Economic bioconversion of plant cell wall hydrolysates into fuels and chemicals has been hampered mainly due to the inability of microorganisms to efficiently co-ferment pentose and hexose sugars, especially glucose and xylose, which are the most abundant sugars in cellulosic hydrolysates. Saccharomyces cerevisiae cannot metabolize xylose due to a lack of xylose-metabolizing enzymes. We developed a rapid and efficient xylose-fermenting S. cerevisiae through rational and inverse metabolic engineering strategies, comprising the optimization of a heterologous xylose-assimilating pathway and evolutionary engineering. Strong and balanced expression levels of the XYL1, XYL2, and XYL3 genes constituting the xylose-assimilating pathway increased ethanol yields and the xylose consumption rates from a mixture of glucose and xylose with little xylitol accumulation. The engineered strain, however, still exhibited a long lag time when metabolizing xylose above 10 g/l as a sole carbon source, defined here as xylose toxicity. Through serial-subcultures on xylose, we isolated evolved strains which exhibited a shorter lag time and improved xylose-fermenting capabilities than the parental strain. Genome sequencing of the evolved strains revealed that mutations in PHO13 causing loss of the Pho13p function are associated with the improved phenotypes of the evolved strains. Crude extracts of a PHO13-overexpressing strain showed a higher phosphatase activity on xylulose-5-phosphate (X-5-P), suggesting that the dephosphorylation of X-5-P by Pho13p might generate a futile cycle with xylulokinase overexpression. While xylose consumption rates by the evolved strains improved substantially as compared to the parental strain, xylose metabolism was interrupted by accumulated acetate. Deletion of ALD6 coding for acetaldehyde dehydrogenase not only prevented acetate accumulation, but also enabled complete and efficient fermentation of xylose as well as a mixture of glucose and xylose by the evolved strain. These findings provide direct guidance for developing industrial strains to produce cellulosic fuels and chemicals.

Genome-scale models of metabolism have only been analyzed with the constraint-based modelling philosophy and there have been several genome-scale gene-protein-reaction models. But research on the modelling for energy metabolism of organisms just began in recent years and research on metabolic weighted complex network are rare in literature. We have made three research based on the complete model of E. coli’s energy metabolism. We first constructed a metabolic weighted network using the rates of free energy consumption within metabolic reactions as the weights. We then analyzed some structural characters of the metabolic weighted network that we constructed. We found that the distribution of the weight values was uneven, that most of the weight values were zero while reactions with abstract large weight values were rare and that the relationship between w (weight values) and v (flux values) was not of linear correlation. At last, we have done some research on the equilibrium of free energy for the energy metabolism system of E. coli. We found that (free energy rate input from the environment) can meet the demand of (free energy rate dissipated by chemical process) and that chemical process plays a great role in the dissipation of free energy in cells. By these research and to a certain extend, we can understand more about the energy metabolism of E. coli.

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.

Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used.

Thermobifida fusca is a high-G+C-content, thermophilic, Gram-positive soil actinobacterium with high cellulolytic activity. In T. fusca, CelR is thought to act as the primary regulator of cellulase gene expression by binding to a 14-bp inverted repeat [5′-(T)GGGAGCGCTCCC(A)] that is upstream of many known cellulase genes. Previously, the ability to study the roles and regulation of cellulase genes in T. fusca has been limited largely by a lack of established genetic engineering methods for T. fusca. In this study, we developed an efficient procedure for creating precise chromosomal gene disruptions and demonstrated this procedure by generating a celR deletion strain. The celR deletion strain was then characterized using measurements for growth behavior, cellulase activity, and gene expression. The celR deletion strain of T. fusca exhibited a severely crippled growth phenotype with a prolonged lag phase and decreased cell yields for growth on both glucose and cellobiose. While the maximum endoglucanase activity and cellulase activity were not significantly changed, the endoglucanase activity and cellulase activity per cell were highly elevated. Measurements of mRNA transcript levels in both the celR deletion strain and the wild-type strain indicated that the CelR protein potentially acts as a repressor for some genes and as an activator for other genes. Overall, we established and demonstrated a method for manipulating chromosomal DNA in T. fusca that can be used to study the cellulolytic capabilities of this organism. Components of this method may be useful in developing genetic engineering methods for other currently intractable organisms.

The field of synthetic biology has made rapid progress in a number of areas including method development, novel applications and community building. In seeking to make biology “engineerable,” synthetic biology is increasing the accessibility of biological research to researchers of all experience levels and backgrounds. One of the underlying strengths of synthetic biology is that it may establish the framework for a rigorous bottom-up approach to studying biology starting at the DNA level. Building upon the existing framework established largely by the Registry of Standard Biological Parts, careful consideration of future goals may lead to integrated multi- scale approaches to biology. Here we describe some of the current challenges that need to be addressed or considered in detail to continue the development of synthetic biology. Specifically, discussion on the areas of elucidating biological principles, computational methods and experimental construction methodologies are presented.

HOXA9-mediated up-regulation of miR-155 was noted during an array-based analysis of microRNA expression in Hoxa9−/−bone marrow (BM) cells. HOXA9 induction of miR-155 was confirmed in these samples, as well as in wild-type versus Hoxa9-deficient marrow, using northern analysis and qRT–PCR. Infection of wild-type BM with HOXA9 expressing or GFP+ control virus further confirmed HOXA9-mediated regulation of miR-155. miR-155 expression paralleled Hoxa9 mRNA expression in fractionated BM progenitors, being highest in the stem cell enriched pools. HOXA9 capacity to induce myeloid colony formation was blunted in miR-155-deficient BM cells, indicating that miR-155 is a downstream mediator of HOXA9 function in blood cells. Pu.1, an important regulator of myelopoiesis, was identified as a putative down stream target for miR-155. Although miR-155 was shown to down-regulate the Pu.1 protein, HOXA9 did not appear to modulate Pu.1 expression in murine BM cells.

Background

Microorganisms possess diverse metabolic capabilities that can potentially be leveraged for efficient production of biofuels. Clostridium thermocellum (ATCC 27405) is a thermophilic anaerobe that is both cellulolytic and ethanologenic, meaning that it can directly use the plant sugar, cellulose, and biochemically convert it to ethanol. A major challenge in using microorganisms for chemical production is the need to modify the organism to increase production efficiency. The process of properly engineering an organism is typically arduous.

Results

Here we present a genome-scale model of C. thermocellum metabolism, iSR432, for the purpose of establishing a computational tool to study the metabolic network of C. thermocellum and facilitate efforts to engineer C. thermocellum for biofuel production. The model consists of 577 reactions involving 525 intracellular metabolites, 432 genes, and a proteomic-based representation of a cellulosome. The process of constructing this metabolic model led to suggested annotation refinements for 27 genes and identification of areas of metabolism requiring further study. The accuracy of the iSR432 model was tested using experimental growth and by-product secretion data for growth on cellobiose and fructose. Analysis using this model captures the relationship between the reduction-oxidation state of the cell and ethanol secretion and allowed for prediction of gene deletions and environmental conditions that would increase ethanol production.

Conclusions

By incorporating genomic sequence data, network topology, and experimental measurements of enzyme activities and metabolite fluxes, we have generated a model that is reasonably accurate at predicting the cellular phenotype of C. thermocellum and establish a strong foundation for rational strain design. In addition, we are able to draw some important conclusions regarding the underlying metabolic mechanisms for observed behaviors of C. thermocellum and highlight remaining gaps in the existing genome annotations.

The generation of well-characterized parts and the formulation of biological design principles in synthetic biology are laying the foundation for more complex and advanced microbial metabolic engineering. Improvements in de novo DNA synthesis and codon-optimization alone are already contributing to the manufacturing of pathway enzymes with improved or novel function. Further development of analytical and computer-aided design tools should accelerate the forward engineering of precisely regulated synthetic pathways by providing a standard framework for the predictable design of biological systems from well-characterized parts. In this review we discuss the current state of synthetic biology within a four-stage framework (design, modeling, synthesis, analysis) and highlight areas requiring further advancement to facilitate true engineering of synthetic microbial metabolism.

Background

Feed-forward motifs are important functional modules in biological and other complex networks. The functionality of feed-forward motifs and other network motifs is largely dictated by the connectivity of the individual network components. While studies on the dynamics of motifs and networks are usually devoted to the temporal or spatial description of processes, this study focuses on the relationship between the specific architecture and the overall rate of the processes of the feed-forward family of motifs, including double and triple feed-forward loops. The search for the most efficient network architecture could be of particular interest for regulatory or signaling pathways in biology, as well as in computational and communication systems.

Results

Feed-forward motif dynamics were studied using cellular automata and compared with differential equation modeling. The number of cellular automata iterations needed for a 100% conversion of a substrate into a target product was used as an inverse measure of the transformation rate. Several basic topological patterns were identified that order the specific feed-forward constructions according to the rate of dynamics they enable. At the same number of network nodes and constant other parameters, the bi-parallel and tri-parallel motifs provide higher network efficacy than single feed-forward motifs. Additionally, a topological property of isodynamicity was identified for feed-forward motifs where different network architectures resulted in the same overall rate of the target production.

Conclusion

It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. These fundamental results can be used as a basis for simulating larger networks as combinations of smaller network modules with implications on studying synthetic gene circuits, small regulatory systems, and eventually dynamic whole-cell models.

In comparison with intensive studies of genetic mechanisms related to biological evolutionary systems, much less analysis has been conducted on metabolic network responses to adaptive evolution that are directly associated with evolved metabolic phenotypes. Metabolic mechanisms involved in laboratory evolution of Escherichia coli on gluconeogenic carbon sources, such as lactate, were studied based on intracellular flux states determined from 13C tracer experiments and 13C-constrained flux analysis. At the end point of laboratory evolution, strains exhibited a more than doubling of the average growth rate and a 50% increase in the average biomass yield. Despite different evolutionary trajectories among parallel evolved populations, most improvements were obtained within the first 250 generations of evolution and were generally characterized by a significant increase in pathway capacity. Partitioning between gluconeogenic and pyruvate catabolic flux at the pyruvate node remained almost unchanged, while flux distributions around the key metabolites phosphoenolpyruvate, oxaloacetate, and acetyl-coenzyme A were relatively flexible over the course of evolution on lactate to meet energetic and anabolic demands during rapid growth on this gluconeogenic carbon substrate. There were no clear qualitative correlations between most transcriptional expression and metabolic flux changes, suggesting complex regulatory mechanisms at multiple levels of genetics and molecular biology. Moreover, higher fitness gains for cell growth on both evolutionary and alternative carbon sources were found for strains that adaptively evolved on gluconeogenic carbon sources compared to those that evolved on glucose. These results provide a novel systematic view of the mechanisms underlying microbial adaptation to growth on a gluconeogenic substrate.

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.

Synopsis

One of the major uses of in silico models in biology is to identify discrepancies between model predictions and experimental data and use these discrepancies to drive discovery of novel biological mechanisms. However, models only allow for identification of the discrepancies; they do not necessarily provide any assistance in discovering what are the missing or incorrect functionalities in the model that cause these discrepancies. Herrgård et al. describe a new in silico method, optimal metabolic network identification, or OMNI, that performs this discovery process in an efficient and systematic manner for genome-scale metabolic networks. Given a preliminary metabolic network model and experimentally determined metabolic flux data, OMNI finds the changes that need to be made to the model so that its predictions match the experimental data as well as possible. Herrgård et al. apply the method to identify metabolic bottlenecks in experimentally evolved Escherichia coli strains and to diagnose problems in strains designed through metabolic engineering strategies to overproduce specific desirable byproducts. The OMNI method can also be adapted to number of other settings, including identification of novel biochemical pathways in ill-characterized organisms based on limited amounts of experimental data.

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of α-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37°C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.