A custom 3D printed paddlewheel improves growth in flat panel photobioreactor.

One of the main challenges with using flat panel photobioreactors for algal growth is uneven mixing and settling of cells in corners, especially when bubbling is the only method used for mixing. In order to improve mixing in our flat panel reactor, we designed a custom paddlewheel. Paddlewheels are frequently used in outdoor algae raceway ponds to improve mixing and we are taking advantage of the same principle for mixing in the reactor. The paddlewheel is easily integrated into our PSI FMT150 1-L flat panel photobioreactor and is printed on a 3D printer using high temperature poly lactic acid (HT-PLA). With the inclusion of an annealing step, the paddlewheel is autoclavable. Addition of the paddlewheel in the reactor minimized cell settling and improved algal growth, as evidenced by a nearly 40% increase in oxygen production rates. Nutrient dispersion and utilization in the culture was also improved as evidenced by a corresponding 38% decrease in CO2 concentration. The paddlewheel device presented here is a cost-effective method for improving algal growth in a flat panel photobioreactor.

Investigating the Unique Ability of Trichodesmium To Fix Carbon and Nitrogen Simultaneously Using MiMoSA.

The open ocean is an extremely competitive environment, partially due to the dearth of nutrients. Trichodesmium erythraeum, a marine diazotrophic cyanobacterium, is a keystone species in the ocean due to its ability to fix nitrogen and leak 30 to 50% into the surrounding environment, providing a valuable source of a necessary macronutrient to other species. While there are other diazotrophic cyanobacteria that play an important role in the marine nitrogen cycle, Trichodesmium is unique in its ability to fix both carbon and nitrogen simultaneously during the day without the use of specialized cells called heterocysts to protect nitrogenase from oxygen. Here, we use the advanced modeling framework called multiscale multiobjective systems analysis (MiMoSA) to investigate how Trichodesmium erythraeum can reduce dimolecular nitrogen to ammonium in the presence of oxygen. Our simulations indicate that nitrogenase inhibition is best modeled as Michealis-Menten competitive inhibition and that cells along the filament maintain microaerobia using high flux through Mehler reactions in order to protect nitrogenase from oxygen. We also examined the effect of location on metabolic flux and found that cells at the end of filaments operate in distinctly different metabolic modes than internal cells despite both operating in a photoautotrophic mode. These results give us important insight into how this species is able to operate photosynthesis and nitrogen fixation simultaneously, giving it a distinct advantage over other diazotrophic cyanobacteria because they can harvest light directly to fuel the energy demand of nitrogen fixation. IMPORTANCE Trichodesmium erythraeum is a marine cyanobacterium responsible for approximately half of all biologically fixed nitrogen, making it an integral part of the global nitrogen cycle. Interestingly, unlike other nitrogen-fixing cyanobacteria, Trichodesmium does not use temporal or spatial separation to protect nitrogenase from oxygen poisoning; instead, it operates photosynthesis and nitrogen fixation reactions simultaneously during the day. Unfortunately, the exact mechanism the cells utilize to operate carbon and nitrogen fixation simultaneously is unknown. Here, we use an advanced metabolic modeling framework to investigate and identify the most likely mechanisms Trichodesmium uses to protect nitrogenase from oxygen. The model predicts that cells operate in a microaerobic mode, using both respiratory and Mehler reactions to dramatically reduce intracellular oxygen concentrations.

Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas.

Economical production of photosynthetic organisms requires the use of natural day/night cycles. These induce strong circadian rhythms that lead to transient changes in the cells, requiring complex modeling to capture. In this study, we coupled times series transcriptomic data from the model green alga Chlamydomonas reinhardtii to a metabolic model of the same organism in order to develop the first transient metabolic model for diurnal growth of algae capable of predicting phenotype from genotype. We first transformed a set of discrete transcriptomic measurements (D. Strenkert, S. Schmollinger, S. D. Gallaher, P. A. Salomé, et al., Proc Natl Acad Sci U S A 116:2374-2383, 2019, https://doi.org/10.1073/pnas.1815238116) into continuous curves, producing a complete database of the cell's transcriptome that can be interrogated at any time point. We also decoupled the standard biomass formation equation to allow different components of biomass to be synthesized at different times of the day. The resulting model was able to predict qualitative phenotypical outcomes of a starchless mutant. We also extended this approach to simulate all single-knockout mutants and identified potential targets for rational engineering efforts to increase productivity. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition, and intracellular fluxes for diurnal growth. IMPORTANCE We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii. The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.

Isotopically nonstationary 13C metabolic flux analysis in resting and activated human platelets.

Platelet metabolism is linked to platelet hyper- and hypoactivity in numerous human diseases. Developing a detailed understanding of the link between metabolic shifts and platelet activation state is integral to improving human health. Here, we show the first application of isotopically nonstationary 13C metabolic flux analysis to quantitatively measure carbon fluxes in both resting and thrombin activated platelets. Metabolic flux analysis results show that resting platelets primarily metabolize glucose to lactate via glycolysis, while acetate is oxidized to fuel the tricarboxylic acid cycle. Upon activation with thrombin, a potent platelet agonist, platelets increase their uptake of glucose 3-fold. This results in an absolute increase in flux throughout central metabolism, but when compared to resting platelets they redistribute carbon dramatically. Activated platelets decrease relative flux to the oxidative pentose phosphate pathway and TCA cycle from glucose and increase relative flux to lactate. These results provide the first report of reaction-level carbon fluxes in platelets and allow us to distinguish metabolic fluxes with much higher resolution than previous studies.

Evaluation of quenching methods for metabolite recovery in photoautotrophic Synechococcus sp. PCC 7002.

The first step of many metabolomics studies is quenching, a technique vital for rapidly halting metabolism and ensuring that the metabolite profile remains unchanging during sample processing. The most widely used approach is to plunge the sample into prechilled cold methanol; however, this led to significant metabolite loss in Synecheococcus sp. PCC 7002. Here we describe our analysis of the impacts of cold methanol quenching on the model marine cyanobacterium Synechococcus sp. PCC 7002, as well as our brief investigation of alternative quenching methods. We tested several methods including cold methanol, cold saline, and two filtration approaches. Targeted central metabolites were extracted and metabolomic profiles were generated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results indicate that cold methanol quenching induces dramatic metabolite leakage in Synechococcus, resulting in a majority of central metabolites being lost prior to extraction. Alternatively, usage of a chilled saline quenching solution mitigates metabolite leakage and improves sample recovery without sacrificing rapid quenching of cellular metabolism. Finally, we illustrate that metabolite leakage can be assessed, and subsequently accounted for, in order to determine absolute metabolite pool sizes; however, our results show that metabolite leakage is inconsistent across various metabolite pools and therefore must be determined for each individually measured metabolite.

Multiscale Multiobjective Systems Analysis (MiMoSA): an advanced metabolic modeling framework for complex systems.

In natural environments, cells live in complex communities and experience a high degree of heterogeneity internally and in the environment. Even in 'ideal' laboratory environments, cells can experience a high degree of heterogeneity in their environments. Unfortunately, most of the metabolic modeling approaches that are currently used assume ideal conditions and that each cell is identical, limiting their application to pure cultures in well-mixed vessels. Here we describe our development of Multiscale Multiobjective Systems Analysis (MiMoSA), a metabolic modeling approach that can track individual cells in both space and time, track the diffusion of nutrients and light and the interaction of cells with each other and the environment. As a proof-of concept study, we used MiMoSA to model the growth of Trichodesmium erythraeum, a filamentous diazotrophic cyanobacterium which has cells with two distinct metabolic modes. The use of MiMoSA significantly improves our ability to predictively model metabolic changes and phenotype in more complex cell cultures.

The challenge and potential of photosynthesis: unique considerations for metabolic flux measurements in photosynthetic microorganisms.

Photosynthetic microorganisms have the potential for sustainable production of chemical feedstocks and products but have had limited success due to a lack of tools and deeper understanding of metabolic pathway regulation. The application of instationary metabolic flux analysis (INST-MFA) to photosynthetic microorganisms has allowed researchers to quantify fluxes and identify bottlenecks and metabolic inefficiencies to improve strain performance or gain insight into cellular physiology. Additionally, flux measurements can also highlight deviations between measured and predicted fluxes, revealing weaknesses in metabolic models and highlighting areas where a lack of understanding still exists. In this review, we outline the experimental steps necessary to successfully perform photosynthetic flux experiments and analysis. We also discuss the challenges unique to photosynthetic microorganisms and how to account for them, including: light supply, quenching, concentration, extraction, analysis, and flux calculation. We hope that this will enable a larger number of researchers to successfully apply isotope assisted metabolic flux analysis (13C-MFA) to their favorite photosynthetic organism.

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii.

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.

The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum.

BACKGROUND: Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition. RESULTS: We created a genome-scale reconstruction of T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ. CONCLUSION: The genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.

Omics in Chlamydomonas for Biofuel Production.

In response to demands for sustainable domestic fuel sources, research into biofuels has become increasingly important. Many challenges face biofuels in their effort to replace petroleum fuels, but rational strain engineering of algae and photosynthetic organisms offers a great deal of promise. For decades, mutations and stress responses in photosynthetic microbiota were seen to result in production of exciting high-energy fuel molecules, giving hope but minor capability for design. However, '-omics' techniques for visualizing entire cell processing has clarified biosynthesis and regulatory networks. Investigation into the promising production behaviors of the model organism C. reinhardtii and its mutants with these powerful techniques has improved predictability and understanding of the diverse, complex interactions within photosynthetic organisms. This new equipment has created an exciting new frontier for high-throughput, predictable engineering of photosynthetically produced carbon-neutral biofuels.

Genome Engineering in Cyanobacteria: Where We Are and Where We Need To Go.

Genome engineering of cyanobacteria is a promising area of development in order to produce fuels, feedstocks, and value-added chemicals in a sustainable way. Unfortunately, the current state of genome engineering tools for cyanobacteria lags far behind those of model organisms such as Escherichia coli and Saccharomyces cerevisiae. In this review, we present the current state of synthetic biology tools for genome engineering efforts in the most widely used cyanobacteria strains and areas that need concerted research efforts to improve tool development. Cyanobacteria pose unique challenges to genome engineering efforts because their cellular biology differs significantly from other eubacteria; therefore, tools developed for other genera are not directly transferrable. Standardized parts, such as promoters and ribosome binding sites, which control gene expression, require characterization in cyanobacteria in order to have fully predictable results. The application of these tools to genome engineering efforts is also discussed; the ability to do genome-wide searching and to introduce multiple mutations simultaneously is an area that needs additional research in order to enable fast and efficient strain engineering.

Multiplexed tracking of combinatorial genomic mutations in engineered cell populations.

Multiplexed genome engineering approaches can be used to generate targeted genetic diversity in cell populations on laboratory timescales, but methods to track mutations and link them to phenotypes have been lacking. We present an approach for tracking combinatorial engineered libraries (TRACE) through the simultaneous mapping of millions of combinatorially engineered genomes at single-cell resolution. Distal genomic sites are assembled into individual DNA constructs that are compatible with next-generation sequencing strategies. We used TRACE to map growth selection dynamics for Escherichia coli combinatorial libraries created by recursive multiplex recombineering at a depth 10(4)-fold greater than before. TRACE was used to identify genotype-to-phenotype correlations and to map the evolutionary trajectory of two individual combinatorial mutants in E. coli. Combinatorial mutations in the human ES2 ovarian carcinoma cell line were also assessed with TRACE. TRACE completes the combinatorial engineering cycle and enables more sophisticated approaches to genome engineering in both bacteria and eukaryotic cells than are currently possible.

Nitrogen-Sparing Mechanisms in Chlamydomonas Affect the Transcriptome, the Proteome, and Photosynthetic Metabolism.

Nitrogen (N) is a key nutrient that limits global primary productivity; hence, N-use efficiency is of compelling interest in agriculture and aquaculture. We used Chlamydomonas reinhardtii as a reference organism for a multicomponent analysis of the N starvation response. In the presence of acetate, respiratory metabolism is prioritized over photosynthesis; consequently, the N-sparing response targets proteins, pigments, and RNAs involved in photosynthesis and chloroplast function over those involved in respiration. Transcripts and proteins of the Calvin-Benson cycle are reduced in N-deficient cells, resulting in the accumulation of cycle metabolic intermediates. Both cytosolic and chloroplast ribosomes are reduced, but via different mechanisms, reflected by rapid changes in abundance of RNAs encoding chloroplast ribosomal proteins but not cytosolic ones. RNAs encoding transporters and enzymes for metabolizing alternative N sources increase in abundance, as is appropriate for the soil environmental niche of C. reinhardtii. Comparison of the N-replete versus N-deplete proteome indicated that abundant proteins with a high N content are reduced in N-starved cells, while the proteins that are increased have lower than average N contents. This sparing mechanism contributes to a lower cellular N/C ratio and suggests an approach for engineering increased N-use efficiency.

Systems-level analysis of nitrogen starvation-induced modifications of carbon metabolism in a Chlamydomonas reinhardtii starchless mutant.

To understand the molecular basis underlying increased triacylglycerol (TAG) accumulation in starchless (sta) Chlamydomonas reinhardtii mutants, we undertook comparative time-course transcriptomics of strains CC-4348 (sta6 mutant), CC-4349, a cell wall-deficient (cw) strain purported to represent the parental STA6 strain, and three independent STA6 strains generated by complementation of sta6 (CC-4565/STA6-C2, CC-4566/STA6-C4, and CC-4567/STA6-C6) in the context of N deprivation. Despite N starvation-induced dramatic remodeling of the transcriptome, there were relatively few differences (5 × 10(2)) observed between sta6 and STA6, the most dramatic of which were increased abundance of transcripts encoding key regulated or rate-limiting steps in central carbon metabolism, specifically isocitrate lyase, malate synthase, transaldolase, fructose bisphosphatase and phosphoenolpyruvate carboxykinase (encoded by ICL1, MAS1, TAL1, FBP1, and PCK1 respectively), suggestive of increased carbon movement toward hexose-phosphate in sta6 by upregulation of the glyoxylate pathway and gluconeogenesis. Enzyme assays validated the increase in isocitrate lyase and malate synthase activities. Targeted metabolite analysis indicated increased succinate, malate, and Glc-6-P and decreased Fru-1,6-bisphosphate, illustrating the effect of these changes. Comparisons of independent data sets in multiple strains allowed the delineation of a sequence of events in the global N starvation response in C. reinhardtii, starting within minutes with the upregulation of alternative N assimilation routes and carbohydrate synthesis and subsequently a more gradual upregulation of genes encoding enzymes of TAG synthesis. Finally, genome resequencing analysis indicated that (1) the deletion in sta6 extends into the neighboring gene encoding respiratory burst oxidase, and (2) a commonly used STA6 strain (CC-4349) as well as the sequenced reference (CC-503) are not congenic with respect to sta6 (CC-4348), underscoring the importance of using complemented strains for more rigorous assignment of phenotype to genotype.

Genome-wide identification of genes conferring energy related resistance to a synthetic antimicrobial peptide (Bac8c).

A fundamental issue in the design and development of antimicrobials is the lack of understanding of complex modes of action and how this complexity affects potential pathways for resistance evolution. Bac8c (RIWVIWRR-NH(2)) is an 8 amino acid antimicrobial peptide (AMP) that has been shown to have enhanced activity against a range of pathogenic Gram-positive and Gram-negative bacteria, as well as yeast. We have previously demonstrated that Bac8c appears to interfere with multiple targets, at least in part through the disruption of cytoplasmic membrane related functions, and that resistance to this peptide does not easily develop using standard laboratory methods. Here, we applied a genomics approach, SCalar Analysis of Library Enrichement (SCALEs), to map the effect of gene overexpression onto Bac8c resistance in parallel for all genes and gene combinations (up to ∼ 10 adjacent genes) in the E. coli genome (a total of ∼ 500,000 individual clones were mapped). Our efforts identified an elaborate network of genes for which overexpression leads to low-level resistance to Bac8c (including biofilm formation, multi-drug transporters, etc). This data was analyzed to provide insights into the complex relationships between mechanisms of action and potential routes by which resistance to this synthetic AMP can develop.

Engineering improved ethanol production in Escherichia coli with a genome-wide approach.

A key challenge to the commercial production of commodity chemical and fuels is the toxicity of such molecules to the microbial host. While a number of studies have attempted to engineer improved tolerance for such compounds, the majority of these studies have been performed in wild-type strains and culturing conditions that differ considerably from production conditions. Here we applied the multiscalar analysis of library enrichments (SCALEs) method and performed a growth selection in an ethanol production system to quantitatively map in parallel all genes in the genome onto ethanol tolerance and production. In order to perform the selection in an ethanol-producing system, we used a previously engineered Escherichia coli ethanol production strain (LW06; ATCC BAA-2466) (Woodruff et al., in press), as the host strain for the multiscalar genomic library analysis (>10(6) clones for each library of 1, 2, or 4kb overlapping genomic fragments). By testing individually selected clones, we confirmed that growth selections enriched for clones with both improved ethanol tolerance and production phenotypes. We performed combinatorial testing of the top genes identified (uspC, otsA, otsB) to investigate their ability to confer improved ethanol tolerance or ethanol production. We determined that overexpression of otsA was required for improved tolerance and productivity phenotypes, with the best performing strains showing up to 75% improvement relative to the parent production strain.

Recombineering to homogeneity: extension of multiplex recombineering to large-scale genome editing.

Recombineering has been an essential tool for genetic engineering in microbes for many years and has enabled faster, more efficient engineering than previous techniques. There have been numerous studies that focus on improving recombineering efficiency, which can be divided into three main areas: (i) optimizing the oligo used for recombineering to enhance replication fork annealing and limit proofreading; (ii) mechanisms to modify the replisome itself, enabling an increased rate of annealing; and (iii) multiplexing recombineering targets and automation. These efforts have increased the efficiency of recombineering several hundred-fold. One area that has received far less attention is the problem of multiple chromosomes, which effectively decrease efficiency on a chromosomal basis, resulting in more sectored colonies, which require longer outgrowth to obtain clonal populations. Herein, we describe the problem of multiple chromosomes, discuss calculations predicting how many generations are needed to obtain a pure colony, and how changes in experimental procedure or genetic background can minimize the effect of multiple chromosomes.

Three acyltransferases and nitrogen-responsive regulator are implicated in nitrogen starvation-induced triacylglycerol accumulation in Chlamydomonas.

Algae have recently gained attention as a potential source for biodiesel; however, much is still unknown about the biological triggers that cause the production of triacylglycerols. We used RNA-Seq as a tool for discovering genes responsible for triacylglycerol (TAG) production in Chlamydomonas and for the regulatory components that activate the pathway. Three genes encoding acyltransferases, DGAT1, DGTT1, and PDAT1, are induced by nitrogen starvation and are likely to have a role in TAG accumulation based on their patterns of expression. DGAT1 and DGTT1 also show increased mRNA abundance in other TAG-accumulating conditions (minus sulfur, minus phosphorus, minus zinc, and minus iron). Insertional mutants, pdat1-1 and pdat1-2, accumulate 25% less TAG compared with the parent strain, CC-4425, which demonstrates the relevance of the trans-acylation pathway in Chlamydomonas. The biochemical functions of DGTT1 and PDAT1 were validated by rescue of oleic acid sensitivity and restoration of TAG accumulation in a yeast strain lacking all acyltransferase activity. Time course analyses suggest than a SQUAMOSA promoter-binding protein domain transcription factor, whose mRNA increases precede that of lipid biosynthesis genes like DGAT1, is a candidate regulator of the nitrogen deficiency responses. An insertional mutant, nrr1-1, accumulates only 50% of the TAG compared with the parental strain in nitrogen-starvation conditions and is unaffected by other nutrient stresses, suggesting the specificity of this regulator for nitrogen-deprivation conditions.

Tools for genome-wide strain design and construction.

Advances in DNA sequencing and synthesis technologies concurrent with the development of new recombinant DNA approaches have enabled the extension of directed evolution algorithms to the genome-scale. It is now possible to simultaneously map the effect of mutation(s) in each and every gene in the genome onto almost any screenable or selectable phenotype in less than a week. Such maps can be used to direct the design and construction of libraries containing billions of rationally designed combinatorial mutations. Such combinatorial libraries can now also be created and evaluated in less than a week. The review presents and discusses these new technologies within the context of directed evolution and inverse metabolic engineering.

Computation of metabolic fluxes and efficiencies for biological carbon dioxide fixation.

With rising energy prices and concern over the environmental impact of fossil fuel consumption, the push to develop biomass derived fuels has increased significantly. Although most global carbon fixation occurs via the Calvin Benson Bassham cycle, there are currently five other known pathways for carbon fixation; the goal of this study was to determine the thermodynamic efficiencies of all six carbon fixation pathways for the production of biomass using flux balance analysis. The three chemotrophic pathways, the reductive acetyl-CoA pathway, the 3-hydroxypropionate/4-hydroxybutyrate cycle and the dicarboxylate/4-hydroxybutyrate cycle, were found to be more efficient than photoautotrophic carbon fixation pathways. However, as hydrogen is not freely available, the energetic cost of hydrogen production from sunlight was calculated and included in the overall energy demand, which results in a 5 fold increase in the energy demand of chemoautotrophic carbon fixation. Therefore, when the cost of hydrogen production is included, photoautotrophic pathways are more efficient. However, the energetic cost for the production of 12 metabolic precursors was found to vary widely across the different carbon fixation pathways; therefore, different pathways may be more efficient at producing products from a single precursor than others. The results of this study have significant impact on the selection or design of autotrophic organisms for biofuel or biochemical production. Overall biomass production from solar energy is most efficient in organisms using the reductive TCA cycle, however, products derived from one metabolic precursor may be more efficiently produced using other carbon fixation pathways.