Arthropods and biofuel production systems in North America

Abstract Biomass harvest may eventually be conducted on over 100 000 000 ha of US crop and forest lands to meet federally-mandated targets for renewable biofuels. Such large-scale land use changes could profoundly impact working landscapes and the arthropod communities that inhabit them. We review the literature on dedicated biofuel crops and biomass harvest from forests to look for commonalities in arthropod community responses. With expanded biofuel production, existing arthropod pests of biofuel crops will likely become more important and new pests will emerge. Beneficial arthropods will also be influenced by biofuel crop habitats, potentially altering the distribution of pollination and pest control services to the surrounding landscape. Production of biofuel crops including initial crop selection, genetic improvement, agronomic practices, and harvest regimes will also influence arthropod communities. In turn, arthropods will impact the productivity and species composition of biomass production systems. Some of these processes have the potential to cause landscape-level changes in arthropod community dynamics and insect-vectored plant diseases. Finally, changes in arthropod populations and their spatiotemporal distribution in the landscape will have impacts on consumers of insects at higher trophic levels, potentially influencing their population and community dynamics and producing feedbacks to arthropod communities. Given that dedicated biofuel crops and intensified biomass harvest from forests are still relatively uncommon in North America, as they increase, we anticipate ‘predictably unpredictable’ shifts in arthropod communities and the ecosystem services and functions they support. We suggest that research on arthropod dynamics within biofuel crops, their spillover into adjacent habitats, and implications for the sustainability of working landscapes are critical topics for both basic and applied investigations.     

Methods of energy extraction from microalgal biomass: a review

The potential of algal biomass as a source of liquid and gaseous biofuels is a highly topical theme, The process operations for algal biofuel production can be grouped into three areas: growth, harvesting and energy extraction, with a wide range of combinations of unit operations that can form a microalgal biofuel production system, but as yet there is no successful economically viable commercial system producing biofuel. This suggests that there are major technical and engineering difficulties to be resolved before economic algal biofuel production can be achieved. This article briefly reviews the methods by which useful energy may be extracted from microalgae biomass: (a) direct combustion, (b) pyrolysis, (c) gasification, (d) liquefaction, (e) hydrogen production by biochemical processes in certain algae, (f) fuel cells, (g) fermentation to bioethanol, (h) trans-esterification to biodiesel, (i) anaerobic digestion.     

Genetic engineering approaches to improve bioethanol production from maize

Biofuels such as bioethanol are becoming a viable alternative to fossil fuels. Utilizing agricultural biomass for the production of biofuel has drawn much interest in many science and engineering disciplines. As one of the major crops, maize offers promise in this regard. Compared to other crops with biofuel potential, maize can provide both starch (seed) and cellulosic (stover) material for bioethanol production. However, the combination of food, feed and fuel in one crop, although appealing, raises concerns related to the land delineation and distribution of maize grown for energy versus food and feed. To avoid this dilemma, the conversion of maize biomass into bioethanol must be improved. Conventional breeding, molecular marker assisted breeding and genetic engineering have already had, and will continue to have, important roles in maize improvement. The rapidly expanding information from genomics and genetics combined with improved genetic engineering technologies offer a wide range of possibilities for enhanced bioethanol production from maize.     

Industrial‐strength ecology: trade‐offs and opportunities in algal biofuel production

Microalgae represent one of the most promising groups of candidate organisms for replacing fossil fuels with contemporary primary production as a renewable source of energy. Algae can produce many times more biomass per unit area than terrestrial crop plants, easing the competing demands for land with food crops and native ecosystems. However, several aspects of algal biology present unique challenges to the industrial‐scale aquaculture of photosynthetic microorganisms. These include high susceptibility to invading aquatic consumers and weeds, as well as prodigious requirements for nutrients that may compete with the fertiliser demands of other crops. Most research on algal biofuel technologies approaches these problems from a cellular or genetic perspective, attempting either to engineer or select algal strains with particular traits. However, inherent functional trade‐offs may limit the capacity of genetic selection or synthetic biology to simultaneously optimise multiple functional traits for biofuel productivity and resilience. We argue that a community engineering approach that manages microalgal diversity, species composition and environmental conditions may lead to more robust and productive biofuel ecosystems. We review evidence for trade‐offs, challenges and opportunities in algal biofuel cultivation with a goal of guiding research towards intensifying bioenergy production using established principles of community and ecosystem ecology.     

Cyanobacteria cultivation in industrial wastewaters and biodiesel production from their biomass: a review

As an alternative fuel biodiesel has become increasingly important due to diminishing petroleum reserves and adverse environmental consequences of exhaust gases from petroleum-fueled engines. Recently, research interest has focused on the production of biofuel from microalgae. Cyanobacteria appeared to be suitable candidates for cultivation in wastes and wastewaters because they produce biomass in satisfactory quantity and can be harvested relatively easily due to their size and structure. In addition, their biomass composition can be manipulated by several environmental and operational factors to produce biomass with concrete characteristics. Herein, we review the culture of cyanobacteria in wastewaters and also the potential resources that can be transformed into biodiesel successfully for meeting the ever-increasing demand for biodiesel production.     

基因工程在能源植物改良中的应用

利用基因工程技术改良能源植物,对降低能源植物向生物燃料(生物乙醇、生物柴油)的转化成本、提高能源转化效率有着非常重要的意义。目前,基因工程技术已被广泛应用于提高植物总的生物产量、降低或改变植物木质素的含量与成分、在植物体中大量表达纤维素降解酶、提高油料植物的产油量以及改变植物油酯的组成成分等方面的研究。概述了利用基因工程技术在以上方面对能源植物进行改良已取得的进展,讨论了现存问题及未来的发展前景。     

Environmental and economic analysis of the fully integrated biorefinery

Cellulosic biofuel systems have the potential to significantly reduce the environmental impact of the world's transportation energy requirements. However, realizing this potential will require systems level thinking and scale integration. Until now, we have lacked modeling tools for studying the behavior of integrated cellulosic biofuel systems. In this paper, we describe a new research tool, the Biorefinery and Farm Integration Tool (BFIT) in which the production of fuel ethanol from cellulosic biomass is integrated with crop and animal (agricultural) production models. Uniting these three subsystems in a single combined model has allowed, for the first time, basic environmental and economic analysis of biomass production, possible secondary products, fertilizer production, and bioenergy production across various regions of the United States. Using BFIT, we simulate cellulosic ethanol production embedded in realistic agricultural landscapes in nine locations under a collection of farm management scenarios. This combined modeling approach permits analysis of economic profitability and highlights key areas for environmental improvement. These results show the advantages of introducing integrated biorefinery systems within agricultural landscapes. This is particularly true in the Midwest, which our results suggest is a good setting for the cellulosic ethanol industry. Specifically, results show that inclusion of cellulosic biofuel systems into existing agriculture enhances farm economics and reduces total landscape emissions. Model results also indicate a limited ethanol price effect from increased biomass transportation distance. Sensitivity analysis using BFIT revealed those variables having the strongest effects on the overall system performance, namely: biorefinery size, switchgrass yield, and biomass farm gate price.     

Metabolic systems analysis to advance algal biotechnology

Algal fuel sources promise unsurpassed yields in a carbon neutral manner that minimizes resource competition between agriculture and fuel crops. Many challenges must be addressed before algal biofuels can be accepted as a component of the fossil fuel replacement strategy. One significant challenge is that the cost of algal fuel production must become competitive with existing fuel alternatives. Algal biofuel production presents the opportunity to fine-tune microbial metabolic machinery for an optimal blend of biomass constituents and desired fuel molecules. Genome-scale model-driven algal metabolic design promises to facilitate both goals by directing the utilization of metabolites in the complex, interconnected metabolic networks to optimize production of the compounds of interest. Network analysis can direct microbial development efforts towards successful strategies and enable quantitative fine-tuning of the network for optimal product yields while maintaining the robustness of the production microbe. Metabolic modeling yields insights into microbial function, guides experiments by generating testable hypotheses, and enables the refinement of knowledge on the specific organism. While the application of such analytical approaches to algal systems is limited to date, metabolic network analysis can improve understanding of algal metabolic systems and play an important role in expediting the adoption of new biofuel technologies.     

Improving sugarcane for biofuel: engineering for an even better feedstock

Sugarcane is a proven biofuel feedstock and accounts for about 40% of the biofuel production worldwide. It has a more favorable energy input/output ratio than that of corn, the other major biofuel feedstock. The rich resource of genetic diversity and the plasticity of autopolyploid genomes offer a wealth of opportunities for the application of genomics and technologies to address fundamental questions in sugarcane towards maximizing biomass production. In a workshop on sugarcane engineering held at Rutgers University, we identified research areas and emerging technologies that could have significant impact on sugarcane improvement. Traditional plant physiological studies and standardized phenotypic characterization of sugarcane are essential for dissecting the developmental processes and patterns of gene expression in this complex polyploid species. Breeder friendly DNA markers associated with target traits will enhance selection efficiency and shorten the long breeding cycles. Integration of cold tolerance from Saccharum spontaneum and Miscanthus has the potential to expand the geographical range of sugarcane production from tropical and subtropical regions to temperate zones. The Flex-stock and mix-stock concepts could be solutions for sustaining local biorefineries where no single biofuel feedstock could provide consistent year-round supplies. The ever increasing capacities of genomics and biotechnologies pave the way for fully exploring these potentials to optimize sugarcane for biofuel production.     

Reconstruction of the Saccharopolyspora erythraea genome-scale model and its use for enhancing erythromycin production

Genome-scale metabolic reconstructions are routinely used for the analysis and design of metabolic engineering strategies for production of primary metabolites. The use of such reconstructions for metabolic engineering of antibiotic production is not common due to the lack of simple design algorithms in the absence of a cellular growth objective function. Here, we present the metabolic network reconstruction for the erythromycin producer Saccharopolyspora erythraea NRRL23338. The model was manually curated for primary and secondary metabolism pathways and consists of 1,482 reactions (2,075 genes) and 1,646 metabolites. As part of the model validation, we explored the potential benefits of supplying amino acids and identified five amino acids “compatible” with erythromycin production, whereby if glucose is supplemented with this amino acid on a carbon mole basis, the in silico model predicts that high erythromycin yield is possible without lowering biomass yield. Increased erythromycin titre was confirmed for four of the five amino acids, namely valine, isoleucine, threonine and proline. In bioreactor experiments, supplementation with 2.5?% carbon mole of valine increased the growth rate by 20?% and simultaneously the erythromycin yield on biomass by 50?%. The model presented here can be used as a framework for the future integration of high-throughput biological data sets in S. erythraea and ultimately to realise strain designs capable of increasing erythromycin production closer to the theoretical yield.     

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.     

Strategies to enhance production of microalgal biomass and lipids for biofuel feedstock

ABSTRACT

Microalgae have enormous potential as feedstock for biofuel production compared with other sources, due to their high areal productivity, relatively low environmental impact, and low impact on food security. However, high production costs are the major limitation for commercialization of algal biofuels. Strategies to maximize biomass and lipid production are crucial for improving the economics of using microalgae for biofuels. Selection of suitable algal strains, preferably from indigenous habitats, and further improvement of those ‘platform strains’ using mutagenesis and genetic engineering approaches are desirable. Conventional approaches to improve biomass and lipid productivity of microalgae mainly involve manipulation of nutritional (e.g. nitrogen and phosphorus) and environmental (e.g. temperature, light and salinity) factors. Approaches such as the addition of phytohormones, genetic and metabolic engineering, and co-cultivation of microalgae with yeasts and bacteria are more recent strategies to enhance biomass and lipid productivity of microalgae. Improvement in culture systems and the use of a hybrid system (i.e. a combination of open ponds and photobioreactors) is another strategy to optimize algal biomass and lipid production. In addition, the use of low-cost substrates such as agri-industrial wastewater for the cultivation of microalgae will be a smart strategy to reduce production costs. Such systems not only generate high algal biomass and lipid productivity, but are also useful for bioremediation of wastewater and bioremoval of waste CO₂. The aim of this review is to highlight the advances in the use of various strategies to enhance production of algal biomass and lipids for biofuel feedstock.     

Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells

Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells – including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.     

Food and bioenergy: reviewing the potential of dual‐purpose wheat crops

Within the bioenergy debate, the ‘food vs. fuel’ controversy quickly replaced enthusiasm for biofuels derived from first‐generation feedstocks. Second‐generation biofuels offer an opportunity to produce fuels from dedicated energy crops, waste materials or coproducts such as cereal straw. Wheat represents one of the most widely grown arable crops around the world, with wheat straw, a potential source of biofuel feedstock. Wheat straw currently has limited economic value; hence, wheat cultivars have been bred for increased grain yield; however, with the development of second‐generation biofuel production, utilization of straw biomass provides the potential for ‘food and fuel’. Reviewing the evidence for the development of dual‐purpose wheat cultivars optimized for food grain and straw biomass production, we present a holistic assessment of a potential ideotype for a dual‐purpose cultivar (DPC). An ideal DPC would be characterized by high grain and straw yields, high straw digestibility (i.e. biofuel yield potential) and good lodging resistance. Considerable variation in these traits exists among current wheat cultivars, facilitating the selection of improved individual traits; however, increasing straw yield and digestibility could potentially have negative trade‐off impacts on grain yield and lodging resistance, reducing the feasibility of a single ideotype. Adoption of alternative management practices could potentially increase straw yield and digestibility, albeit these practices are also associated with potential trade‐offs among cultivar traits. Benefits from using DPCs include reduced logistics costs along the biofuel feedstock supply chain, but practical barriers to differential pricing for straw digestibility traits are likely to reduce the financial incentive to farmers for growing higher ‘biofuel‐quality’ straw cultivars. Further research is required to explore the relationships among the ideotype traits to quantify potential DPC benefits; this will help to determine whether stakeholders along the bioenergy feedstock supply chain will invest in the development of DPCs that provide food and fuel potential.     

基于酶约束的代谢网络模型研究进展及其应用

基因组尺度代谢网络模型已经成功地应用于指导代谢工程改造,但由于传统通量平衡分析法仅考虑化学计量学和反应方向约束,模拟得到的是理论最优结果,对一些现象如代谢溢流、底物层级利用等无法准确描述。近年来人们通过在代谢网络模型中引入新的蛋白量、热力学等约束发展了新的约束优化计算方法,可以更准确真实地模拟细胞在不同条件下的代谢行为。文中主要对近年来提出的多种酶约束模型进行评述,对酶约束引入的基本思路、酶约束的数学方程表示及优化目标设定、引入酶约束后对代谢通量计算结果的影响及酶约束模型在代谢工程菌种改造中的应用等进行了全面深入的介绍,并提出了已有各种方法存在的主要问题,展望了相关方法的未来发展方向。通过引入新的约束,代谢网络模型能够更精确模拟和预测细胞在环境和基因扰动下的代谢行为,为代谢工程菌种改造提供更准确可靠的指导。     

Modification of native grasses for biofuel production may increase virus susceptibility

Bioenergy production is driving modifications to native plant species for use as novel biofuel crops. Key aims are to increase crop growth rates and to enhance conversion efficiency by reducing biomass recalcitrance to digestion. However, selection for these biofuel‐valuable traits has potential to compromise plant defenses and alter interactions with pests and pathogens. Insect‐vectored plant viruses are of particular concern because perennial crops have potential to serve as virus reservoirs that influence regional disease dynamics. In this study, we examined relationships between growth rates and biomass recalcitrance in five switchgrass (Panicum virgatum) populations, ranging from near‐wildtype to highly selected cultivars, in a common garden trial. We measured biomass accumulation rates and assayed foliage for acid detergent lignin, neutral detergent fiber, in vitro neutral detergent fiber digestibility and in vitro true dry matter digestibility. We then evaluated relationships between these traits and susceptibility to a widely distributed group of aphid‐transmitted Poaceae viruses (Luteoviridae: Barley and cereal yellow dwarf viruses, B/CYDVs). Virus infection rates and prevalence were assayed with RT‐PCR in the common garden, in greenhouse inoculation trials, and in previously established switchgrass stands across a 300‐km transect in Michigan, USA. Aphid host preferences were quantified in a series of arena host choice tests with field‐grown foliage. Contrary to expectations, biomass accumulation rates and foliar digestibility were not strongly linked in switchgrass populations we examined, and largely represented two different trait axes. Natural B/CYDV prevalence in established switchgrass stands ranged from 0% to 28%. In experiments, susceptibility varied notably among switchgrass populations and was more strongly predicted by potential biomass accumulation rates than by foliar digestibility; highly selected, productive cultivars were most virus‐susceptible and most preferred by aphids. Evaluation and mitigation of virus susceptibility of new biofuel crops is recommended to avert possible unintended consequences of biofuel production on regional pathogen dynamics.     

Methods for discovery and characterization of cellulolytic enzymes from insects

Abstract Cellulosic ethanol has been identified as a crucial biofuel resource due to its sustainability and abundance of cellulose feedstocks. However, current methods to obtain glucose from lignocellulosic biomass are ineffective due to recalcitrance of plant biomass. Insects have evolved endogenous and symbiotic enzymes to efficiently use lignocellulosic material as a source of metabolic glucose. Even though traditional biochemical methods have been used to identify and characterize these enzymes, the advancement of genomic and proteomic research tools are expected to allow new insights into insect digestion of cellulose. This information is highly relevant to the design of improved industrial processes of biofuel production and to identify potential new targets for development of insecticides. This review describes the diverse methodologies used to detect, quantify, purify, clone and express cellulolytic enzymes from insects, as well as their advantages and limitations.     

The dilemma for lipid productivity in green microalgae: importance of substrate provision in improving oil yield without sacrificing growth

Rising oil prices and concerns over climate change have resulted in more emphasis on research into renewable biofuels from microalgae. Unlike plants, green microalgae have higher biomass productivity, will not compete with food and agriculture, and do not require fertile land for cultivation. However, microalgae biofuels currently suffer from high capital and operating costs due to low yields and costly extraction methods. Microalgae grown under optimal conditions produce large amounts of biomass but with low neutral lipid content, while microalgae grown in nutrient starvation accumulate high levels of neutral lipids but are slow growing. Producing lipids while maintaining high growth rates is vital for biofuel production because high biomass productivity increases yield per harvest volume while high lipid content decreases the cost of extraction per unit product. Therefore, there is a need for metabolic engineering of microalgae to constitutively produce high amounts of lipids without sacrificing growth. Substrate availability is a rate-limiting step in balancing growth and fatty acid (FA) production because both biomass and FA synthesis pathways compete for the same substrates, namely acetyl-CoA and NADPH. In this review, we discuss the efforts made for improving biofuel production in plants and microorganisms, the challenges faced in achieving lipid productivity, and the important role of precursor supply for FA synthesis. The main focus is placed on the enzymes which catalyzed the reactions supplying acetyl-CoA and NADPH.