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Philip W Kuchel - One of the best experts on this subject based on the ideXlab platform.

  • Plasma membrane oxidoreductases: effects on Erythrocyte Metabolism and redox homeostasis.
    Antioxidants & redox signaling, 2006
    Co-Authors: Eleanor C. Kennett, Philip W Kuchel
    Abstract:

    Plasma membrane oxidoreductases (PMORs) have been found in the membranes of all cells. These systems have been studied extensively in the human Erythrocyte, so much is known about their activity and effect on Erythrocyte cellular functioning. PMORs have been shown to be involved in a number of events associated with cell growth and function in other cell lines, but perhaps their most important role, especially in the nucleus- free mature Erythrocyte, is as a redox sensor. The PMOR reduces extracellular oxidants by using the reducing power of intracellular antioxidants, making the cell Metabolism respond to changes in the local redox environment. Thus, the activity of the PMOR is closely linked to the metabolic status of the Erythrocyte. The main intracellular reductant for this system is ascorbic acid; however, the cell must also have the ability to supply NADH for full activity. Nuclear magnetic resonance studies on the effects of extracellular oxidants on intracellular Metabolism have increased our know...

  • Current status and challenges in connecting models of Erythrocyte Metabolism to experimental reality.
    Progress in biophysics and molecular biology, 2004
    Co-Authors: Philip W Kuchel
    Abstract:

    Detailed kinetic models of human Erythrocyte Metabolism have served to summarize the vast literature and to predict outcomes from laboratory and "Nature's" experiments on this simple cell. Mathematical methods for handling the large array of nonlinear ordinary differential equations that describe the time dependence of this system are well developed, but experimental methods that can guide the evolution of the models are in short supply. NMR spectroscopy is one method that is non-selective with respect to analyte detection but is highly specific with respect to their identification and quantification. Thus time courses of Metabolism are readily recorded for easily changed experimental conditions. While the data can be simulated, the systems of equations are too complex to allow solutions of the inverse problem, namely parameter-value estimation for the large number of enzyme and membrane-transport reactions operating in situ as opposed to in vitro. Other complications with the modelling include the dependence of cell volume on time, and the rates of membrane transport processes are often dependent on the membrane potential. These matters are discussed in the light of new modelling strategies.

  • modelling Metabolism with mathematica detailed examples including Erythrocyte Metabolism
    2003
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    Introduction to Chemical Kinetics and Numerical Integration Aims and Objectives Complexity Definitions Time Courses of Reactions Numerical Integration of Differential Equations Predictor Corrector Methods Conclusions Elements of Enzyme Kinetics Kinetics of Enzymic Reactions Enzyme Inhibition Enzyme Mechanisms Regulatory Enzymes Basic Procedures for Simulating Metabolic Systems Introduction Relationships between Unitary Rate Constants and Steady-State Parameters Upper Limit of Values for Unitary Rate Constants Realistic Enzyme Models Deriving Expressions for Steady- State Parameters Multiple Equilibria pH Effects on Kinetic Parameters A Simple Model of the Urea Cycle Conclusions Advanced Simulation of Metabolic Pathways Introduction Simulating the Time Dependent Behaviour of Multienzyme Systems Using Matrix Notation in Simulating Metabolic Pathways Generating the Stoichiometry Matrix Determining Steady- State Concentrations Conservation Relations Stability of a Steady State When Cell Volume Changes with Time Decomposition of N and Calculation of the Link Matrix (Optional) Metabolic Control Analysis Introduction Control Coefficients Calculation of Control Coefficients by Numerical Perturbation Elasticity Coefficients Response Coefficients Internal Response Coefficients Conclusions Parameter Estimation Introduction Approaches to Parameter Estimation Least Squares Maximum a Posteriori (MAP) Parameters in Rate Equations Parameters in Systems of Differential Equations Optimal Parameter Variances of Parameters Model of Erythrocyte Metabolism Introduction Models of Erythrocyte Metabolism Stoichiometry of Human Erythrocyte Metabolism In Vivo Steady State of the Erythrocyte Conservation of Mass Relationships Simulating a Timecourse Metabolic Control Analysis of Human Erythrocyte Metabolism Introduction Normal In Vivo Steady State Identifying Zero Fluxes Flux Control Coefficients Concentration Control Coefficients Response Coefficients and Partitioned Responses Elasticity Coefficients Internal Response Coefficients Concluding Remarks Note: Each chapter contains Exercises and References. Appendices Rate Equation Deriver Metabolic Control Analysis Functions Rate Equations for Enzymes of the Human Erythrocyte Initial Conditions and External Parameters for the Erythrocyte Model Equation List Describing the Erythrocyte Model of Chapters 7 and 8

  • model of 2 3 bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations equations and parameter refinement
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    Over the last 25 years, several mathematical models of Erythrocyte Metabolism have been developed. Although these models have identified the key features in the regulation and control of Erythrocyte Metabolism, many important aspects remain unexplained. In particular, none of these models have satisfactorily accounted for 2,3-bisphosphoglycerate (2,3-BPG) Metabolism. 2,3-BPG is an important modulator of haemoglobin oxygen anity, and hence an understanding of the regulation of 2,3BPG concentration is important for understanding blood oxygen transport. A detailed, comprehensive, and hence realistic mathematical model of Erythrocyte Metabolism is presented that can explain the regulation and control of 2,3-BPG concentration and turnover. The model is restricted to the core metabolic pathways, namely glycolysis, the 2,3-BPG shunt and the pentose phosphate pathway (PPP), and includes membrane transport of metabolites, the binding of metabolites to haemoglobin and Mg#+, as well as

  • model of 2 3 bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations computer simulation and metabolic control analysis
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    This is the third of three papers [see also Mulquiney, Bubb and Kuchel (1999) Biochem. J. 342, 565-578; Mulquiney and Kuchel (1999) Biochem. J. 342, 579-594] for which the general goal was to explain the regulation and control of 2,3-bisphosphoglycerate (2,3-BPG) Metabolism in human Erythrocytes. 2,3-BPG is a major modulator of haemoglobin oxygen affinity and hence is vital in blood oxygen transport. A detailed mathematical model of Erythrocyte Metabolism was presented in the first two papers. The model was refined through an iterative loop of experiment and simulation and it was used to predict outcomes that are consistent with the metabolic behaviour of the Erythrocyte under a wide variety of experimental and physiological conditions. For the present paper, the model was examined using computer simulation and Metabolic Control Analysis. The analysis yielded several new insights into the regulation and control of 2,3-BPG Metabolism. Specifically it was found that: (1) the feedback inhibition of hexokinase and phosphofructokinase by 2, 3-BPG are equally as important as the product inhibition of 2,3-BPG synthase in controlling the normal in vivo steady-state concentration of 2,3-BPG; (2) H(+) and oxygen are effective regulators of 2,3-BPG concentration and that increases in 2,3-BPG concentrations are achieved with only small changes in glycolytic rate; (3) these two effectors exert most of their influence through hexokinase and phosphofructokinase; (4) flux through the 2,3-BPG shunt changes in absolute terms in response to different energy demands placed on the cell. This response of the 2,3-BPG shunt contributes an [ATP]-stabilizing effect. A 'cost' of this is that 2, 3-BPG concentrations are very sensitive to the energy demand of the cell and; (5) the flux through the 2,3-BPG shunt does not change in response to different non-glycolytic demands for NADH.

Peter Mulquiney - One of the best experts on this subject based on the ideXlab platform.

  • modelling Metabolism with mathematica detailed examples including Erythrocyte Metabolism
    2003
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    Introduction to Chemical Kinetics and Numerical Integration Aims and Objectives Complexity Definitions Time Courses of Reactions Numerical Integration of Differential Equations Predictor Corrector Methods Conclusions Elements of Enzyme Kinetics Kinetics of Enzymic Reactions Enzyme Inhibition Enzyme Mechanisms Regulatory Enzymes Basic Procedures for Simulating Metabolic Systems Introduction Relationships between Unitary Rate Constants and Steady-State Parameters Upper Limit of Values for Unitary Rate Constants Realistic Enzyme Models Deriving Expressions for Steady- State Parameters Multiple Equilibria pH Effects on Kinetic Parameters A Simple Model of the Urea Cycle Conclusions Advanced Simulation of Metabolic Pathways Introduction Simulating the Time Dependent Behaviour of Multienzyme Systems Using Matrix Notation in Simulating Metabolic Pathways Generating the Stoichiometry Matrix Determining Steady- State Concentrations Conservation Relations Stability of a Steady State When Cell Volume Changes with Time Decomposition of N and Calculation of the Link Matrix (Optional) Metabolic Control Analysis Introduction Control Coefficients Calculation of Control Coefficients by Numerical Perturbation Elasticity Coefficients Response Coefficients Internal Response Coefficients Conclusions Parameter Estimation Introduction Approaches to Parameter Estimation Least Squares Maximum a Posteriori (MAP) Parameters in Rate Equations Parameters in Systems of Differential Equations Optimal Parameter Variances of Parameters Model of Erythrocyte Metabolism Introduction Models of Erythrocyte Metabolism Stoichiometry of Human Erythrocyte Metabolism In Vivo Steady State of the Erythrocyte Conservation of Mass Relationships Simulating a Timecourse Metabolic Control Analysis of Human Erythrocyte Metabolism Introduction Normal In Vivo Steady State Identifying Zero Fluxes Flux Control Coefficients Concentration Control Coefficients Response Coefficients and Partitioned Responses Elasticity Coefficients Internal Response Coefficients Concluding Remarks Note: Each chapter contains Exercises and References. Appendices Rate Equation Deriver Metabolic Control Analysis Functions Rate Equations for Enzymes of the Human Erythrocyte Initial Conditions and External Parameters for the Erythrocyte Model Equation List Describing the Erythrocyte Model of Chapters 7 and 8

  • model of 2 3 bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations equations and parameter refinement
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    Over the last 25 years, several mathematical models of Erythrocyte Metabolism have been developed. Although these models have identified the key features in the regulation and control of Erythrocyte Metabolism, many important aspects remain unexplained. In particular, none of these models have satisfactorily accounted for 2,3-bisphosphoglycerate (2,3-BPG) Metabolism. 2,3-BPG is an important modulator of haemoglobin oxygen anity, and hence an understanding of the regulation of 2,3BPG concentration is important for understanding blood oxygen transport. A detailed, comprehensive, and hence realistic mathematical model of Erythrocyte Metabolism is presented that can explain the regulation and control of 2,3-BPG concentration and turnover. The model is restricted to the core metabolic pathways, namely glycolysis, the 2,3-BPG shunt and the pentose phosphate pathway (PPP), and includes membrane transport of metabolites, the binding of metabolites to haemoglobin and Mg#+, as well as

  • model of 2 3 bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations computer simulation and metabolic control analysis
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    This is the third of three papers [see also Mulquiney, Bubb and Kuchel (1999) Biochem. J. 342, 565-578; Mulquiney and Kuchel (1999) Biochem. J. 342, 579-594] for which the general goal was to explain the regulation and control of 2,3-bisphosphoglycerate (2,3-BPG) Metabolism in human Erythrocytes. 2,3-BPG is a major modulator of haemoglobin oxygen affinity and hence is vital in blood oxygen transport. A detailed mathematical model of Erythrocyte Metabolism was presented in the first two papers. The model was refined through an iterative loop of experiment and simulation and it was used to predict outcomes that are consistent with the metabolic behaviour of the Erythrocyte under a wide variety of experimental and physiological conditions. For the present paper, the model was examined using computer simulation and Metabolic Control Analysis. The analysis yielded several new insights into the regulation and control of 2,3-BPG Metabolism. Specifically it was found that: (1) the feedback inhibition of hexokinase and phosphofructokinase by 2, 3-BPG are equally as important as the product inhibition of 2,3-BPG synthase in controlling the normal in vivo steady-state concentration of 2,3-BPG; (2) H(+) and oxygen are effective regulators of 2,3-BPG concentration and that increases in 2,3-BPG concentrations are achieved with only small changes in glycolytic rate; (3) these two effectors exert most of their influence through hexokinase and phosphofructokinase; (4) flux through the 2,3-BPG shunt changes in absolute terms in response to different energy demands placed on the cell. This response of the 2,3-BPG shunt contributes an [ATP]-stabilizing effect. A 'cost' of this is that 2, 3-BPG concentrations are very sensitive to the energy demand of the cell and; (5) the flux through the 2,3-BPG shunt does not change in response to different non-glycolytic demands for NADH.

  • model of 2 3 bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations1 in vivo kinetic characterization of 2 3 bisphosphoglycerate synthase phosphatase using 13c and 31p nmr
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, William A Bubb, Philip W Kuchel
    Abstract:

    This is the first in a series of three papers [see also Mulquiney and Kuchel (1999) Biochem. J. 342, 579-594; Mulquiney and Kuchel (1999) Biochem. J. 342, 595-602] that present a detailed mathematical model of Erythrocyte Metabolism which explains the regulation and control of 2,3-bisphosphoglycerate (2,3-BPG) Metabolism. 2,3-BPG is a modulator of haemoglobin oxygen affinity and hence plays an important role in blood oxygen transport and delivery. This paper presents an in vivo kinetic characterization of 2,3-BPG synthase/phosphatase (BPGS/P), the enzyme that catalyses both the synthesis and degradation of 2,3-BPG. Much previous work had indicated that the behaviour of this enzyme in vitro is markedly different from that in vivo. (13)C and (31)P NMR were used to monitor the time courses of selected metabolites when Erythrocytes were incubated with or without [U-(13)C]glucose. Simulations of the experimental time courses were then made. By iteratively changing the parameters of the BPGS/P part of the model until a good match between the NMR-derived data and simulations were achieved, it was possible to characterize BPGS/P kinetically in vivo. This work revealed that: (1) the pH-dependence of the synthase activity results largely from a strong co-operative inhibition of the synthase activity by protons; (2) 3-phosphoglycerate and 2-phosphoglycerate are much weaker inhibitors of 2,3-BPG phosphatase in vivo than in vitro; (3) the K(m) of BPGS/P for 2,3-BPG is significantly higher than that measured in vitro; (4) the maximal activity of the phosphatase in vivo is approximately twice that in vitro, when P(i) is the sole activator (second substrate); and (5) 2-phosphoglycollate appears to play no role in the activation of the phosphatase in vivo. Using the newly determined kinetic parameters, the percentage of glycolytic carbon flux that passes through the 2, 3-BPG shunt in the normal in vivo steady state was estimated to be 19%.

  • Model of 2,3-bisphosphoglycerate Metabolism in the human Erythrocyte based on detailed enzyme kinetic equations: equations and parameter refinement.
    Biochemical Journal, 1999
    Co-Authors: Peter Mulquiney, Philip W Kuchel
    Abstract:

    Over the last 25 years, several mathematical models of Erythrocyte Metabolism have been developed. Although these models have identified the key features in the regulation and control of Erythrocyte Metabolism, many important aspects remain unexplained. In particular, none of these models have satisfactorily accounted for 2,3-bisphosphoglycerate (2,3-BPG) Metabolism. 2,3-BPG is an important modulator of haemoglobin oxygen affinity, and hence an understanding of the regulation of 2,3-BPG concentration is important for understanding blood oxygen transport. A detailed, comprehensive, and hence realistic mathematical model of Erythrocyte Metabolism is presented that can explain the regulation and control of 2,3-BPG concentration and turnover. The model is restricted to the core metabolic pathways, namely glycolysis, the 2,3-BPG shunt and the pentose phosphate pathway (PPP), and includes membrane transport of metabolites, the binding of metabolites to haemoglobin and Mg(2+), as well as pH effects on key enzymic reactions and binding processes. The model is necessarily complex, since it is intended to describe the regulation and control of 2,3-BPG Metabolism under a wide variety of physiological and experimental conditions. In addition, since H(+) and blood oxygen tension are important external effectors of 2,3-BPG concentration, it was important that the model take into account the large array of kinetic and binding phenomena that result from changes in these effectors. Through an iterative loop of experimental and simulation analysis many values of enzyme-kinetic parameters of the model were refined to yield close conformity between model simulations and 'real' experimental data. This iterative process enabled a single set of parameters to be found which described well the metabolic behaviour of the Erythrocyte under a wide variety of conditions.

Bernhard O. Palsson - One of the best experts on this subject based on the ideXlab platform.

  • a multi scale computational platform to mechanistically assess the effect of genetic variation on drug responses in human Erythrocyte Metabolism
    PLOS Computational Biology, 2016
    Co-Authors: Nathan Mih, Elizabeth Brunk, Aarash Bordbar, Bernhard O. Palsson
    Abstract:

    Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of Metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in Metabolism.

  • A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism - Fig 3
    2016
    Co-Authors: Nathan Mih, Elizabeth Brunk, Aarash Bordbar, Bernhard O. Palsson
    Abstract:

    a) Protein structure of COMT (WT) from PDB entry 3BWM. In orange—crystallized position of an inhibitor analog, dinitrocatechol (DNC). In blue, cofactors needed for catalysis, S-adenosyl-methionine (SAM) and magnesium (Mg). In red, the position of the SNP (contained in PDB entry 3BWY). Zoom in—shows the active site of the enzyme with the crystallized DNC bound. b) Protein structure of G6PD (WT) from PDB entry 2BH9. In orange—crystallized position of the metabolite glucose-6-phosphate (G6P). In blue, the cofactor NADP+. In red, the position of the SNP. Zoom in—shows the active site of the enzyme with G6P bound. c) Protein structure of GAPDH (WT) from PDB entry 1U8F. The orange arrow indicates the known binding site of the metabolite glyceraldehyde-3-phosphate (G3P), which was not crystallized in the experimental structure. In blue, the cofactor NAD+. In red, the position of the SNV. Zoom in—binding site interactions of G3P in E. coli PDB entry 1DC4.

  • A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism - Fig 5
    2016
    Co-Authors: Nathan Mih, Elizabeth Brunk, Aarash Bordbar, Bernhard O. Palsson
    Abstract:

    a) Systems modeling framework used in this study. Inputs used for constraint-based and kinetic modeling are derived from molecular modeling calculations and experimental data when available. In order to understand how small-scale changes from enzyme variants affect the entire system, we look at the internal system changes (in reaction flux and metabolite concentration), differences in metabolite import & export, and how the cell handles an increase in oxidative or energy loads. Oxidative load is defined as the conversion of NADPH to NADP+, whose rate of reaction is increased under states of oxidative stress. Energy load is defined as the use of ATP. For all panels, the change in metabolic flux is colored by a difference from the wild-type flux state, red being a decreased flux in the mutant state and blue being an increased flux. b) Constraint-based modeling for the mutant COMT enzyme. The SNP is predicted to decrease the binding affinity of the enzyme in norepinephrine and dopamine Metabolism. Increasing the Km (predicted) of COMT for the respective reactions leads to decreased flux and as a result decreased export of their methylated counterparts. Inhibitors tolcapone (TCW) and entacapone (ENT) are also predicted to have a lowered binding affinity to COMT, leading to similar effects. c) Kinetic modeling for the mutant G6PD enzyme. Decreases of the Km (predicted and experimental) and of the Kcat (experimental) lead to major systemic changes of the pentose phosphate pathway and glycolysis. The ratio of NADPH to NADP+ greatly decreases and subsequently the oxidative load able to be handled also decreases. d) Kinetic modeling for the mutant GAPDH enzyme. The cell is unable to handle the predicted increase in Km (predicted) and results in an infeasible state of the model, corresponding to cell lysis.

  • iab rbc 283 a proteomically derived knowledge base of Erythrocyte Metabolism that can be used to simulate its physiological and patho physiological states
    BMC Systems Biology, 2011
    Co-Authors: Aarash Bordbar, Neema Jamshidi, Bernhard O. Palsson
    Abstract:

    The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human Erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored. Erythrocyte proteomic data was used to reconstruct the most expansive description of Erythrocyte Metabolism to date, following extensive manual curation, assessment of the literature, and functional testing. The reconstruction contains 281 enzymes representing functions from glycolysis to cofactor and amino acid Metabolism. Such a comprehensive view of Erythrocyte Metabolism implicates the Erythrocyte as a potential biomarker for different diseases as well as a 'cell-based' drug-screening tool. The analysis shows that 94 Erythrocyte enzymes are implicated in morbid single nucleotide polymorphisms, representing 142 pathologies. In addition, over 230 FDA-approved and experimental pharmaceuticals have enzymatic targets in the Erythrocyte. The advancement of proteomic technologies and increased generation of high-throughput proteomic data have created the need for a means to analyze these data in a coherent manner. Network reconstructions provide a systematic means to integrate and analyze proteomic data in a biologically meaning manner. Analysis of the red cell proteome has revealed an unexpected level of complexity in the functional capabilities of human Erythrocyte Metabolism.

  • iab rbc 283 a proteomically derived knowledge base of Erythrocyte Metabolism that can be used to simulate its physiological and patho physiological states
    BMC Systems Biology, 2011
    Co-Authors: Aarash Bordbar, Neema Jamshidi, Bernhard O. Palsson
    Abstract:

    Background The development of high-throughput technologies capable of whole cell measurements of genes, proteins, and metabolites has led to the emergence of systems biology. Integrated analysis of the resulting omic data sets has proved to be hard to achieve. Metabolic network reconstructions enable complex relationships amongst molecular components to be represented formally in a biologically relevant manner while respecting physical constraints. In silico models derived from such reconstructions can then be queried or interrogated through mathematical simulations. Proteomic profiling studies of the mature human Erythrocyte have shown more proteins present related to metabolic function than previously thought; however the significance and the causal consequences of these findings have not been explored.

Kelly L Drew - One of the best experts on this subject based on the ideXlab platform.

  • red blood cell metabolic responses to torpor and arousal in the hibernator arctic ground squirrel
    Journal of Proteome Research, 2019
    Co-Authors: Sarah Gehrke, Travis Nemkov, Davide Stefanoni, Sarah A Rice, Rebecca Wilkerso, Julie A Reisz, Kirk C Hanse, Alfredo Lucas, Pedro Cabrales, Kelly L Drew
    Abstract:

    Arctic ground squirrels provide a unique model to investigate metabolic responses to hibernation in mammals. During winter months these rodents are exposed to severe hypothermia, prolonged fasting, and hypoxemia. In the light of their role in oxygen transport/off-loading and owing to the absence of nuclei and organelles (and thus de novo protein synthesis capacity), mature red blood cells have evolved metabolic programs to counteract physiological or pathological hypoxemia. However, red blood cell Metabolism in hibernation has not yet been investigated. Here we employed targeted and untargeted metabolomics approaches to investigate Erythrocyte Metabolism during entrance to torpor to arousal, with a high resolution of the intermediate time points. We report that torpor and arousal promote Metabolism through glycolysis and pentose phosphate pathway, respectively, consistent with previous models of oxygen-dependent metabolic modulation in mature Erythrocytes. Erythrocytes from hibernating squirrels showed up...

H G Holzhutter - One of the best experts on this subject based on the ideXlab platform.

  • mathematical modelling of red blood cell enzyme deficiencies as an example for large scale parameter changes in biochemical reaction systems
    1996
    Co-Authors: R Schuster, H G Holzhutter
    Abstract:

    There are numerous examples showing that the Metabolism of cells can be severely impaired if the activity of only one of the participating enzymes undergoes large-scale alterations resulting, for example, from spontaneous mutations (inherited or aquired enzymopathies), administration of toxic drugs or self-inactivation of enzymes during cell aging. Beside these unavoidably occuring natural changes of enzyme-kinetic properties, there is substantial interest in medicine and biotechnology to modify the kinetic properties of enzymes in order to manipulate the Metabolism and functional performance of specific cells. In all these areas one important subject of mathematically oriented theoretical research is to quantify the metabolic changes caused by changing the activity of a given enzyme in a defined manner. The theoretical approach presented in this paper is based on a comprehensive mathematical model of Erythrocyte Metabolism encompassing the main pathways of cellular energy and redox Metabolism. The decision for the Erythrocyte was made in the light of the long tradition and advanced level reached in the mathematical modelling of the Metabolism of this cell type (e.g. Rapoport et ai., 1976; Joshi and Palsson, 1990; Schuster et ai., 1988). A second reason was the great number of various enzyme deficiencies which have been elucidated during the last three decades (cf.Valentine et ai., 1983; Tanaka and Zerez, 1990) and which represent an excellent basis for comparing computational results with ‘experiments’ done by nature. Hitherto deficiencies of about 20 enzymes of human Erythrocytes associated with widely different degrees of severity and complexity have been identified (Valentine et al., 1983; Fujii and Miwa, 1990). We define a ‘homeostasis function’ which takes into account the metabolic entities essential for cell integrity. This function is used for predicting the range of enzyme activities in which the metabolic alterations should be either tolerable, associated with non-chronic or chronic hemolytic diseases, or letal.

  • use of mathematical models for predicting the metabolic effect of large scale enzyme activity alterations application to enzyme deficiencies of red blood cells
    FEBS Journal, 1995
    Co-Authors: R Schuster, H G Holzhutter
    Abstract:

    There are numerous examples showing that the Metabolism of cells can be severely impaired if the activity of only one of the participating enzymes undergoes large-scale alterations, resulting, for example, from spontaneous mutations (inherited or aquired enzymopathies), the administration of toxic drugs or self-inactivation of enzymes during cell aging. However, a quantitative relationship between the degree of enzyme deficiency and the extent of metabolic dysfunction is very difficult to establish by experimental means. An alternative is to tackle this problem by mathematical modelling. Our approach is based on a comprehensive mathematical model of the energy and redox Metabolism for human Erythrocytes. We calculate stationary states of the cell Metabolism, varying the activity of each of the participating enzymes by several orders of magnitude. The metabolic states are then evaluated in terms of a performance function which relates the metabolic variables to the overall functional fitness of the cell. The performance function for the Erythrocyte takes into account the homeostasis of three essential metabolic variables: the energetic state (ATP), the reductive capacity (reduced glutathione), and the osmotic state. Based on the behaviour of the performance function at varying enzyme activities, we estimate those ranges of enzyme activities, in which the metabolic alterations should be either tolerable, associated with non-chronic or chronic diseases, or letal. For most enzymopathies, the experimental and clinical observations can be satisfactorily rationalized by the computational results. Moreover, a surprisingly high correlation is found between the range of the activity range where disease is predicted by the model and the observed number of diseased probands. Another objective of our study was to contribute to the theory of metabolic control. The well-elaborated concept of the metabolic control theory is restricted to (infinitely) small activity alterations. In order to quantify the metabolic effect of finite (large-scale) changes in the activity of an enzyme, we propose, as a control measure, the effective activity Ea, defined as the relative activity of an enzyme (with respect to the activity in a reference state) required to bring about a change in the stationary value of a metabolic variable by the (finite) factor α. We demonstrate that none of the existing extrapolation methods using the conventional control coefficient is capable to provide reliable predictions of the effective activities for all enzymes of Erythrocyte Metabolism.