Cellular energetics analysis by a mathematical model of energy balance: estimation of parameters in human skeletal muscle.

Cellular energy balance requires that the physiological demands by ATP-utilizing functions be matched by ATP synthesis to sustain muscle activity. We devised a new method of analysis of these processes in data from single individuals. Our approach is based on the logic of current information on the major mechanisms involved in this energy balance and can quantify not directly measurable parameters that govern those mechanisms. We use a mathematical model that simulates by ordinary, nonlinear differential equations three components of cellular bioenergetics (cellular ATP flux, mitochondrial oxidative phosphorylation, and creatine kinase kinetics). We incorporate data under resting conditions, during the transition toward a steady state of stimulation and during the transition during recovery back to the original resting state. Making use of prior information about the kinetic parameters, we fitted the model to previously published dynamic phosphocreatine (PCr) and inorganic phosphate (P(i)) data obtained in normal subjects with an activity-recovery protocol using (31)P nuclear magnetic resonance spectroscopy. The experiment consisted of a baseline phase, an ischemic phase (during which muscle stimulation and PCr utilization occurred), and an aerobic recovery phase. The model described satisfactorily the kinetics of the changes in PCr and P(i) and allowed estimation of the maximal velocity of oxidative phosphorylation and of the net ATP flux in individuals both at rest and during stimulation. This work lays the foundation for a quantitative, model-based approach to the study of in vivo muscle energy balance in intact muscle systems, including human muscle.

[1]  E. Hultman,et al.  Glycogen, glycolytic intermediates and high-energy phosphates determined in biopsy samples of musculus quadriceps femoris of man at rest. Methods and variance of values. , 1974, Scandinavian journal of clinical and laboratory investigation.

[2]  C. Reggiani,et al.  Myofibrillar ATPase activity in skinned human skeletal muscle fibres: fibre type and temperature dependence. , 1996, The Journal of physiology.

[3]  B. Chance,et al.  Metabolic control principles and 31P NMR. , 1986, Federation proceedings.

[4]  M. Johnson,et al.  Data on the distribution of fibre types in thirty-six human muscles. An autopsy study. , 1973, Journal of the neurological sciences.

[5]  K. Gunter,et al.  Mitochondrial calcium transport: physiological and pathological relevance. , 1994, The American journal of physiology.

[6]  M. Kushmerick,et al.  Mammalian skeletal muscle fibers distinguished by contents of phosphocreatine, ATP, and Pi. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[7]  T. Richards,et al.  Activation of glycolysis in human muscle in vivo. , 1997, The American journal of physiology.

[8]  T. Moerland,et al.  Diffusion coefficients of atp and creatine phosphate in isolated muscle: pulsed gradient 31p nmr of small biological samples , 1995, NMR in biomedicine.

[9]  B. Saltin,et al.  Maximal perfusion of skeletal muscle in man. , 1985, The Journal of physiology.

[10]  M. Kushmerick,et al.  Glycolysis is independent of oxygenation state in stimulated human skeletal muscle in vivo , 1998, The Journal of physiology.

[11]  W. Cleland,et al.  Inhibition of creatine kinase by chromium nucleotides. , 1973, The Journal of biological chemistry.

[12]  R. Wevers,et al.  A Method for Quantitative Measurement of Mitochondrial Creatine Kinase in Human Skeletal Muscle , 1992, Annals of Clinical Biochemistry.

[13]  G Sjøgaard,et al.  Extra- and intracellular water spaces in muscles of man at rest and with dynamic exercise. , 1982, The American journal of physiology.

[14]  C Cobelli,et al.  SAAM II: Simulation, Analysis, and Modeling Software for tracer and pharmacokinetic studies. , 1998, Metabolism: clinical and experimental.

[15]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[16]  D. Koshland Switches, thresholds and ultrasensitivity , 1987 .

[17]  M J Kushmerick,et al.  Activity of creatine kinase in a contracting mammalian muscle of uniform fiber type. , 1994, Biophysical journal.

[18]  D. Pette,et al.  The Molecular Diversity of Mammalian Muscle Fibers , 1993 .

[19]  L. Rowell,et al.  Exercise : regulation and integration of multiple systems , 1996 .

[20]  Adnan M. Awad,et al.  Properties of the Akaike information criterion , 1996 .

[21]  G K Radda,et al.  The use of NMR spectroscopy for the understanding of disease. , 1986, Science.

[22]  M J Kushmerick,et al.  Energy balance in muscle activity: simulations of ATPase coupled to oxidative phosphorylation and to creatine kinase. , 1998, Comparative biochemistry and physiology. Part B, Biochemistry & molecular biology.

[23]  H V Westerhoff,et al.  The Signal Transduction Function for Oxidative Phosphorylation Is at Least Second Order in ADP* , 1996, The Journal of Biological Chemistry.

[24]  E. H. Twizell The mathematical modeling of metabolic and endocrine systems: E.R. Carson, C. Cobelli and L. Finkelstein John Wiley and Sons, Chichester, Sussex, UK, 394 pp., £45.15, 1983 , 1984 .

[25]  M. Kushmerick Multiple equilibria of cations with metabolites in muscle bioenergetics. , 1997, The American journal of physiology.

[26]  R. Connett,et al.  A simple model of aerobic metabolism: applications to work transitions in muscle. , 1990, The American journal of physiology.

[27]  P. Esselman,et al.  Individual variation in contractile cost and recovery in a human skeletal muscle. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[28]  R. Veech,et al.  Effects of pH and free Mg2+ on the Keq of the creatine kinase reaction and other phosphate hydrolyses and phosphate transfer reactions. , 1979, The Journal of biological chemistry.

[29]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[30]  C. Cobelli,et al.  Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. , 1980, The American journal of physiology.

[31]  F. Plum Handbook of Physiology. , 1960 .

[32]  R. Hansford Role of calcium in respiratory control. , 1994, Medicine and science in sports and exercise.

[33]  M. Kushmerick,et al.  Separate measures of ATP utilization and recovery in human skeletal muscle. , 1993, The Journal of physiology.

[34]  R. Meyer,et al.  A linear model of muscle respiration explains monoexponential phosphocreatine changes. , 1988, The American journal of physiology.

[35]  J. Burke,et al.  A relative weighting method for estimating parameters and variances in multiple data sets , 1996 .

[36]  L. Larsson,et al.  Maximum velocity of shortening in relation to myosin isoform composition in single fibres from human skeletal muscles. , 1993, The Journal of physiology.

[37]  N. Secher,et al.  31P-NMR spectroscopy, rsEMG, and histochemical fiber types of human wrist flexor muscles. , 1994, Journal of applied physiology.

[38]  E. Ravussin,et al.  Whole-body energy metabolism and skeletal muscle biochemical characteristics. , 1994, Metabolism: clinical and experimental.

[39]  G. Kitagawa,et al.  Akaike Information Criterion Statistics , 1988 .

[40]  S Nioka,et al.  Multiple controls of oxidative metabolism in living tissues as studied by phosphorus magnetic resonance. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Claudio Cobelli,et al.  Minimal model SGoverestimation and SIunderestimation: improved accuracy by a Bayesian two-compartment model. , 1999, American journal of physiology. Endocrinology and metabolism.

[42]  B. Locke,et al.  Reaction-diffusion analysis of the effects of temperature on high-energy phosphate dynamics in goldfish skeletal muscle. , 1997, The Journal of experimental biology.

[43]  D. Koshland,et al.  Amplification and adaptation in regulatory and sensory systems. , 1982, Science.