Individual Biochemical Reactions Research Articles

Identification of environmental aquatic bacteria by mass spectrometry supported by biochemical differentiation.

In this study, the purposefulness of using the API20E biochemical identification system as a supportive tool for enhancing the discrimination of environmental bacteria by MALDI-TOF MS method was evaluated. The identification results of MALDI-TOF MS and API20E for 321 Gram-negative strains isolated from the riverine freshwater and its sediment, and from the tissues of fish from the same water body were compared. Of 190 isolates identified with probable to highly probable species-level identification, and secure genus to probable species identification, 14 isolates (7.37%) had identification score over 2.300, and from the same group 19 isolates (10%) had excellent or very good identification to the genus by API20E system. With regard to agreement at genus level, out of 231 strains with genus designation available by API20E at any level of identification reliability, MALDI-TOF MS genus identification agreed in 163 (70.6%) strains. Of these, 135 (82.8%) were Aeromonas species and the remaining isolates belonged to 7 different genera. Although API20E resulted in frequent misidentification due to a limited profile index, its individual biochemical reactions might contribute to overall characterization of isolates. For example, for all reliable A. hydrophila strain identifications with MALDI-TOF MS, ONPG, GLU and OX reactions were unarguably positive for all fish and water/sediment isolates, whereas only fish isolates yielded additional 100% positive TDA and VP reactions. Thus, after initial identification with MALDI-TOF MS, environmental isolates with lower identification scores should be further analyzed. Before commencing confirmatory testing with nucleic acid-based methods, biochemical API20E tests could be applied as a purposeful and inexpensive identification support in targeting better identification accuracy. In this study, this was particularly evident with A. hydrophila, Chryseobacterium sp. and Pseudomonas sp. This identification strategy could significantly resolve methodological and cost-related shortcomings frequently occurring with large number of environmental isolates.

WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways.

We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.

Quantification of the Radiation Effects on the Stability of the Irradiated Plants Development in Light Culture

The radiation environment parameters affect not only individual biochemical reactions and physiological processes occurring in the plant, but also make it possible to achieve stable plant development as a whole. (Research purpose) To develop a method, technical means and methodology for the quantitative assessment of the optical radiation effect on plants, the use of which ensures energy efficiency and environmental friendliness of the resulting product by choosing the most optimal light source. (Materials and methods) The authors demonstrated that the stability of plant development can be characterized by fluctuating asymmetry indices, the values of which form uneven optical density on both sides of the leaf. They developed and manufactured an experimental model of a device for measuring leaves' optical density. They studied the cucumber (Cucumis sativus L.) of the Safa mix F1 mid-early hybrid. They used plants in a juvenile age state to reduce the duration of the experiment, that means from the appearance of the first to the appearance of the second leaf. (Results and discussion) The authors found that the differences between the parameters of plants grown under light sources № 1 and № 2 are not significant. They took the parameters of plants grown under light sources № 1 and № 3 for comparison. It was determined that these sources have approximately the same flow shares in the blue range (27.4 and 26.2 percent). They found out that the flow shares in the red range are different (34.4 and 49.5 percent), which determined different values of the ratio R:B (1.3 and 1.9). It was noted that the values of the ratio R: FR (4.1 and 16.3) are also different. (Conclusions) The authors proposed a method for assessing the effect of radiation on plants by the stability of their development. It was established that the application of this method to plants grown under conditions of light culture is promising for a comprehensive assessment of the quality of the light medium created by radiation sources; allows to choose the most optimal light sources.

Integrating genome-scale metabolic models into the prediction of microbial kinetics in natural environments

We propose a new method to predict microbial metabolic rates in natural environments using genome-scale metabolic models. This method is a hybrid of existing approaches, i.e., rate laws and flux balance analysis (FBA). It accounts for the availabilities of chemical energy and growth nutrients in the environment, and applies FBA independently to the respiration and biosynthesis pathways of genome-scale metabolic models. We illustrate the new method by modeling the metabolism of a representative methanogen – Methanosarcina barkeri – in laboratory reactors and in pristine and biostimulated aquifers. The laboratory application demonstrates that the hybrid method predicts the rates of individual biochemical reactions within overall cell metabolism and tracks, explicitly, cellular fluxes of carbon and energy. The aquifer applications reveal that the growth of methanogens in natural systems can be limited by multiple factors, including energy sources and growth nutrients, and that the limitations are subject to Liebig’s Law of the Minimum. These results highlight the improvements of the new method in biogeochemical reaction modeling, including its applicability to diverse environments, from eutrophic to oligotrophic.

Quantitative time-course metabolomics in human red blood cells reveal the temperature dependence of human metabolic networks

The temperature dependence of biological processes has been studied at the levels of individual biochemical reactions and organism physiology (e.g. basal metabolic rates) but has not been examined at the metabolic network level. Here, we used a systems biology approach to characterize the temperature dependence of the human red blood cell (RBC) metabolic network between 4 and 37 °C through absolutely quantified exo- and endometabolomics data. We used an Arrhenius-type model (Q10) to describe how the rate of a biochemical process changes with every 10 °C change in temperature. Multivariate statistical analysis of the metabolomics data revealed that the same metabolic network-level trends previously reported for RBCs at 4 °C were conserved but accelerated with increasing temperature. We calculated a median Q10 coefficient of 2.89 ± 1.03, within the expected range of 2-3 for biological processes, for 48 individual metabolite concentrations. We then integrated these metabolomics measurements into a cell-scale metabolic model to study pathway usage, calculating a median Q10 coefficient of 2.73 ± 0.75 for 35 reaction fluxes. The relative fluxes through glycolysis and nucleotide metabolism pathways were consistent across the studied temperature range despite the non-uniform distributions of Q10 coefficients of individual metabolites and reaction fluxes. Together, these results indicate that the rate of change of network-level responses to temperature differences in RBC metabolism is consistent between 4 and 37 °C. More broadly, we provide a baseline characterization of a biochemical network given no transcriptional or translational regulation that can be used to explore the temperature dependence of metabolism.

Functional centrality as a predictor of shifts in metabolic flux states.

BackgroundThe flux phenotype describes the entirety of biochemical conversions in a cell, which renders it a key characteristic of metabolic function. To quantify the functional relevance of individual biochemical reactions, functional centrality has been introduced based on cooperative game theory and structural modeling. It was shown to be capable to determine metabolic control properties utilizing only structural information. Here, we demonstrate the capability of functional centrality to predict changes in the flux phenotype.ResultsWe use functional centrality to successfully predict changes of metabolic flux triggered by switches in the environment. The predictions via functional centrality improve upon predictions using control-effective fluxes, another measure aiming at capturing metabolic control using structural information.ConclusionsThe predictions of flux changes via functional centrality corroborate the capability of the measure to gain a mechanistic understanding of metabolic control from the structure of metabolic networks.

A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases.

The involvement of vitamins and other micronutrients in intermediary metabolism was elucidated in the mid 1900’s at the level of individual biochemical reactions. Biochemical pathways remain the foundational knowledgebase for understanding how micronutrient adequacy modulates health in all life stages. Current daily recommended intakes were usually established on the basis of the association of a single nutrient to a single, most sensitive adverse effect and thus neglect interdependent and pleiotropic effects of micronutrients on biological systems. Hence, the understanding of the impact of overt or sub-clinical nutrient deficiencies on biological processes remains incomplete. Developing a more complete view of the role of micronutrients and their metabolic products in protein-mediated reactions is of importance. We thus integrated and represented cofactor-protein interaction data from multiple and diverse sources into a multi-layer network representation that links cofactors, cofactor-interacting proteins, biological processes, and diseases. Network representation of this information is a key feature of the present analysis and enables the integration of data from individual biochemical reactions and protein-protein interactions into a systems view, which may guide strategies for targeted nutritional interventions aimed at improving health and preventing diseases.

Heterogeneity of tumor-induced gene expression changes in the human metabolic network

Reprogramming of cellular metabolism is an emerging hallmark of neoplastic transformation. However, it is not known how metabolic gene expression in tumors differs from that in normal tissues, or whether different tumor types exhibit similar metabolic changes. Here we compare expression patterns of metabolic genes across 22 diverse types of human tumors. Overall, the metabolic gene expression program in tumors is similar to that in the corresponding normal tissues. Although expression changes of some metabolic pathways (e.g., up-regulation of nucleotide biosynthesis and glycolysis) are frequently observed across tumors, expression changes of other pathways (e.g., oxidative phosphorylation and the tricarboxylic acid (TCA) cycle) are very heterogeneous. Our analysis also suggests that the expression changes of major metabolic processes across tumors can be rationalized in terms of several principal components. On the level of individual biochemical reactions, many hundreds of metabolic isoenzymes show significant and tumor-specific expression changes. These isoenzymes are potential targets for anticancer therapy.

A network perspective on metabolic inconsistency

BackgroundIntegrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation.ResultsHere, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns.ConclusionsThe methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.

The Chemical Master Equation Approach to Nonequilibrium Steady-State of Open Biochemical Systems: Linear Single-Molecule Enzyme Kinetics and Nonlinear Biochemical Reaction Networks

We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC) with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a) individual biochemical reactions, (b) nonlinear network dynamics approaching to attractors, and (c) cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a) and (c) are stochastic while that with (b) is dominantly deterministic. Both (b) and (c) are emergent properties of a dynamic biochemical network; We suggest that the (c) is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b) in a “punctuated equilibrium” manner.

Online Training of New Curators

The basic information in Reactome is provided by bench biologists who are experts on a particular pathway, the Reactome Team is always working hard to drive engagement. This engagement between experts, curators, editors and reviewers requires maintenance and improvement, and in this sense Reactome is itself a model for large biocuration projects that are driven by community engagement. This tutorial will highlight issues from the perspective of online training participants, the trainer's and the audience's. From the audience perspective the tutorial will introduce the concepts that drive the Reactome data model, cover the basic steps that a researcher would have to follow in order to breakdown a biological pathway into its "reaction-based" Reactome representation. Introduce the user to the tools that are used by authors, the "authortool" and the tools used by curators, the "curatortool" to move that data into the Reactome database. From the trainer perspective the tutorial will focus on the essential role that a clear explanation of a resource's data model plays in priming the audience for the technical aspects of biocuration. Technical challenges and online delivery methods will be discussed and examples of systems used will be presented with discussion of the negative and positive aspects. Pedagogical models for enhancing audience participation will be briefly presented. The Reactome project is a collaboration among Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium to develop a curated resource of core pathways and reactions in human biology. The information in this database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross referenced with the sequence databases at NCBI, Ensembl and UniProt, the UCSC Genome Browser , KEGG (Gene and Compound ), ChEBI, PubMed and GO. The information is then managed by groups of curators at CSHL and EBI, peer-reviewed by other researchers and published on the web. While Reactome is targeted at human pathways, it also includes many individual biochemical reactions from non-human systems such as rat, mouse, pufferfish and zebrafish. This makes the database relevant to the many researchers who work on model organisms. All the information in Reactome is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. Reactome is a free on-line resource, and Reactome software is open-source.

Online Training of New Curators

AbstractThe basic information in Reactome is provided by bench biologists who are experts on a particular pathway, the Reactome Team is always working hard to drive engagement. This engagement between experts, curators, editors and reviewers requires maintenance and improvement, and in this sense Reactome is itself a model for large biocuration projects that are driven by community engagement. This tutorial will highlight issues from the perspective of online training participants, the trainer's and the audience's. From the audience perspective the tutorial will introduce the concepts that drive the Reactome data model, cover the basic steps that a researcher would have to follow in order to breakdown a biological pathway into its "reaction-based" Reactome representation. Introduce the user to the tools that are used by authors, the "authortool" and the tools used by curators, the "curatortool" to move that data into the Reactome database. From the trainer perspective the tutorial will focus on the essential role that a clear explanation of a resource's data model plays in priming the audience for the technical aspects of biocuration. Technical challenges and online delivery methods will be discussed and examples of systems used will be presented with discussion of the negative and positive aspects. Pedagogical models for enhancing audience participation will be briefly presented. The Reactome project is a collaboration among Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and The Gene Ontology Consortium to develop a curated resource of core pathways and reactions in human biology. The information in this database is authored by biological researchers with expertise in their fields, maintained by the Reactome editorial staff, and cross referenced with the sequence databases at NCBI, Ensembl and UniProt, the UCSC Genome Browser , KEGG (Gene and Compound ), ChEBI, PubMed and GO. The information is then managed by groups of curators at CSHL and EBI, peer-reviewed by other researchers and published on the web. While Reactome is targeted at human pathways, it also includes many individual biochemical reactions from non-human systems such as rat, mouse, pufferfish and zebrafish. This makes the database relevant to the many researchers who work on model organisms. All the information in Reactome is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. Reactome is a free on-line resource, and Reactome software is open-source.

Temperature compensation through systems biology

Temperature has a strong influence on most individual biochemical reactions. Despite this, many organisms have the remarkable ability to keep certain physiological fluxes approximately constant over an extended temperature range. In this study, we show how temperature compensation can be considered as a pathway phenomenon rather than the result of a single-enzyme property. Using metabolic control analysis, it is possible to identify reaction networks that exhibit temperature compensation. Because most activation enthalpies are positive, temperature compensation of a flux can occur when certain control coefficients are negative. This can be achieved in networks with branching reactions or if the first irreversible reaction is regulated by a feedback loop. Hierarchical control analysis shows that networks that are dynamic through regulated gene expression or signal transduction may offer additional possibilities to bring the apparent activation enthalpies close to zero and lead to temperature compensation. A calorimetric experiment with yeast provides evidence that such a dynamic temperature adaptation can actually occur.

Development of a multiplex PCR assay detecting 52 autosomal SNPs

An efficient method that can be used to simultaneously amplify a set of genetic loci across the genome with high reliability can provide a valuable tool for single nucleotide polymorphism (SNP) forensic genotyping. A crucial element is the number of individual biochemical reactions that must be performed. The SNPforID consortium (www.snpforid.org) was established in 2003 with the principal goal of developing a SNP-based system of DNA analysis that would have comparable discrimination power and ease of use to those of existing short tandem repeat (STR) based techniques. Here, we describe a strategy for amplifying 52 genomic DNA fragments, each containing one SNP, in a single tube, and accurately genotyping the PCR product mixture using two single base extension reactions. This multiplex approach reduces the cost of SNP genotyping and requires as little as 0.5 ng of genomic DNA to detect 52 SNPs. We used a multiple injection approach for DNA sequencers that can effectively detect all the SNPs amplified in a single electrophoretic run. We present SNP data for 700 unrelated individuals from 9 populations.

Ubiquitin Binding Site of the Ubiquitin E2 Variant (UEV) Protein Mms2 Is Required for DNA Damage Tolerance in the Yeast RAD6 Pathway

Different ubiquitin modifications to proliferating cell nuclear antigen (PCNA) signal distinct modes of lesion bypass in the RAD6 pathway of DNA damage tolerance. The modification of PCNA with monoubiquitin signals an error-prone bypass, whereas the extension of this modification into a Lys-63-linked polyubiquitin chain promotes error-free bypass. Chain formation is catalyzed by the Mms2/Ubc13 conjugating enzyme variant/conjugating enzyme (UEV.E2) complex together with the Rad5 ubiquitin ligase. In vitro studies of this UEV.E2 complex have identified a ubiquitin binding site that is mainly localized on Mms2. However, the role of this site in DNA damage tolerance and the molecular features of the ubiquitin/Mms2 interaction are poorly understood. Here we identify two molecular determinants, the side chains of Mms2-Ile-57 and ubiquitin-Ile-44, that are required for chain assembly in vitro and error-free lesion bypass in vivo. Mutating either of these side chains to alanine elicits a severe 10-20-fold inhibition of chain synthesis that is caused by compromised binding of the acceptor ubiquitin to Mms2. These results suggest that the ubiquitin binding site of Mms2 is necessary for error-free lesion bypass in the RAD6 pathway and provide new insights into ubiquitin recognition by UEV proteins.

ENERGY PARTITIONING BETWEEN DIFFERENT-SIZED ORGANISMS AND ECOSYSTEM STABILITY

EcologyVolume 85, Issue 7 p. 1811-1813 Forum: Metabolic Theory ENERGY PARTITIONING BETWEEN DIFFERENT-SIZED ORGANISMS AND ECOSYSTEM STABILITY Bai-Lian Li, Bai-Lian Li Department of Botany and Plant Sciences, University of California, Riverside, California 92521 USA E-mail: [email protected]Search for more papers by this authorVictor G. Gorshkov, Victor G. Gorshkov Theoretical Physics Division, Petersburg Nuclear Physics Institute, 188300, Gatchina, St. Petersburg, RussiaSearch for more papers by this authorAnastassia M. Makarieva, Anastassia M. Makarieva Theoretical Physics Division, Petersburg Nuclear Physics Institute, 188300, Gatchina, St. Petersburg, RussiaSearch for more papers by this author Bai-Lian Li, Bai-Lian Li Department of Botany and Plant Sciences, University of California, Riverside, California 92521 USA E-mail: [email protected]Search for more papers by this authorVictor G. Gorshkov, Victor G. Gorshkov Theoretical Physics Division, Petersburg Nuclear Physics Institute, 188300, Gatchina, St. Petersburg, RussiaSearch for more papers by this authorAnastassia M. Makarieva, Anastassia M. Makarieva Theoretical Physics Division, Petersburg Nuclear Physics Institute, 188300, Gatchina, St. Petersburg, RussiaSearch for more papers by this author First published: 01 July 2004 https://doi.org/10.1890/03-0693Citations: 29 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume85, Issue7July 2004Pages 1811-1813 RelatedInformation

Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices

Genome-scale metabolic networks can be reconstructed. The systemic biochemical properties of these networks can now be studied. Here, genome-scale reconstructed metabolic networks were analysed using singular value decomposition (SVD). All the individual biochemical conversions contained in a reconstructed metabolic network are described by a stoichiometric matrix (S). SVD of S led to the definition of the underlying modes that characterize the overall biochemical conversions that take place in a network and rank-ordered their importance. The modes were shown to correspond to systemic biochemical reactions and they could be used to identify the groups and clusters of individual biochemical reactions that drive them. Comparative analysis of the Escherichia coli, Haemophilus influenzae, and Helicobacter pylori genome-scale metabolic networks showed that the four dominant modes in all three networks correspond to: (1) the conversion of ATP to ADP, (2) redox metabolism of NADP, (3) proton-motive force, and (4) inorganic phosphate metabolism. The sets of individual metabolic reactions deriving these systemic conversions, however, differed among the three organisms. Thus, we can now define systemic metabolic reactions, or eigen-reactions, for the study of systems biology of metabolism and have a basis for comparing the overall properties of genome-specific metabolic networks.

Multiple sulphur isotopic interpretations of biosynthetic pathways: implications for biological signatures in the sulphur isotope record

ABSTRACTIsotopic fractionations produced by biosynthetic processes are the result of networks of individual biochemical reactions that operate at differing efficiencies and with distinct fractionation factors. These reaction networks determine the magnitude and direction of the net isotopic fractionation associated with a given process. Here we examine the ways that biological reaction networks control mass‐dependent isotopic fractionations of multiple sulphur isotopes. We describe how material‐flow through some networks can produce characteristic multiple‐sulphur‐isotope signatures that differ from those produced by their constituent steps and demonstrate that experimental results with Archaeaglobus fulgidus can be evaluated using multiple sulphur isotopes in the context of previously published models for dissimilatory sulphate reduction. Our evaluation of these data is consistent with the interpretation that the dependence of sulphur isotope fractionation on external sulphate concentration is rooted in differences between the forward and reverse ↔ adenosine‐5′‐phosphosulphate (APS) ↔ steps. The framework provided by our analysis has the potential to evaluate the biosynthetic pathways that produce the isotopic fractionations, to isolate the primary sources of isotopic fractionations (sulphate reduction or disproportionation reactions) and to establish criteria to identify the signature of specific sulphur metabolisms in the geological record. The results highlight the new types of information that can be obtained by including measurements of δ33S {δ33S = [(33S/32S)sample/(33S/32S)reference − 1]*1000} with measurements of δ34S.

Genome complexity reduction for SNP genotyping analysis.

Efficient single nucleotide polymorphism (SNP) genotyping methods are necessary to accomplish many current gene discovery goals. A crucial element in large-scale SNP genotyping is the number of individual biochemical reactions that must be performed. An efficient method that can be used to simultaneously amplify a set of genetic loci across a genome with high reliability can provide a valuable tool for large-scale SNP genotyping studies. In this paper we describe and characterize a method that addresses this goal. We have developed a strategy for reducing genome complexity by using degenerate oligonucleotide primer (DOP)-PCR and applied this strategy to SNP genotyping in three complex eukaryotic genomes; human, mouse, and Arabidopsis thaliana. Using a single DOP-PCR primer, SNP loci spread throughout a genome can be amplified and accurately genotyped directly from a DOP-PCR product mixture. DOP-PCRs are extremely reproducible. The DOP-PCR method is transferable to many species of interest. Finally, we describe an in silico approach that can effectively predict the SNP loci amplified in a given DOP-PCR, permitting the design of an efficient set of reactions for large-scale, genome-wide SNP studies.

Metabolic dynamics in the human red cell. Part III—Metabolic reaction rates

The kinetics of the enzymatic reaction and transport rates that constitute the main metabolic pathways of the red cell metabolism are described. The red cell metabolic model consists of 33 dynamic mass balances that contain 41 enzymatic reaction rate laws and transmembrane processes. Our goal here is two-fold: the development of rate expressions for the individual biochemical reactions of the integrated red cell metabolism, and order of magnitudes estimates to gauge their dynamic properties in terms of response times and action scales of substrates, products and regulators.