Waste-to-energy: Co-pyrolysis of potato peel and macroalgae for biofuels and biochemicals.

Waste-to-energy conversion presents a pivotal strategy for mitigating the energy crisis and curbing environmental pollution. Pyrolysis is a widely embraced thermochemical approach for transforming waste into valuable energy resources. This study delves into the co-pyrolysis of terrestrial biomass (potato peel) and marine biomass (Sargassum angastifolium) to optimize the quantity and quality of the resultant bio-oil and biochar. Initially, thermogravimetric analysis was conducted at varying heating rates (5, 20, and 50 °C/min) to elucidate the thermal degradation behavior of individual samples. Subsequently, comprehensive analyses employing FTIR, XRD, XRF, BET, FE-SEM, and GC-MS were employed to assess the composition and morphology of pyrolysis products. Results demonstrated an augmented bio-oil yield in mixed samples, with the highest yield of 27.1 wt% attained in a composition comprising 75% potato peel and 25% Sargassum angastifolium. As confirmed by GC-MS analysis, mixed samples exhibited reduced acidity, particularly evident in the bio-oil produced from a 75% Sargassum angastifolium blend, which exhibited approximately half the original acidity. FTIR analysis revealed key functional groups on the biochar surface, including O-H, CO, and C-O moieties. XRD and XRF analyses indicated the presence of alkali and alkaline earth metals in the biochar, while BET analysis showed a surface area ranging from 0.64 to 1.60 m2/g. The favorable characteristics of the products highlight the efficacy and cost-effectiveness of co-pyrolyzing terrestrial and marine biomass for the generation of biofuels and value-added commodities.

OptEnvelope: A target point guided method for growth-coupled production using knockouts.

Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a mathematic model of metabolism is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is presented based on finding minimal set of active reactions for a target point in the feasible solution space (envelope) using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space envelope in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable production envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results illustrate that OptEnvelope is capable to find multiple strong coupled envelopes located in the desired solution space because of its novel target point oriented strategy of envelope search. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.

Metabolic regulation boosts bioelectricity generation in Zymomonas mobilis microbial fuel cell, surpassing ethanol production.

Zymomonas mobilis (Z. mobilis), a bacterium known for its ethanol production capabilities, can also generate electricity by transitioning from ethanol production to electron generation. The purpose of this study is to investigate the ability of Z. mobilis to produce bioelectricity when utilized as a biocatalyst in a single-chamber microbial fuel cell (MFC). Given the bacterium's strong inclination towards ethanol production, a metabolic engineering strategy was devised to identify key reactions responsible for redirecting electrons from ethanol towards electricity generation. To evaluate the electroactivity of cultured Z. mobilis and its ethanol production in the presence of regulators, the reduction of soluble Fe(III) was utilized. Among the regulators tested, CuCl2 demonstrated superior effectiveness. Consequently, the MFC was employed to analyze the electrochemical properties of Z. mobilis using both a minimal and modified medium. By modifying the bacterial medium, the maximum current and power density of the MFC fed with Z. mobilis increased by more than 5.8- and sixfold, respectively, compared to the minimal medium. These findings highlight the significant impact of metabolic redirection in enhancing the performance of MFCs. Furthermore, they establish Z. mobilis as an active electrogenesis microorganism capable of power generation in MFCs.

Integrating gene expression data into a genome-scale metabolic model to identify reprogramming during adaptive evolution.

The development of a method for identifying latent reprogramming in gene expression data resulting from adaptive laboratory evolution (ALE) in response to genetic or environmental perturbations has been a challenge. In this study, a method called Metabolic Reprogramming Identifier (MRI), based on the integration of expression data to a genome-scale metabolic model has been developed. To identify key genes playing the main role in reprogramming, a MILP problem is presented and maximization of an adaptation score as a criterion indicating a pattern of using metabolism with maximum utilization of gene expression resources is defined as an objective function. Then, genes with complete expression usage and significant expression differences between wild-type and evolved strains were selected as key genes for reprogramming. This score is also applied to evaluate the compatibility of expression patterns with maximal use of key genes. The method was implemented to investigate the reprogramming of Escherichia coli during adaptive evolution caused by changing carbon sources. cyoC and cydB responsible for establishing proton gradient across the inner membrane were identified to be vital in the E. coli reprogramming when switching from glucose to lactate. These results indicate the importance of the inner membrane in reprogramming of E. coli to adapt to the new environment. The method predicts no reprogramming occurs during the evolution for growth on glycerol.

Regulatory-systemic approach in Aspergillus niger for bioleaching improvement by controlling precipitation.

In the context of e-waste recycling by fungal bioleaching, nickel and cobalt precipitate as toxic metals by oxalic acid, whereas organic acids, such as citric, act as a high-performance chelating agent in dissolving these metals. Oxalic acid elimination requires an excess and uneconomical carbon source concentration in culture media. To resolve this issue, a novel and straightforward systems metabolic engineering method was devised to switch metabolic flux from oxalic acid to citric acid. In this technique, the genome-scale metabolic model of Aspergillus niger was applied to predicting flux variability and key reactions through the calculation of multiple optimal solutions for cellular regulation. Accordingly, BRENDA regulators and a novel molecular docking-oriented approach were defined a regulatory medium for this end. Then, ligands were evaluated in fungal culture to assess their impact on organic acid production for bioleaching of copper and nickel from waste telecommunication printed circuit boards. The protein structure of oxaloacetate hydrolase was modeled based on homology modeling for molecular docking. Metformin, glutathione, and sodium fluoride were found to be effective as inhibitors of oxalic acid production, enabling the production of 8100 ppm citric acid by controlling cellular metabolism. Indirect bioleaching demonstrated that nickel did not precipitate, and the bioleaching efficiency of copper and nickel increased from 40% and 24% to 61% and 100%, respectively. Bioleaching efficiency was evaluated qualitatively by FE-SEM, EDX, mapping, and XRD analysis. KEY POINTS: • A regulatory-systemic procedure for controlling cellular metabolism was introduced • Metformin inhibited oxalic acid, leading to 8100 ppm citric acid production • Bioleaching of copper and nickel in TPCBs improved by 21% and 76.

Developing a metabolic model-based fed-batch feeding strategy for Pichia pastoris fermentation through fine-tuning of the methanol utilization pathway.

Pichia pastoris is a commonly used microbial host for recombinant protein production. It is mostly cultivated in fed-batch mode, in which the environment of the cell is continuously changing. Hence, it is vital to understand the influence of feeding strategy parameters on the intracellular reaction network to fine-tune bioreactor performance. This study used dynamic flux balance analysis (DFBA) integrated with transcriptomics data to simulate the recombinant P. pastoris (Muts ) growth during the induction phase for three fed-batch strategies, conducted at constant specific growth rates (µ-stat). The induction phase was split into equal time intervals, and the correlated reactions with protein yield were identified in the three fed-batch strategies using the Pearson correlation coefficient. Subsequently, principal component analysis (PCA) was applied to cluster induction phase time intervals and identify the role of correlated reactions on metabolic differentiation of time intervals. It was found that increasing fluxes through the methanol dissimilation pathway increased protein yield. By adding a methanol assimilation pathway inhibitor (HgCl2 ) to the shake flask medium growing on glycerol: methanol mixture (10%: 90%, v/v), the protein titre increased by 60%. As per DFBA, the higher the methanol to biomass flux ratio (Rmeoh/Δx ), the higher the protein yield. Finally, a novel feeding strategy was developed to increase the amount of Rmeoh/Δx compared to the three feeding strategies. The concentration of recombinant human growth hormone (rhGH), used as the model protein, increased by 16% compared to the optimal culture result obtained previously (800 mg L-1 to 928 mg L-1 ), while production yield improved by 85% (24.8 mg gDCW -1 to 46 mg gDCW -1 ).

Investigating ethanol production using the Zymomonas mobilis crude extract.

Cell-free systems have become valuable investigating tools for metabolic engineering research due to their easy access to metabolism without the interference of the membrane. Therefore, we applied Zymomonas mobilis cell-free system to investigate whether ethanol production is controlled by the genes of the metabolic pathway or is limited by cofactors. Initially, different glucose concentrations were added to the extract to determine the crude extract's capability to produce ethanol. Then, we investigated the genes of the metabolic pathway to find the limiting step in the ethanol production pathway. Next, to identify the bottleneck gene, a systemic approach was applied based on the integration of gene expression data on a cell-free metabolic model. ZMO1696 was determined as the bottleneck gene and an activator for its enzyme was added to the extract to experimentally assess its effect on ethanol production. Then the effect of NAD+ addition at the high concentration of glucose (1 M) was evaluated, which indicates no improvement in efficiency. Finally, the imbalance ratio of ADP/ATP was found as the controlling factor by measuring ATP levels in the extract. Furthermore, sodium gluconate as a carbon source was utilized to investigate the expansion of substrate consumption by the extract. 100% of the maximum theoretical yield was obtained at 0.01 M of sodium gluconate while it cannot be consumed by Z. mobilis. This research demonstrated the challenges and advantages of using Z. mobilis crude extract for overproduction.

Improvement of Lutein Production in Auxenochlorella protothecoides Using Its Genome-Scale Metabolic Model and a System-Oriented Approach.

Lutein is a valuable metabolite widely used in the food, pharmaceutical, cosmetic, and aquaculture industries. Marigold flowers are the most common source of commercial lutein, but cultivation area, weather conditions, and high manpower costs are among the disadvantages of lutein production from marigold flowers. Microalgae are an excellent alternative to plant sources of lutein as they do not have the limitations of plant extraction. Auxenochlorella protothecoides is a promising candidate for commercial production of lutein. In the present research, a genome-scale metabolic model was applied to introduce some strategies to improve lutein production in A. protothecoides. The effective reactions to improve lutein production were determined based on analysis of multiple optimal solutions. The enzymatic regulators of candidate reactions were identified using the BRENDA database. The effect of 13 activators was investigated experimentally. Our results showed that sodium citrate has the greatest effect on lutein production, so it was introduced as the most effective compound for increasing lutein production by A. protothecoides.

Cellular Genome-Scale Metabolic Modeling Identifies New Potential Drug Targets Against Hepatocellular Carcinoma.

Genome-scale metabolic modeling (GEM) is one of the key approaches to unpack cancer metabolism and for discovery of new drug targets. In this study, we report the Transcriptional Regulated Flux Balance Analysis-CORE (TRFBA-), an algorithm for GEM using key growth-correlated reactions using hepatocellular carcinoma (HCC), an important global health burden, as a case study. We generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to forecast potential drug targets for HCC. A total of 108 essential genes for growth were predicted by the TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterol, and steroid biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of nine drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Some of these drugs in this model performed better than Sorafenib, the first-line therapeutic against HCC. A HepG2 cell-specific GEM highlights sterol metabolism to be essential for cell growth. HSD11B2 downregulation results in lower cell growth. Most of the compounds, selected by drug repurposing approach, show a significant inhibitory effect on cell growth in a wide range of concentrations. These findings offer new molecular leads for drug discovery for hepatic cancer while also illustrating the importance of GEM and drug repurposing in cancer therapeutics innovation.

Investigating role of abiotic side and finding optimum abiotic condition for improving gas biodesulfurization using Thioalkalivibrio versutus.

Hydrogen sulfide (H2S) is a super toxic substance that produces SOx gases when combusted. Therefore, it should be removed from gas streams. Biodesulfurization is one of the developing methods for removing sulfide. Gas biodesulfurization must be accelerated to be competitive with chemical processes. This process has two sides: biotic and abiotic sides. To increase the rate of sulfide removal, this substance should be given to the bacteria in the maximum amount (Max. - RHS B). Therefore, it is necessary to minimize the rate of adverse abiotic reactions of sulfide (Min. - RHS A). Minimizing the sulfide reaction with biosulfur and oxygen and thiosulfate generation (Min. - RHS thio2) was assessed in de-microbized medium. It was concluded that the pH should be kept as low as possible. The kinetics of thiosulfate formation from sulfide oxidation (- RHS thio1) are strongly dependent on the sulfide concentration, and to minimize this reaction rate, sulfide should be gently injected into the culture. To minimize sulfide reduction to hydrogen sulfide (Min. - RHS rev), the pH should be kept as high as possible. Using the Design Expert v.13, a model was driven for the abiotic side to obtain optimum condition. The pH value was found to be 8.2 and the sulfide concentration to 2.5E-05 M. Thioalkalivibrio versutus cultivation under identified abiotic conditions resulted in biological removal of sulfide up to 1.5 g/h. The culture was not able to remove 2 g/h input sulfide, and to increase this, the biotic side should be studied.

Reconstruction of a generic genome-scale metabolic network for chicken: Investigating network connectivity and finding potential biomarkers.

Chicken is the first sequenced avian that has a crucial role in human life for its meat and egg production. Because of various metabolic disorders, study the metabolism of chicken cell is important. Herein, the first genome-scale metabolic model of a chicken cell named iES1300, consists of 2427 reactions, 2569 metabolites, and 1300 genes, was reconstructed manually based on KEGG, BiGG, CHEBI, UNIPROT, REACTOME, and MetaNetX databases. Interactions of metabolic genes for growth were examined for E. coli, S. cerevisiae, human, and chicken metabolic models. The results indicated robustness to genetic manipulation for iES1300 similar to the results for human. iES1300 was integrated with transcriptomics data using algorithms and Principal Component Analysis was applied to compare context-specific models of the normal, tumor, lean and fat cell lines. It was found that the normal model has notable metabolic flexibility in the utilization of various metabolic pathways, especially in metabolic pathways of the carbohydrate metabolism, compared to the others. It was also concluded that the fat and tumor models have similar growth metabolisms and the lean chicken model has a more active lipid and carbohydrate metabolism.

Valine feeding reduces ammonia production through rearrangement of metabolic fluxes in central carbon metabolism of CHO cells.

Ammonia is a toxic byproduct of CHO cell metabolism, which inhibits cell growth, reduces cell viability, alters glycosylation, and decreases recombinant protein productivity. In an attempt to minimize the ammonium accumulation in cell culture media, different amino acids were added individually to the culture medium before the production phase to alleviate the negative effects of ammonium on cell culture performance. Among all the amino acids examined in this study, valine showed the most positive impact on CHO cell culture performance. When the cultured CHO cells were fed with 5 mM valine, EPO titer was increased by 25% compared to the control medium, and ammonium and lactate production were decreased by 23 and 26%, respectively, relative to the control culture. Moreover, the sialic acid content of the EPO protein in valine-fed culture was higher than in the control culture, most likely because of the lower ammonium concentration. Flux balance analysis (FBA) results demonstrated that the citric acid cycle was enriched by valine feeding. The measurement of TCA cycle activity supported this finding. The analysis revealed that there might be a link between promoting tricarboxylic acid (TCA) cycle metabolism in valine-fed culture and reduction in lactate and ammonia accumulation. Furthermore, in valine-fed culture, FBA outcomes showed that alanine was excreted into the medium as the primary mechanism for reducing ammonium concentration. It was predicted that the elevated TCA cycle metabolism was concurrent with an increment in recombinant protein production. Taken together, our data demonstrate that valine addition could be an effective strategy for mitigating the negative impacts of ammonium and enhancing glycoprotein production in both quality and quantity. KEY POINTS: • Valine feeding can mitigate the negative impacts of ammonia on CHO cell growth. • Valine addition assists the ammonia removal mechanism by enriching the TCA cycle. • Ammonia is removed from the media through alanine excretion in valine-fed culture.

A system-oriented strategy to enhance electron production of Synechocystis sp. PCC6803 in bio-photovoltaic devices: experimental and modeling insights.

Bio-photovoltaic devices (BPVs) harness photosynthetic organisms to produce bioelectricity in an eco-friendly way. However, their low energy efficiency is still a challenge. A comprehension of metabolic constraints can result in finding strategies for efficiency enhancement. This study presents a systemic approach based on metabolic modeling to design a regulatory defined medium, reducing the intracellular constraints in bioelectricity generation of Synechocystis sp. PCC6803 through the cellular metabolism alteration. The approach identified key reactions that played a critical role in improving electricity generation in Synechocystis sp. PCC6803 by comparing multiple optimal solutions of minimal and maximal NADH generation using two criteria. Regulatory compounds, which controlled the enzyme activity of the key reactions, were obtained from the BRENDA database. The selected compounds were subsequently added to the culture media, and their effect on bioelectricity generation was experimentally assessed. The power density curves for different culture media showed the BPV fed by Synechocystis sp. PCC6803 suspension in BG-11 supplemented with NH4Cl achieved the maximum power density of 148.27 mW m-2. This produced power density was more than 40.5-fold of what was obtained for the BPV fed with cyanobacterial suspension in BG-11. The effect of the activators on BPV performance was also evaluated by comparing their overpotential, maximum produced power density, and biofilm morphology under different conditions. These findings demonstrated the crucial role of cellular metabolism in improving bioelectricity generation in BPVs.

An integrated modular framework for modeling the effect of ammonium on the sialylation process of monoclonal antibodies produced by CHO cells.

BACKGROUND: Monoclonal antibodies (mABs) have emerged as one of the most important therapeutic recombinant proteins in the pharmaceutical industry. Their immunogenicity and therapeutic efficacy are influenced by post-translational modifications, specifically the glycosylation process. Bioprocess conditions can influence the intracellular process of glycosylation. Among all the process conditions that have been recognized to affect the mAB glycoforms, the detailed mechanism underlying how ammonium could perturb glycosylation remains to be fully understood. It was shown that ammonium induces heterogeneity in protein glycosylation by altering the sialic acid content of glycoproteins. Hence, understanding this mechanism would aid pharmaceutical manufacturers to ensure consistent protein glycosylation. METHODS: Three different mechanisms have been proposed to explain how ammonium influences the sialylation process. In the first, the inhibition of CMP-sialic acid transporter, which transports CMP-sialic acid (sialylation substrate) into the Golgi, by an increase in UDP-GlcNAc content that is brought about by the augmented incorporation of ammonium into glucosamine formation. In the second, ammonia diffuses into the Golgi and raises its pH, thereby decreasing the sialyltransferase enzyme activity. In the third, the reduction of sialyltransferase enzyme expression level in the presence of ammonium. We employed these mechanisms in a novel integrated modular platform to link dynamic alteration in mAB sialylation process with extracellular ammonium concentration to elucidate how ammonium alters the sialic acid content of glycoproteins. RESULTS: Our results show that the sialylation reaction rate is insensitive to the first mechanism. At low ammonium concentration, the second mechanism is the controlling mechanism in mAB sialylation and by increasing the ammonium level (< 8 mM) the third mechanism becomes the controlling mechanism. At higher ammonium concentrations (> 8 mM) the second mechanism becomes predominant again. CONCLUSION: The presented model in this study provides a connection between extracellular ammonium and the monoclonal antibody sialylation process. This computational tool could help scientists to develop and formulate cell culture media. The model illustrated here can assist the researchers to select culture media that ensure consistent mAB sialylation.

Improving ethanol production by studying the effect of pH using a modified metabolic model and a systemic approach.

pH is an important factor affecting the growth and production of microorganisms; especially, its effect on ethanologenic microorganisms. It can change the ionization state of metabolites via the change in the charge of their functional groups that may lead to metabolic alteration. Here, we estimated the ionization state of metabolites and balanced the charge of reactions in genome-scale metabolic models of Saccharomyces cerevisiae, Escherichia coli, and Zymomonas mobilis at pH levels 5, 6, and 7. The robustness analysis was first implemented to anticipate the effect of proton exchange flux on growth rates for the constructed metabolic models at various pH. In accordance with previous experimental reports, the models predict that Z. mobilis is more sensitive to pH rather than S. cerevisiae and the yeast is more regulated by pH rather than E. coli. Then, a systemic approach was proposed to predict the pH effect on metabolic change and to find effective reactions on ethanol production in S. cerevisiae. The correlated reactions with ethanol production at predicted optimal pH in a range of proton exchange rates determined by robustness analysis were identified using the Pearson correlation coefficient. Then, fluxes of these reactions were applied to cluster the various pHs by principal component analysis and to identify the role of these reactions on metabolic differentiation because of pH change. Finally, 12 reactions were selected for up and downregulation to improve ethanol production. Enzyme regulators of the selected reactions were identified using the BRENDA database and 11 selected regulators were screened and optimized via Plackett-Burman and two-level full factorial designs, respectively. The proposed approach has enhanced yields of ethanol from 0.18 to 0.36 mol/mol carbon. Hence, not only a comprehensive approach for understanding the effect of pH on metabolism was proposed in this study, but also it successfully introduced key manipulations for ethanol overproduction.

A systematic strategy using a reconstructed genome-scale metabolic network for pathogen Streptococcuspneumoniae D39 to find novel potential drug targets.

Streptococcus pneumoniae is a Gram-positive bacterium that is one of the major causes of various infections such as pneumonia, meningitis, otitis media and endocarditis. Since antibiotic resistance of S. pneumoniae is pointed out as a challenge in the treatment of these infections, more studies are required to focus on disease prevention. In this research, a first manually curated genome-scale metabolic network of the pathogen S. pneumoniae D39 was reconstructed based on its genome annotation data, and biochemical knowledge from literature and databases. The model was validated by amino acid auxotrophies, gene essentiality analysis, and different carbohydrate sources. Then, a two-stage strategy was developed to find target genes for growth reduction of the pathogen and their importance in the various infection sites. In the first stage, growth-associated genes were identified by integration of transcriptomic data with the model and in the second stage, the importance of each gene in the metabolism for growth was evaluated using principal component analysis. The reports presented in the literature confirm the effect of some found genes on the growth of S. pneumoniae.

ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities.

The development of new methods capable of more realistic modeling of microbial communities necessitates that their results be quantitatively comparable with experimental findings. In this research, a new integrated agent and constraint based modeling framework abbreviated ACBM has been proposed that integrates agent-based and constraint-based modeling approaches. ACBM models the cell population in three-dimensional space to predict spatial and temporal dynamics and metabolic interactions. When used to simulate the batch growth of C. beijerinckii and two-species communities of F. prausnitzii and B. adolescent., ACBM improved on predictions made by two previous models. Furthermore, when transcriptomic data were integrated with a metabolic model of E. coli to consider intracellular constraints in the metabolism, ACBM accurately predicted growth rate, half-rate constant, and concentration of biomass, glucose, and acidic products over time. The results also show that the framework was able to predict the metabolism changes in the early stationary compared to the log phase. Finally, ACBM was implemented to estimate starved cells under heterogeneous feeding and it was concluded that a percentage of cells are always subject to starvation in a bioreactor with high volume.

Reconstruction, verification and in-silico analysis of a genome-scale metabolic model of bacterial cellulose producing Komagataeibacter xylinus.

In this study, a comprehensive genome-scale metabolic network of Komagataeibacter xylinus as the model microorganism was reconstructed based on genome annotation, for better understanding of metabolic growth and biosynthesis of bacterial cellulose (BC). The reconstructed network included 640 genes, 783 metabolic reactions and 865 metabolites. The model was completely successful to predict the lack of growth under anaerobic conditions. Model validation by the data for the growth of acetic acid bacteria with ethanol-limited chemostat cultures showed that there is a good agreement for the O2 and CO2 fluxes with actual growth conditions. Then the model was used to forecast the simultaneous production of BC and by-products. The obtained data showed that the rate of BC production is consistent with experimental data with an accuracy of 93.7%. Finally, the study of flux balance analysis (FBA) data showed that the pentose phosphate pathway and the TCA cycle play an important role in growth-promoting metabolism in K. xylinus and have a close relationship with BC biosynthesis. By integrating this model with various metabolic engineering and systems biology tools in the future, it is possible to overcome the common challenges in the large-scale BC production, such as low yield and productivity.

Reconstruction of a regulated two-cell metabolic model to study biohydrogen production in a diazotrophic cyanobacterium Anabaena variabilis ATCC 29413.

Anabaena variabilis is a diazotrophic filamentous cyanobacterium that differentiates to heterocysts and produces hydrogen as a byproduct. Study on metabolic interactions of the two differentiated cells provides a better understanding of its metabolism especially for improving hydrogen production. To this end, a genome-scale metabolic model for Anabaena variabilis ATCC 29413, iAM957, was reconstructed and evaluated in this research. Then, the model and transcriptomic data of the vegetative and heterocyst cells were applied to construct a regulated two-cell metabolic model. The regulated model improved prediction for biomass in high radiation levels. The regulated model predicts that heterocysts provide an oxygen-free environment and then, this model was used to find strategies for improving hydrogen production in heterocysts. The predictions indicate that the removal of uptake hydrogenase improves hydrogen production which is consistent with previous empirical research. Furthermore, the regulated model proposed activation of some reactions to provide redox cofactors which are required for improving hydrogen production up to 60% by bidirectional hydrogenase.

A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism.

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.