Visual Network Analysis of Dynamic Metabolic Pathways

Abstract. We extend our previous work on the exploration of static metabolic networks to evolving, and therefore dynamic, pathways. We apply our visualization software to data from a simulation of early metabolism. Thereby, we show that our technique allows us to test and argue for or against different scenarios for the evolution of metabolic pathways. This supports a profound and efficient analysis of the structure and properties of the generated metabolic networks and its underlying components, while giving the user a vivid impression of the dynamics of the system. The analysis process is inspired by Ben Shneiderman’s mantra of information visualization. For the overview, user-defined diagrams give insight into topological changes of the graph as well as changes in the attribute set associated with the participating enzymes, substances and reactions. This way, “interesting features” in time as well as in space can be recognized. A linked view implementation enables the navigation into more detailed layers of perspective for in-depth analysis of individual network configuration

Experimentally validated reconstruction and analysis of a genome-scale metabolic model of an anaerobic Neocallimastigomycota fungus

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi

Metabolic pathway analysis: Basic concepts and scientific applications in the post-genomic era

This article reviews the relatively short history of metabolic pathway analysis. Computer-aided algorithms for the synthesis of metabolic pathways are discussed. Important algebraic concepts used in pathway analysis, such as null space and convex cone, are explained. It is demonstrated how these concepts can be translated into meaningful metabolic concepts. For example, it is shown that the simplest vectors spanning the region of all admissible fluxes in stationary states, for which the term elementary flux modes was coined, correspond to fundamental pathways in the system. The concepts are illustrated with the help of a reaction scheme representing the glyoxylate cycle and adjacent reactions of aspartate and glutamate synthesis. The interrelations between pathway analysis and metabolic control theory are outlined. Promising applications for genome annotation and for biotechnological purposes are discussed. Armed with a better understanding of the architecture of cellular metabolism and the enormous amount of genomic data available today, biochemists and biotechnologists will be able to draw the entire metabolic map of a cell and redesign it by rational and directed metabolic engineering

Saccharomyces cerevisiae phenotypes can be predicted using constraint based analysis of a genome-scale reconscructed metabolic network

Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for ≈70–80% of the conditions considered

Mechanism of release from pellets coated with an ethylcellulose-based film

Studies were conducted to determine the mechanism of drug release from pellets coated with an ethylcellulose-based pseudolatex widely accepted for use as a sustained release coating for pharmaceuticals. Possible mechanisms for release include solution/diffusion through the continuous polymer phase and/or plasticizer channels, diffusion through aqueous pores and osmotically driven release through aqueous pores. To distinguish between these mechanisms, the release rate was studied as a function of coating thickness, plasticizer content, and osmotic pressure in the dissolution medium. As the coating thickness was increased from 9 to 50 [mu]m, the rate of release fell from 9.93[middle dot]10-3 to 1.71[middle dot]10-3 g phenylpropanolamine (PPA)[middle dot]HCl/100 ml h in an inversely proportional manner. Release as a function of plasticizer content was studied over the range 12 to 24% dibutyl sebacate (DBS). At 18 or 24% DBS, the rates of release of PPA[middle dot]HCl were virtually identical, about 50% of PPA[middle dot]HCl in six hours. At 12% DBS through, over 80% was released in the first hour. Surface area measurements and scanning electron microscopy (SEM) showed that the larger surface area of the 12% DBS batch was attributable to the presence of cracks in the coating. These results indicated that while the plasticizer is important in terms of forming a continuous film, diffusion through plasticizer channels is unlikely to make a significant contribution to the overall release rate. Release was also studied as a function of the osmotic pressure in the medium. A plot of release rate vs. osmotic pressure revealed an inverse linear relationship with a nonzero intercept. The steep dependency of release rate on osmotic pressure of the medium suggested that osmotically driven release is a major mechanism for release, while the nonzero intercept indicated some contribution from diffusion mechanisms. For all batches, SEM indicated that the film exhibited pores approximately 2 [mu]m in diameter, consistent with these mechanisms. In summary, then, the release from PPA[middle dot]HCl pellets coated with an ethylcellulose-based film appears to be a combination of osmotically driven release and diffusion through the polymer and/ or aqueous pores. A mathematical expression for this type of release is presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28280/1/0000033.pd