A genome-scale metabolic model of Methanococcus maripaludis S2 for CO2 capture and conversion to methane.

Methane is a major energy source for heating and electricity. Its production by methanogenic bacteria is widely known in nature. M. maripaludis S2 is a fully sequenced hydrogenotrophic methanogen and an excellent laboratory strain with robust genetic tools. However, a quantitative systems biology model to complement these tools is absent in the literature. To understand and enhance its methanogenesis from CO2, this work presents the first constraint-based genome-scale metabolic model (iMM518). It comprises 570 reactions, 556 distinct metabolites, and 518 genes along with gene-protein-reaction (GPR) associations, and covers 30% of open reading frames (ORFs). The model was validated using biomass growth data and experimental phenotypic studies from the literature. Its comparison with the in silico models of Methanosarcina barkeri, Methanosarcina acetivorans, and Sulfolobus solfataricus P2 shows M. maripaludis S2 to be a better organism for producing methane. Using the model, genes essential for growth were identified, and the efficacies of alternative carbon, hydrogen and nitrogen sources were studied. The model can predict the effects of reengineering M. maripaludis S2 to guide or expedite experimental efforts.

In silico modeling and evaluation of Gordonia alkanivorans for biodesulfurization.

The genus Gordonia is well known for its catabolic diversity and ability to transform several compounds including the various recalcitrant polyaromatic sulfur heterocycles (PASHs) found in the fossil fuels. In fact, some strains offer the unique ability to desulfurize even benzothiophene (BT) and other thiophenic compounds, which most of the commonly studied rhodococci strains cannot. In this work, we present the first genome scale metabolic model for G. alkanivorans, a desulfurizing strain, to enable a holistic study of its metabolism and comparison with R. erythropolis. Our model consists of 881 unique metabolites and 922 reactions associated with 568 ORFs/genes and 544 unique enzymes. It successfully predicts the growth rates from experimental studies and quantitatively elucidates the pathways for the desulfurization of the commonly studied sulfur compounds, namely dibenzothiophene (DBT) and benzothiophene (BT). Using our model, we identify the minimal media for G. alkanivorans, and show the significant effect of carbon sources on desulfurization with ethanol as the best source. Our model shows that the sulfur-containing amino acids such as cysteine and methionine decrease desulfurization activity, and G. alkanivorans prefers BT over DBT as a sulfur source. It also suggests that this preference may be driven by the lower NADH requirements for BT metabolism rather than the higher affinity of the transport system for BT. Our in silico comparison of R. erythropolis and G. alkanivorans suggests the latter to be a better desulfurizing strain due to its versatility for both BT and DBT, higher desulfurization activity, and higher growth rate.

Roles of sulfite oxidoreductase and sulfite reductase in improving desulfurization by Rhodococcus erythropolis.

Rhodococcus erythropolis has been widely studied for desulfurization. However, activity levels required for commercial application have not been achieved. A major limitation of the current work in biodesulfurization is inadequate information regarding sulfur metabolism generally, and in particular the metabolism of the sulfur obtained from dibenzothiophene (DBT) metabolism via the 4S pathway. In this work, we have investigated the possible routes taken by the sulfur from DBT to convert into biomass or other metabolites. We propose two alternate hypotheses. In the first, we hypothesize that the cell can convert via sulfite reductase (SR) the sulfite from the metabolism of DBT into sulfide that can be assimilated into biomass. However, in the process, it may convert any excess sulfite into extracellular sulfate via sulfite oxidoreductase (SOR) to avoid the toxic effects of sulfite. In the second, we speculate that the cell cannot assimilate the sulfite directly into biomass via SR. It must first use SOR to produce extracellular sulfate, and then recapture that sulfate into biomass via SR. Thus, either way, we propose that SOR and SR activities, in addition to dsz genes and cofactors, may be critical in increasing desulfurization levels significantly. In particular, we suggest that the simultaneous increase in SOR activity and decrease in SR activity can enable increased desulfurization activity.

Reconstruction of a genome-scale metabolic network of Rhodococcus erythropolis for desulfurization studies.

The remarkable catabolic diversity of Rhodococcus erythropolis makes it an interesting organism for bioremediation and fuel desulfurization. However, a model that can describe and explain the combined influence of various intracellular metabolic activities on its desulfurizing capabilities is missing from the literature. Such a model can greatly aid the development of R. erythropolis as an effective desulfurizing biocatalyst. This work reports the reconstruction of the first genome-scale metabolic model for R. erythropolis using the available genomic, experimental, and biochemical information. We have validated our in silico model by successfully predicting cell growth results and explaining several experimental observations in the literature on biodesulfurization using dibenzothiophene. We report several in silico experiments and flux balance analyses to propose minimal media, determine gene and reaction essentiality, and compare effectiveness of carbon, nitrogen, and sulfur sources. We demonstrate the usefulness of our model by studying a few in silico mutants of R. erythropolis for improved biodesulfurization, and comparing the desulfurization abilities of R. erythropolis with an in silico mutant of E. coli.

Modeling and Monte Carlo simulation of TCDD transport in a river.

A one-dimensional water quality model to assess the long-term fate of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in three compartments (water, sediment, fish) of a river has been developed using the literature data on various model parameters. The transient deterministic model with constant or nonrandom parameters is solved numerically by the method of orthogonal collocation, while an analytical solution is developed for the steady-state model. The impact of uncertainty in several model parameters has been studied by means of Monte Carlo simulations assuming that the uncertain parameters are uncorrelated and can be modeled by three probability distributions (uniform, normal and lognormal). For the case of a high TCDD discharge into a small, shallow river, we find that the maximum TCDD contents of water and fish are well below the prescribed safe limits. We also find that the effects of uncertainty on water quality metrics are quite complex or nonintuitive and can be substantial. This is especially true for TCDD in fish, which can be higher by as much as 50-70% than the deterministic predictions, if the parameter uncertainties follow uniform distributions.