol-limited medium. Congruent with carbon source effectiveness study  of an organism is a mathematical representation of its metabolism in silico. Ideally, the entire known metabolic reaction network is modelled as a stoichiometric matrix S with rows addressing the metabolites and columns addressing the reactions. Each element of Sij in the stoichiometric matrix is the coefficient of the ith metabolite in the jth reaction. Each reaction is linked to its catalysing enzyme which in turn is associated with its encoding gene(s). The relationship between genes, proteins, and reactions are established based on Boolean logical regulatory rules. The structure of metabolic network hence captures essential molecular biology of the relevant organism at metabolic level.The genome-scale metabolic network is further developed to a constraint-based model (CBM) by restricting the flux of each reaction to physico-biologically relevant constraints. Typical constraints imposed on the fluxes include unidirectionality of some reactions, quasi-steady-state metabolic operation, and optimal flux distribution. The CBM can then be explored by constrain-based analysis (CBA) to characterize metabolic phenotypes under defined environmental conditions.E. coli, iJO1366, to analyse the phenotypes relevant to TNT metabolism in this bacterium. In doing so, firstly we extended iJO1366 to account for a curated version of the known or evidently hypothesized pathways of TNT biotransformation in E. coli. The pathway information was extracted from literature [In the present study we used the most comprehensive high quality metabolic model of terature ,30,44,45terature  and was E. coli. The standard protocol for constraint-based analysis (CBA) of metabolic networks is documented [E. coli extended GSMM.We adopted a constraint-based approach to analyse the TNT biotransformation capacity of cumented . Given tcumented , the FluBecause each metabolite usually participates in more than one bioconversion, the metabolic network is an underdetermined system and thus there will be an infinitive number of solutions to satisfy the stoichiometric constraints. FBA is a linear-programing-based approach to find one steady-state flux distribution solution within the closed flux space allowing the global optimal value of a specified metabolic objective function (OF) to be reached ,81. BaseWhile FBA estimates the optimal growth based on quasi-steady-state assumption, the microbial batch growth is a dynamic process in which biomass concentration increases exponentially along gradual exhaustion of the limited substrate. Dynamic flux balance analysis (DFBA) is an iterative FBA-based method for simulation of the (fed-)batch bioprocess . AccordiWhile the LP nature of FBA guarantees identification of the globally optimal OF value (here biomass production), this unique value could be realized through infinitive number of flux distributions. In another word, flux through many reactions can vary within a certain range without affecting OF. FVA is a FBA-based technique to identify the span within which each reaction flux can vary while still supporting the FBA-determined OF value ,84.max/Comparison of in silico and experimental data in our and some previous studies shows that the optimal metabolic operation assumed by FBA is usually not realized during wet-lab experiments ,28. The Although all reaction flux values within FVA-determined interval will yield an identical OF value, the probability distribution of flux within the feasible range is not uniform. Radom sampling of the flux space can elucidate the structure of flux distribution which in turn can be analyzed to obtain the most probable flux values within the allowable space . WheneveRobustness analysis (RA) is a CBA technique to evaluate the amount with which a metabolic OF will change along with a unit of change in a particular reaction flux (control flux) . The RA The FBA-determined optimal flux distribution is a function of environmental parameters imposed on the metabolic network. Phenotype Phase Plane (PhPP) analysis is used to characterize how an optimal phenotype of interest will response to variation of environmental conditions . We usedE. coli to predict a series of strain design scenarios enabling higher yield of TNT biotransformation. More specifically, our aim was to identify single- to triple-reaction knockouts (KOs) potentially improving the minimum feasible TNTUR relative to the wild-type (WT) strain. Several CBA-based methods have been developed for in silico strain design [Genome-scale constraint-based metabolic models are extensively used to identify relevant metabolic engineering strategies aimed at improved yield of a target bioconversion ,87. We un design . OptKnocn design ,89. Due n design . A tri-ln design . Becausen design ), and foE. coli, including control of nitroreduction pathway by NADPH availability, extreme limitation of complete TNT nitroreduction, simultaneous release of nitrite, ammonium, and nitro reduction products during TNT degradation, and the time profile of TNT elimination in batch cultures. Our analyses also provided potentially useful insights into the impact of process parameters on TNT bioremediation efficiency, including oxygen uptake rate (OUR) and aeration regime, TNT concentration, inoculum density, and carbon source. High cell density ethanol-limited anaerobic cultivation showed the highest efficiency among other processes, giving it merit for more in-depth research. We also designed a series of E. coli stains with improved TNT biotransformation phenotype in silico. The obtained results may guide future efforts in development of high performance microbial TNT clean-up process and highlights CBA as an informative approach to accelerating bioremediation research.The present study extended the application of constraint-based analysis (CBA) to the microbial remediation of nitroaromatic compounds (NACs). Our results replicated several TNT biotransformation phenotypes observed in


Appropriate substrate selection between fats and glucose is associated with the success of interventions that maintain health such as exercise or caloric restriction, or with the severity of diseases such as diabetes or other metabolic disorders. Although the interaction and mutual inhibition between glucose and fatty-acids (FAs) catabolism has been studied for decades, a quantitative and integrated understanding of the control and regulation of substrate selection through central catabolic pathways is lacking. We addressed this gap here using a computational model representing cardiomyocyte catabolism encompassing glucose (Glc) utilization, pyruvate transport into mitochondria and oxidation in the tricarboxylic acid (TCA) cycle, β-oxidation of palmitate , oxidative phosphorylation, ion transport, pH regulation, and ROS generation and scavenging in cytoplasmic and mitochondrial compartments. The model is described by 82 differential equations and 119 enzymatic, electron transport and substrate transport reactions accounting for regulatory mechanisms and key players, namely pyruvate dehydrogenase (PDH) and its modulation by multiple effectors. We applied metabolic control analysis to the network operating with various Glc to Palm ratios. The flux and metabolites’ concentration control were visualized through heat maps providing major insights into main control and regulatory nodes throughout the catabolic network. Metabolic pathways located in different compartments were found to reciprocally control each other. For example, glucose uptake and the ATP demand exert control on most processes in catabolism while TCA cycle activities and membrane-associated energy transduction reactions exerted control on mitochondrial processes namely β-oxidation. PFK and PDH, two highly regulated enzymes, exhibit opposite behavior from a control perspective. While PFK activity was a main rate-controlling step affecting the whole network, PDH played the role of a major regulator showing high sensitivity (elasticity) to substrate availability and key activators/inhibitors, a trait expected from a flexible substrate selector strategically located in the metabolic network. PDH regulated the rate of Glc and Palm consumption, consistent with its high sensitivity toward AcCoA, CoA, and NADH. Overall, these results indicate that the control of catabolism is highly distributed across the metabolic network suggesting that fuel selection between FAs and Glc goes well beyond the mechanisms traditionally postulated to explain the glucose-fatty-acid cycle. The profile of substrates consumption has been the focus of great interest in medical research due to their associations with diverse health conditions, such as metabolic disorders, diabetes, heart failure and cancer that display altered patterns of glucose and fats utilization. The interplay between fats and glucose utilization has been intensely studied in the context of fed-fast transitions, caloric restriction, starvation and insulin insensitivity in metabolic disorders . Organs Glucose and FAs are metabolized through central catabolic pathways, a universal biochemical backbone from prokaryotes to eukaryotes. Glucose and FAs catabolism converges at the level of AcCoA, which can be generated via pyruvate dehydrogenase from glucose-derived pyruvate, or from mitochondrial β-oxidation. Central catabolism provides all the precursors  for cellular biomass , as wellMetabolic control analysis (MCA) has been mainly used in biotechnological studies aimed at rationally optimizing processes leading to commercially valuable products, or eliminating undesirable chemicals, e.g., from the environment . In fundMetabolic control quantifies through flux and metabolite concentration control coefficients to what extent a certain flux through a metabolic pathway varies upon changes in enzymatic and/or transport activity . To distinguish control from regulation has practical implications because it provides crucial clues about strategies to modulate metabolism . On the In the present work, we apply an integrative and quantitative approach to study the control and regulation of substrate selection in a computational model of central catabolism comprising the utilization and processing of glucose and FAs in cytoplasmic and mitochondrial compartments. This strategy is amenable for systems biology approaches integrating comprehensive –“omics” data  combined with computational modeling and bioinformatics . The preTo study the control and regulation of substrate selection in central catabolism fueled by different combinations of Glc and the FA Palm, a computational model was formulated encompassing main catabolic pathways involved in the utilization of glucose and palmitoylCoA (PCoA), the activated form of Palm .2 in the prosthetic group from Electron Transfer Flavoprotein (ETF). AcCoA will be further degraded in the TCA cycle generating NADH and succinate that supply electrons to the respiratory chain. ATP synthesis, ion transport   , H+ leakytoplasm  are all ytoplasm .The model comprises 82 state variables and 119 enzymatic, electron transport and substrate and ion transport reactions. Model simulations were run with a code written in Matlab  using the ODEs15 integrator. MCA was performed using a program written in Matlab and its algebraic toolbox.-10 sec-1.A description of the model in terms of ordinary differential equations (ODEs), rate equations describing the kinetics of each of the processes included in the metabolic network depicted in To characterize the computational model steady state behavior, we performed simulations with either increasing glucose concentration in the extracellular compartment at fixed 10 μM PCoA or augmenting PCoA concentration at constant 10 mM Glc. vs. FAs. For example, a 0.5 PDH/CS flux ratio indicates that half of the AcCoA derives from PCoA oxidation in mitochondria although the flux sustained by PDH is several-fold larger than the FA uptake via carnitine palmitoyl transferase 1 (CPT1) (The flux ratio of PDH over citrate synthase (CS) (PDH/CS) quantifies the relative amount of mitochondrial energy derived from Glc with respect to FA oxidation . AcCoA i1 (CPT1) . The lat1 (CPT1)  for skel-1 . Under these conditions, we found that substrate selection is more sensitive to glucose than PCoA variation, and although the changes in substrate levels reciprocally affect each other, both substrates, to a greater or lesser extent, feed mitochondrial OxPhos. In the next section, we analyze the main rate-controlling steps of the flux through central catabolic pathways of Glc and PCoA.We calculated the overall flux control throughout central catabolism at different steady states of the computational model, obtained in simulations depicted in x-axis are the activities that control the flux through the processes on the y-axis . Importantly, the red-blue columns at the extreme left of the heat map display the large and highly distributed control exerted throughout the catabolic network by glucose transport (Glut4), hexokinase (HK), and phosphofructokinase (PFK) steps from glycolysis  (x-axis). Further downstream, the rate of cytoplasmic ATP hydrolysis (HydroATP), representing all energy-demand processes in the cytoplasmic compartment , and the mitochondrial phosphate carrier (PiC), also exert a widespread control throughout the catabolic network (x-axis).+-dependent isocitrate dehydrogenase, IDH) and antioxidant metabolism  cluster together, indicating that they mainly exert control over themselves while negligibly controlling other pathways in the network. Activities operating inside mitochondria, e.g., aconitase (ACO), IDH NAD+- dependent, malate dehydrogenase (MDH), aspartate-amino transferase (AAT) and membrane-associated processes  all exert control on the TCA cycle and OxPhos along with other mitochondrial matrix processes such as β-oxidation and antioxidant reaction fluxes. Reaction steps from FA oxidation mainly control fluxes through β-oxidation and exert some quantifiable control over TCA cycle and OxPhos.Interestingly, “redox and antioxidants” processes involved in redox  notably to AcCoA and CoA  from the lower part of glycolysis, and CPT1 from β-oxidation, that catalyzes the transfer of palmitoyl carnitine from the cytoplasm to the mitochondrial matrix, were studied as representative steps from Glc and FA oxidation pathways, respectively.The control by ATP demand from cytoplasmic processes (pie slice in cyan) increased with the level of glucose. At 5mM Glc, control by the ATP demand and PFK increased from ∼5 and 4.5%, respectively, while at 10mM Glc both activities’ control augmented to ∼ 9%, irrespective of PCoA level . Likely,2 , are the most rate-controlling steps of the FAs oxidation flux. Other mitochondrial activities such as the TCA cycle, H+ leak and ion transport , also exert control over β-oxidation. As expected, control by the H+ leak, which is dependent on the level of PCoA (acting as an uncoupler at high concentrations) (rations) , increasTogether, the data presented show that the quantitative distribution of the flux control throughout central catabolism fueled by Glc and Palm depends upon the relative levels of both substrates; increasing Glc concentrations have higher effects than enhanced FA levels. However, qualitatively, a similar pattern of control was found throughout all conditions analyzed. Glucose uptake and ATP demand are main rate-controlling steps of the flux through glucose catabolism in the cytoplasm whereas steps from OxPhos and ion transport pathways control the flux of FA oxidation in mitochondria.2O2 for redox signaling) or epigenetic  pathways, depend on metabolites or ions concentration for triggering , including the mitochondrial membrane electric potential (Δψm), the level of which is controlled by the steps shown on the x-axis.Some important general control principles of metabolism can be visualized in this heatmap. Metabolite concentrations are controlled by the same rate-controlling steps of the flux through pathways to which these metabolites belong. For example, Glut4, HK, PFK, and HydroATP are main rate-controlling steps of the flux with widespread influence throughout the catabolic network , arrows,Together, the data presented show that although the share of the control of cellular metabolites concentration is highly distributed, general principles apply such as the key role played by the main rate-controlling steps of the flux, and, locally, by the enzymes that consume those metabolites, enabling a comprehensive understanding of a complex but crucial aspect of metabolism and physiology.In this work we have analyzed for the first time the control and regulatory properties of central catabolism fueled by glucose and fatty acids, using a comprehensive, integrative computational model, allied with a quantitative systems biology approach. Control analysis under different relative supply levels of glucose and palmitate showed the interdependence and mutual inhibition between degradation pathways, consistent with reported data . Using o1  in central catabolism. Heat maps also enable a quick localization of the most rate-controlling processes in the network, while highlighting the distributed nature of the control. For example, the control of successive reaction steps displays the same control coefficients because all steady state fluxes are equal in a linear pathway. On the contrary, at branching points the sign of the control coefficients is inverted because the steps in one branch negatively control the fluxes on the other branch. This is illustrated by PFK that controls positively glycolysis and negatively the pentose phosphate pathway –5. Also,The widespread control exerted by steps upstream of glucose metabolism  depends Previous studies applying MCA have revealed that glucose metabolism in working rat hearts was mainly controlled at the level of glucose uptake and phosphorylation by hexokinase . In thosThe divergent behavior of the flux control displayed by PFK and PDH invites revising the concepts of control and regulation . PFK andIn vivo, other hormones would influence catabolic fluxes, such as glucagon, catecholamines, and leptin. These effects will be considered in future editions of our modeling.The magnitude of the glucose oxidation inhibition by FAs found in our model simulations, i.e., 15–20% decrease in PDH activity, is much smaller than the 4- to 10-fold decrease observed in working hearts in the presence of low and high fat, respectively, compared to the absence of exogenous fat . Under tex vivo heart perfusion experiments used to parameterize the model. The activity of cytoplasmic ATP citrate lyase, that converts citrate into AcCoA, is not included, while adenylate kinase, that interconverts adenine nucleotides, is only implicitly considered in an aggregated, generalized energy demand (HydroATP). Consequently, cytoplasmic citrate and AMP are not state variables but parameters in the model. Due to the importance of AMP as a modulator of PFK, we investigated the effect of micromolar levels of AMP under 10 mM Glc/10 μM PCoA  that pathways such as antioxidant systems and other alternative routes (polyols) exert negligible control over substrate selection but confer robustness to complex networks function under relevant but specific (patho)physiological conditions , we consider our results acceptable under the conditions described herein. Additional work will be needed to further adapt the analytical tools of MCA to stiff systems that mimic real, complex, biological networks.The size and complexity of the metabolic network described by our computational model encompass processes sustaining widely different fluxes. For example, glucose catabolic pathways vary between 10ents see . Using tAs far as we are aware, this is the first time that a validated, comprehensive computational model of central catabolism distributed in cytoplasmic and mitochondrial compartments, including detailed kinetics and known regulatory mechanisms, is presented and its control and regulatory properties quantitatively analyzed as applied to substrate selection between glucose and FAs. It is shown that both substrates, to a greater or lesser extent, fuel mitochondria. Substrate selection changes as a function of the relative levels of Glc and FA, reciprocally influencing each other. Under these conditions, quantitative rather than qualitative changes in the profile pattern of flux and metabolites’ concentration control coefficients were corroborated, i.e., the main rate-controlling steps of fluxes and metabolite concentrations remained the same.SC, MA, and SS contributed to conception and design of the study. SC designed and analyzed the model simulations, performed the control analysis, and wrote the first draft of the manuscript. MA wrote sections of the manuscript. All authors contributed to manuscript edition, read and approved the submitted version.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.


Microalgae are capable of producing sustainable bioproducts and biofuels by using carbon dioxide or other carbon substances in various cultivation modes. It is of great significance to exploit microalgae for the economical viability of biofuels and the revenues from high-value bioproducts. However, the industrial performance of microalgae is still challenged with potential conflict between cost of microalgae cultivation and revenues from them, which is mainly ascribed to the lack of comprehensive understanding of carbon metabolism and energy conversion. In this review, we provide an overview of the recent advances in carbon and energy fluxes of light-dependent reaction, Calvin–Benson–Bassham cycle, tricarboxylic acid cycle, glycolysis pathway and processes of product biosynthesis in microalgae, with focus on the increased photosynthetic and carbon efficiencies. Recent strategies for the enhanced production of bioproducts and biofuels from microalgae are discussed in detail. Approaches to alter microbial physiology by controlling light, nutrient and other environmental conditions have the advantages of increasing biomass concentration and product yield through the efficient carbon conversion. Engineering strategies by regulating carbon partitioning and energy route are capable of improving the efficiencies of photosynthesis and carbon conversion, which consequently realize high-value biomass. The coordination of carbon and energy fluxes is emerging as the potential strategy to increase efficiency of carbon fixation and product biosynthesis. To achieve more desirable high-value products, coordination of multi-stage cultivation with engineering and stress-based strategies occupies significant positions in a long term. Furthermore, their less requirements for arable land and freshwater make them attractive for commercial exploitation. However, thus far, the biotechnological application in microalgae has been limited under industrial conditions for the lack of comprehensive understanding of carbon metabolism and energy conversion. Having considered the interest in industrial settings, many institutions and companies designed and proposed new strategies to improve the industrial performance of microalgae, such as utilizing light-emitting diode (LED) light, optimizing culture modes and modifying carbon fixation pathways and photosystems . Th. Th2 mol2 for the biosynthesis of organic carbon-based compounds [2O) has been paid much attention due to the absence of NADPH in this pathway [+, like O2, NO2− [During photosynthesis, chlorophyll traps energy from sunlight and produces ATP and NADPH to respond to the metabolic reactions. The energy is used to fix COompounds . There a pathway . The plaO2, NO2− .2. Given that the stoichiometric ratio of ATP and NADPH per CO2 fixed is 3:2 and the ratio from LEF is merely 2.6:2; the ATP is deficient in CO2 fixation. As a result, the reducing equivalents are accumulated in the chloroplast, which may result in the expression of antioxidant and defense genes. Chloroplast is evolved to counteract the imbalance by regulating several important biological processes. Under high light conditions, changes of plastoquinone (PQ) pool are known to balance and maintain photosynthetic energy distribution between PSII and PSI in the short term and photosystem stoichiometry such as light-harvesting proteins is adjusted to counteract the imbalance in the long term [Cofactors like ATP and NAD(P)H play important roles in regulating intracellular environment to orientate the metabolic pathways into biomass accumulation or product biosynthesis in the various cultivation conditions. However, ATP/ADT and NAD(P)H/NAD(P) are shared in numerous pathways and protein transports. The translocators located in membranes are biosynthesized by enough ATP amount, and it has been reported ATP directly participates into about 200 metabolic pathways . NAD(P)Hong term .2, while the excess NADPH produced from CEF will be accumulated in chloroplast or transferred into cytoplasm and mitochondria, which affects the cellular redox state, and finally leads to the increase of ROS [A tight stoichiometric ratio of ATP and NADPH in chloroplast is needed to fix COe of ROS . The mite of ROS , 45. Thee of ROS .l-galactono-1,4-lactone dehydrogenase in photorespiration. Therefore, the photosynthetic efficiency can be enhanced by increasing the ascorbate even though the TCA cycle is impaired. The regulation shows an ascorbate-mediated manner in photosynthesis and respiratory. In addition, another mechanism has also been proposed to reinforce the high degree of metabolic co-ordination. For example, uncoupling proteins located in inner mitochondrial membrane are related to maintain the redox poise of mitochondrial electron transport chain, especially when the demand of oxidation of NADH is high, showing potential to facilitate both TCA cycle and photosynthetic metabolism [Generally, photosynthesis and respiratory processes are energy-generating processes that are involved in redox regulation and ROS metabolism. The processes are interlinked and influence the energy flux and redox fluctuations between chloroplast, mitochondria and cytosol. A high level of metabolic coordination and balance between signaling and metabolic pathways is required to maintain energy flux through organelles with less ROS produced. Recent studies link rate of ascorbate biosynthesis with the two processes . The asctabolism .2O2, without formation of energy. In this pathway, electron from PSII is used to reduce atmospheric O2, thereby generate O2−. Then the O2− is reduced to H2O by the catalyzations of superoxide dismutase and ascorbate peroxidase. This pathway provides a “safe” way to dissipate excess photons with high electron flux. During photorespiration, malate/oxaloacetate shuttle and other pathways of reducing power exchange are performed to deliver NADPH/NADH among chloroplast, cytosol and mitochondria to generate H2O2 in cytosol, which can be catalyzed by peroxidase to generate H2O.Energy dissipation is usually ignored in the microalgal researches that cause nutrient loss in the cultivation processes. During photosynthesis, chlorophyll and carotenoid molecules located in LHCs are able to efficiently absorb the light and transfer the energy into RC. However, part of the energy will be dissipated by carotenoid as heat . As show6-sugars requires 18 ATP molecules and 12 NADPH molecules, and the energy to link sugars requires 1 ATP/sugar. Normally, it requires 8 photons to fix 1 CO2 molecule, which actually requires 3 ATP molecules and 2 NADPH molecules. Therefore, the lowest photon consumption on acetyl-CoA is 16 mol mol−1 and on C6-sugars is 48 mol mol−1, respectively. To evaluate the efficiency of photon utilization, index of YP is used and described as follows:YP for acetyl-CoA is 6.25 × 10−2 and for starch is 2.08 × 10−2 mol mol−1, respectively.The metabolism of microalgae is associated with the energy consumption, such as CBB cycle and TAG biosynthesis. Evaluating theoretical energy consumption in microalgae is essential for enhancing the biomass and product yields by rerouting the energy in metabolic flux. Polysaccharides are typically used as the energy and carbon reserves. In microalgae, starch is the most common type of polysaccharides that stores carbon and energy as the primary metabolite. Other polysaccharides in microalgae are widely distributed in intracellular environment like chrysolaminarin  and extr2 in starch requires 3.17 ATP molecules and 2 NAD(P)H molecules, which is equal to 8.17 ATP molecules, and 1 molecule CO2 in TAG requires 2.30 ATP molecules and 2.81 NAD(P)H molecules, equal to 9.32 ATP molecules [53H94O6) is 592 mol mol−1. Therefore, the maximum YP is 1.69 × 10−3 mol mol−1 for TAG (C53H94O6). The need of energy for TAG is higher than starch.The lipid usually represents more concentrated storage of energy. In lipid biosynthesis, the requirement of energy is in high level , the forolecules . The lowAdvances of methods based on microbial physiology in biomass accumulation of microalgae have been untied with gene technology and fermentation model to increase the valuable products or save the industrial cost. The designs of cultivation modes and methods to maintain energy (primarily from light) and nutrient balances have the objective of creating a suitable environment for cell growth and the designs to disturb the balances aim at forming the stressed environment for product biosynthesis Table .Table 1EConditions of microalgae are key factors to increase biomass and the product yields. However, the conditions significantly differ from various microalgae used. Therefore, we discuss these conditions in aspect of methods to improve environment, and thus the methods can be applied in various microalgae.Light is the critical factor to increase biomass accumulation and product biosynthesis. In mass cultivation, enhancing light intensity is the traditional way to provide sufficient protons to increase biomass, while the effect is unsatisfactory due to the photoinhibition as cell number increases. Furthermore, a considerable amount of heat will be generated if the light contacts closely the microalgae. As an alternative, innovative light source, which possesses a narrow spectral output to overlap the photosynthetic absorption spectrum of microalgae used, is needed to increase the conversation of light energy and decrease the elimination of heat. Therefore, searching the optimal light wavelength with LED light is the other way to increase biomass with high photosynthetic efficiency, which is also the active field in past several years , 63. How−1 [−1 than open raceway pond [Fistulifera sp. with specific growth rate of 0.29–0.57 g L−1 and Araya et al. [Chlorella vulgaris with specific growth rate of 0.131 g L−1 day− 1.The industrial performance of microalgae by autotrophic cultivation is limited by cell concentration and light supply. Open ponds applying natural light is common option for mass cultivation, while the biomass is merely around 0.5 g L−1 . Therefoway pond . Sato etway pond  establisa et al.  used panCs, Cx and Cp are the concentration of substrate, biomass and products, CF·s is the feeding concentration of substrate, Yxs and Yps are growth yield on nutrients and products, μsx is the specific consumption rate of nutrients, n is product content and t is the cultivation time. The stable nutrient concentration in culture process is the most important factor to keep the growth rate in a high level and to achieve high biomass. Adding nutrients is usually done during cultivation. To avoid the effect of dilution rate, fed-batch culture is feasible to improve biomass.Under suitable environmental conditions, the sufficient nutrients lengthen the steady-state growth of microalgae with high growth rate . HoweverYxs) is stable to specific strain, which determines the ratio in fed-batch media. Nutrient-fold fed-batch culture is unsuitable because Yxs of nutrients differs from the initial ratio of nutrient concentration. PH-stable fed-batch culture is proposed to solve feeding time, while the amount is hard to control. Exponential fed-batch culture can be used to effectively overcome the difficulty. As shown in Eq. , neutral (7.5–8.0) and alkaline (8.3–9.0) pH suggested neutral pH resulted in the maximum cell growth rate [Nannochloropsis salina, Chlamydomonas applanata and Dunaliella bardawil [Chlorella ellipsoidea, Dunaliella salina and Nannochloropsis salina [Euglena mutabilis [PH is considered as the critical environmental factor that controls cell metabolism and accumulation of biomass. It seems all the microalgae strains have a small optimal range of pH for high cell growth rate. The growth of wth rate . The simbardawil –78. In as salina . Althougutabilis . The env2, lutein and secondary metabolites like astaxanthin, β-carotene [The products from microalgae can be categorized into primary metabolites such as Hcarotene . High teper carbon molecule into starch than lipid, they would prefer to guide the carbon molecules into starch. Therefore, N-starvation is effective to induce carbon into lipid, while the conversion efficiency is limited by the energy requirement. Phosphorus starvation (P-starvation) is known to increase the TAG levels, potentially by phospholipid degradation. Kamalanathan et al. [C. reinhardtii. In addition, several researches also indicated P-starvation resulted in higher cellular lipid levels [Nutrient-limitation is the conventional stress-based strategy to increase the product yields , 80. As n et al.  reportedd levels , 82. TheMonoraphidium sp. SB2 [H. pluvialis [Isochrysis galbana [−1 improved the lipid productivity [High temperature has strong effect on cell activity, which influences the enzymes in product biosynthesis. Under temperature at 30 °C, 7.4% increase of lipid productivity was achieved in  sp. SB2 . The effluvialis , 84, whiluvialis . The syn galbana . In addiuctivity .Scenedesmus abundans is realized under higher light with 20.9% increase to proximate result [Chlorella sp. and Monoraphidium sp. at the expense of degradation of chlorophyll and reduction of protein, carbohydrate content and membrane lipid [−2 s−1 of blue light within reduced cultivation time of 8 days. Mixed light of blue and white increases 11.8% of astaxanthin in Haematococcus pluvialis [Product biosynthesis of microalgae is an energy-demand process with high level of reactive oxygen species (ROS). High light intensity provides both of them for microalgae. Under high light, cellular morphology changes with less activity, the cell division slows down and cell wall becomes hard and thick. An increase of lipid content in e result . Neutralne lipid . The ligne lipid  has obtaluvialis . SufficiChlorella zofingiensis. When the pH was adjusted to 8 in Scenedesmus abundans, 19.3% increase of lipid productivity to proximate result was obtained [C. zofingiensis [C. reinhardtii converted starch into lipid was also consistent with reported researches [Environmental factors like salts, pH and ROS also affect product biosynthesis. Salts in environment influence osmotic pressure of cell. Pelah et al.  found unobtained . High ROngiensis . The higngiensis . Our prengiensis . The addsearches –96, whicAlthough the stress-based strategies promote the biosynthesis of products, the biomass concentration is limited by the conditions. To maximize the productivity or reduce the cost, more efficient approaches such as multi-stage cultivation and bacteria–microalgae co-cultivation are used by combined the stresses .C. vulgaris, Nannochloropsis, Phaeodactylum tricornutum [Microalgae are regarded as promising cell factories for metabolic engineering. In recent years, the release of whole genome sequence of many microalgae, such as cornutum , benefit2 and glucose in carbon flux and also linked to numerous metabolic pathways like PP pathway; thus the two cycles show significant roles in metabolism of microalgae. Engineering pathways of carotenoid and lipid biosynthesis are the active fields as their pharmaceutical and food functions are being revealed and the benefits of biodiesel from microalgae also attract many researches . In CBB cycle, 15 enzymes such as of RPE, RPI, FBA, TPI, PGK are proved to be activated by TRXs [CP12 are also regulated by TRXs.CBB cycle is a redox-regulated process. Reversible redox post-translational modifications play an important role in regulation of cell metabolism by transforming cysteine residues to different forms, which have been recognized early to regulate the enzymes in CBB cycle. In addition, most of the enzyme reactions are controlled by thioredoxins /NADP(H). The coding genes of gapA and gapB of photosynthetic glyceraldehyde-3-phosphate dehydrogenase are effected by TRXs in different levels; the former one is inactive to TRXs, which requires the cooperative action of CP12. Therefore, CBB can be effectively regulated by CP12 via NAD(H)/NADP(H) ratio. Tamoi et al. [Sccp12-disruption) mutant cells was significantly slower than wild-type cells and O2 consumption rate was 0.43-fold lower than wild strain under dark conditions and therefore concluded that the oligomerization of CP12 regulated the carbon flux from CBB cycle to PP pathway. RCA acts on Rubisco by cutting off the inactive ribulose bisphosphate by ATP-dependent reaction and then the free Rubisco is formylated by combination of Mg2+ and CO2. Therefore, RCA directly effects Rubisco activity and CO2 fixation. The gene CBBX had been originally recognized in the function of carbon fixation. The CBBX is unclear and the functional cbbX genes appear to be associated with red-type Rubiscos [i et al.  observedRubiscos . MeanwhiRubiscos , 108.+ is available for electron, TCA cycle acts to oxidize substrate to CO2 and produce NADH. Otherwise, TCA cycle acts to produce intermediates for anabolism such as citric acid, l-glutamic acid. However, the intermediate yield in TCA cycle is analyzed to be lower than the theoretical maximum yield (YE) [The TCA cycle is activated as two architectures depend on whether the terminal electron acceptor is available in the reaction. TCA cycle can be divided into the reductive branch from oxaloacetate to succinyl-CoA and the oxidative branch from citrate to 2-oxoglutarate. When the NADeld (YE) . The tureld (YE) .+ to decrease the gibbs free energy of the reaction might induce the reaction to occur more easily [Engineering TCA cycle is prone to allocate carbon in targeted products at the expense of the generation of energy. Therefore, pathway of intermediate biosynthesis that requires low level of energy is discussed to alleviate the undesirable effect of energy . Replacie easily . Glyoxyle easily .PSY, PDS, LCYb, CHYb and BKT are suggested as crucial hinges during the carotenogenesis [PSY is the rate-limiting enzyme in the pathway of carotenoid biosynthesis and the coding genes of PSY is different in plants and microalgae [PSY in C. reinhardtii by nuclear transformation increases 2.0- and 2.2-fold of carotenoids violaxanthin and lutein to untransformed cells [PDS gene mutation is a promising approach to increase astaxanthin in H. pluvialis [LCYE and γ-carotene (β-branch) by LCYB [H. pluvialis and C. zofingiensis can synthesize astaxanthin from β-carotene by the action BKT. The transgenic Arabidopsis thaliana with BKT from C. reinhardtii (CrBKT), C. zofingiensis (CzBKT) and H. pluvialis (HpBKT3) has different development in increasing astaxanthin in orange leaves. Overexpressing CrBKT is capable of increasing 1.8-fold in total carotenoids, while the operation of CzBKT and HpBKT3 has no influence on carotenoid accumulation [The key metabolic steps and crucial genes that control biosynthesis of algal carotenogenesis have been reviewed and applied in many researches . The genogenesis –113. PSYcroalgae , 114. Oved cells . The PDSluvialis . In the  by LCYB . Severalmulation . Furthermulation .Cyclotella cryptica T13L into Cyclotella cryptica CYCLO1 and Navicula saprophila NAVIC1, while the lipid content was changeless. To overcome the limit of ACCase, a metabolic sink is required for overproducing the free fatty acid. The sink provided by expression of thioesterase activity has the potential to accelerate metabolic flux from the complex fatty acids to free fatty acids. (ii) For example, Davis et al. [AtDGAT1 and SiDGAT1 is effective to increase oil content [Engineering lipid pathway is an efficient way to increase lipid content in microalgae. Several studies have explored the overexpression of genes to increase lipid product in lipid pathway. In Kennedy pathway, TAG biosynthesis is divided into four stages. The pathway for acetyl-CoA undergoes carboxylation by acetyl-CoA carboxylase (ACCase), which is a limiting enzyme in lipid pathway; (i) Roesseler et al.  found ths et al.  induced s et al. . Diacylg content , 124. Du content . In summSimply, carbon fixation is a ATP-demand process and product biosynthesis is associated with NAD(P)H. Therefore, engineering cofactors of ATP and NAD(P)H are important for biomass accumulation and product biosynthesis.The absorption of solar energy and delivery of electron are limited during the light reactions. Increasing the amount of energy flux by engineering LHCs is performed to enhance the efficiency of light reactions , 99. The2 fixation of microalgae, the pathway kinetics and ATP requirements are the key features to determine the efficiency of CO2 fixation pathways. The poor performance of Rubisco results in slow pathway kinetic and high ATP consumption. As alternative to Rubisco, some attractive carboxylases can be embedded in synthetic CO2 fixation pathways with lower ATP costs than the CBB cycle. Phosphoenolpyruvate carboxylase (PEPc) has greater catalytic capacity than Rubisco to fix carbon, with catalytic rate of 3.4–8.4 μmol min−1 mg−1 to PEPc and 0.9–1.5 μmol min−1 mg−1 to Rubisco, respectively , non-lipid biomass (Cx), non-lipid product concentration (Cp) and lipid concentration (CL) as follows:Yxs, YLs and Yps are growth yield on nutrients and production (g/g), μsx is the specific consumption rate of nutrients that maintains cell. Neglecting non-lipid product concentration (Cp) and specific consumption rate of nutrients (μsx), which show less important in lipid biosynthesis, Eq.  is the lipid content. In situation of low lipid content, the C is important for commercialization of the lipid production, while in high lipid content for most oleaginous microalgae, Y is more significant [Y is harmonized by the NADPH amounts, forming NAD(P)H sink has the potential to increase the lipid yield and lipid content.The lipid biosynthesis is related to the substrate concentration . However, little information of NADP+-dependent GPD is available in microalgae. To regulate the NADPH metabolism, the unbalanced conditions of reducing equivalents may be harmful to cells even induce cellular apoptosis by generating high level of ROS. Therefore, electrochemical system is a novel process to control metabolic pathway under unbalanced conditions of reducing equivalents [2 and increase product yields, defined as photosynthetic microbial fuel cell, photosynthetic microbial electrolysis cell and electro-fermentation H sink now is proved to enable commercialization of microbial carbohydrate-based lipid production . In micrivalents . Until nion Fig. a. Among   and fluorescent probes are gushed to boost COal cells . ECS rep ECSquad , the lincroalgae . Therefocroalgae . These tAlthough tremendous approaches have successfully promoted the biomass and product yields, commercialization of the functional components still remains a challenge. Recent techno-economic analysis work has suggested cost reductions for biofuel economical viability of microalgae was essential, while exceedingly difficult . The devC. pyrenoidosa, are the promising strains in animal feed that can get the value of 750 $ tonne−1 [−1 [−1 for the cultivation. However, the revenue from the lipid in biofuels was merely around 200 $ tonne−1 [In the economic analysis, the three storage carbon substances: protein, carbohydrate and lipid are the main products from microalgae that are exploited in various market scenarios. As the extraction of functional proteins is high cost with low revenue in cultivation process, the microalgal cells are usually broken as animal feed . The pri tonne−1 . The carne−1 [−1 . The digne−1 [−1 . The low tonne−1 . The los tonne−1 .The comprehensive understanding of carbon metabolism and energy conversion of microalgae will provide preferable orientations to design the cultivation processes. The enhancement of biomass concentration is always encouraged for the researchers, not only in microalgae production, but also in waste treatment. To some extent, the researchers make great efforts to keep the biomass increasing, and then nutrient loading amount from waste and bioproduct from microalgae are continuously promoted. The lacking understanding of key limiting factors during the cultivation processes, which can be identified by carbon metabolism and energy conversion, is the major difficulty for the researchers to increase the biomass concentration. This might be one of the reasons that researches usually obtain sub-optimum biomass concentration at the industrial scale. For example, to overcome the nutrient limitation during cultivation processes, fed-batch culture is usually performed based on the microalgal characteristics of specific growth rate and nutrient yield on biomass. These characteristics of a specific strain, however, are fluctuant under different cultivation conditions, which are supposed to determine based on the cultivation systems. To increase the efficiency of light conversion, various bioreactors can be assembled to shorten the optical path. The fermenter is a stable and easy system to control the cell growth, while its low transmittance of light largely limits the final biomass concentration. Therefore, the flat plate bioreactor can be assembled to fermenter with the 12 h day at plate bioreactor and 12 h dark at fermenter.H. pluvialis at the stage of astaxanthin accumulation and shortened the cultivation time with high astaxanthin content. In short, coordination of multi-stage cultivation with engineering and stress-based strategies occupies significant positions in a long term.The low biomass concentration in mass cultivation is still the major challenge for the microalgae production. Indeed, the lipid and carotenoid productivities are largely dependent on biomass concentration , 93. AltChlorella sp. [Another strategy is to apply microalgae in waste treatment, which can offset the cultivation cost by addressing the environment issues . The maiella sp. , 146. Inella sp. . As an eella sp. .Understanding the storage carbon metabolism and energy conversion of microalgae to produce bioproducts and biofuels can bring to light the key barriers, which are the key hinges to guide prospective biomass component and productivity for the commercial viability. The designs of cultivation modes and methods to maintain energy (primarily from light) and nutrient balances have the objective of creating a suitable environment for cell growth and the designs to disturb the balances aim at forming the stressed environment for product biosynthesis. To achieve more desirable high-value products, coordination of multi-stage cultivation with engineering and stress-based strategies occupies significant positions in the long term.