Simultaneous blood-tissue exchange of oxygen, carbon dioxide, bicarbonate, and hydrogen ion

A computational model of skeletal muscle metabolism linking cellular adaptations induced by altered loading states to metabolic responses during exercise

BACKGROUND

The alterations in skeletal muscle structure and function after prolonged periods of unloading are initiated by the chronic lack of mechanical stimulus of sufficient intensity, which is the result of a series of biochemical and metabolic interactions spanning from cellular to tissue/organ level. Reduced activation of skeletal muscle alters the gene expression of myosin heavy chain isoforms to meet the functional demands of reduced mechanical load, which results in muscle atrophy and reduced capacity to process fatty acids. In contrast, chronic loading results in the opposite pattern of adaptations.

METHODS

To quantify interactions among cellular and skeletal muscle metabolic adaptations, and to predict metabolic responses to exercise after periods of altered loading states, we develop a computational model of skeletal muscle metabolism. The governing model equations - with parameters characterizing chronic loading/unloading states- were solved numerically to simulate metabolic responses to moderate intensity exercise (WR < or = 40% VO2 max).

RESULTS

Model simulations showed that carbohydrate oxidation was 8.5% greater in chronically unloaded muscle compared with the loaded muscle (0.69 vs. 0.63 mmol/min), while fat oxidation was 7% higher in chronically loaded muscle (0.14 vs. 0.13 mmol/min), during exercise. Muscle oxygen uptake (VO2) and blood flow (Q) response times were 29% and 44% shorter in chronically loaded muscle (0.4 vs. 0.56 min for VO2 and 0.25 vs. 0.45 min for Q).

CONCLUSION

The present model can be applied to test complex hypotheses during exercise involving the integration and control of metabolic processes at various organizational levels (cellular to tissue) in individuals who have undergone periods of chronic loading or unloading.

Bayesian flux balance analysis applied to a skeletal muscle metabolic model

In this article, the steady state condition for the multi-compartment models for cellular metabolism is considered. The problem is to estimate the reaction and transport fluxes, as well as the concentrations in venous blood when the stoichiometry and bound constraints for the fluxes and the concentrations are given. The problem has been addressed previously by a number of authors, and optimization-based approaches as well as extreme pathway analysis have been proposed. These approaches are briefly discussed here. The main emphasis of this work is a Bayesian statistical approach to the flux balance analysis (FBA). We show how the bound constraints and optimality conditions such as maximizing the oxidative phosphorylation flux can be incorporated into the model in the Bayesian framework by proper construction of the prior densities. We propose an effective Markov chain Monte Carlo (MCMC) scheme to explore the posterior densities, and compare the results with those obtained via the previously studied linear programming (LP) approach. The proposed methodology, which is applied here to a two-compartment model for skeletal muscle metabolism, can be extended to more complex models.

Linking pulmonary oxygen uptake, muscle oxygen utilization and cellular metabolism during exercise

The energy demand imposed by physical exercise on the components of the oxygen transport and utilization system requires a close link between cellular and external respiration in order to maintain ATP homeostasis. Invasive and non-invasive experimental approaches have been used to elucidate mechanisms regulating the balance between oxygen supply and consumption during exercise. Such approaches suggest that the mechanism controlling the various subsystems coupling internal to external respiration are part of a highly redundant and hierarchical multi-scale system. In this work, we present a "systems biology" framework that integrates experimental and theoretical approaches able to provide simultaneously reliable information on the oxygen transport and utilization processes occurring at the various steps in the pathway of oxygen from air to mitochondria, particularly at the onset of exercise. This multi-disciplinary framework provides insights into the relationship between cellular oxygen consumption derived from measurements of muscle oxygenation during exercise and pulmonary oxygen uptake by indirect calorimetry. With a validated model, muscle oxygen dynamic responses is simulated and quantitatively related to cellular metabolism under a variety of conditions.

Multi-organ system model of O2 and CO2 transport during isocapnic and poikilocapnic hypoxia

A multi-organ systems model of O(2) and CO(2) transport is developed to analyze the control of ventilation and blood flow during hypoxia. Among the aspects of the control processes that this model addressed are possible mechanisms responsible for the second phase of the ventilatory hypoxic response to mild hypoxia, i.e., hypoxic ventilatory decline (HVD). Species mass transport processes are described by compartmental mass balances in brain, heart, skeletal muscle, and "other tissues" connected in parallel via the circulation. In pulmonary and systemic capillaries and in the vasculature connecting the systemic tissues, species transport processes are represented by a one-dimensional, convection-dispersion model. The effects of bicarbonate acid-base buffering, hemoglobin, and myoglobin on the transport processes are included. The model incorporates feedback control mechanisms through a cardiorespiratory control system in which peripheral and central chemoreceptors sense O(2) and CO(2) partial pressures. Model simulations of the ventilatory responses to isocapnic and poikilocapnic hypoxia show two phases with distinct dynamics. A fast phase is discernable immediately after switching from normoxic to hypoxic conditions, while a delayed slow phase (HVD) typically becomes manifested after several minutes. Model simulations allow quantitative evaluation of several proposed mechanisms to account for HVD. Under isocapnic hypoxia, simulations indicate that an increase in brain blood flow has no effect on HVD, but that HVD can be entirely described by central ventilatory depression (CVD). Under poikilocapnic hypoxia, the hypocapnia caused by hypoxic hyperventilation has no effect on HVD.

Multi-scale computational model of fuel homeostasis during exercise: effect of hormonal control

A mathematical model of the whole-body metabolism is developed to predict fuel homeostasis during exercise by using hormonal control over cellular metabolic processes. The whole body model is composed of seven tissue compartments: brain, heart, liver, GI (gastrointestinal) tract, skeletal muscle, adipose tissue, and "other tissues". Each tissue compartment is described by dynamic mass balances and major cellular metabolic reactions. The glucagon-insulin controller is incorporated into the whole body model to predict hormonal changes during exercise. Moderate [150 W power output at 60% of peak oxygen consumption (VO(2max))] exercise for 60 min was implemented by increasing ATP utilization rates in heart and skeletal muscle. Arterial epinephrine level was given as an input function, which directly affects heart and skeletal muscle metabolism and indirectly other tissues via glucagon-insulin controller. Model simulations were validated with experimental data from human exercise studies. The exercise induced changes in hormonal signals modulated metabolic flux rates of different tissues in a coordinated way to achieve glucose homeostasis, demonstrating the efficacy of hormonal control over cellular metabolic processes. From experimental measurements of whole body glucose balance and arterial substrate concentrations, this model could predict the dynamic changes of hepatic glycogenolysis and gluconeogenesis, which are not easy to measure experimentally, suggesting the higher contribution of glycogenolysis ( approximately 75%). In addition, it could provide dynamic information on the relative contribution of carbohydrates and lipids for fuel oxidation in skeletal muscle. Model simulations indicate that external fuel supplies from other tissue/organ systems to skeletal muscle become important for prolonged exercise emphasizing the significance of interaction among tissues. In conclusion, this model can be used as a valuable complement to experimental studies due to its ability to predict what is difficult to measure directly, and usefulness to provide information about dynamic behaviors.

Relating pulmonary oxygen uptake to muscle oxygen consumption at exercise onset: in vivo and in silico studies

Assessment of the rate of muscle oxygen consumption, UO(2m), in vivo during exercise involving a large muscle mass is critical for investigating mechanisms regulating energy metabolism at exercise onset. While UO(2m) is technically difficult to obtain under these circumstances, pulmonary oxygen uptake, VO(2p), can be readily measured and used as a proxy to UO(2m). However, the quantitative relationship between VO(2p) and UO(2m) during the nonsteady phase of exercise in humans, needs to be established. A computational model of oxygen transport and utilization--based on dynamic mass balances in blood and tissue cells--was applied to quantify the dynamic relationship between model-simulated UO(2m) and measured VO(2p) during moderate (M), heavy (H), and very heavy (V) intensity exercise. In seven human subjects, VO(2p) and muscle oxygen saturation, StO(2m), were measured with indirect calorimetry and near infrared spectroscopy (NIRS), respectively. The dynamic responses of VO(2p) and StO(2m) at each intensity were in agreement with previously published data. The response time of muscle oxygen consumption, tauUO(2m) estimated by direct comparison between model results and measurements of StO(2m) was significantly faster (P < 0.001) than that of pulmonary oxygen uptake, tauVO(2p) (M: 13 +/- 4 vs. 65 +/- 7 s; H: 13 +/- 4 vs. 100 +/- 24 s; V: 15 +/- 5 vs. 82 +/- 31 s). Thus, by taking into account the dynamics of oxygen stores in blood and tissue and determining muscle oxygen consumption from muscle oxygenation measurements, this study demonstrates a significant temporal dissociation between UO(2m) and VO(2p) at exercise onset.

Multiscale modeling of cardiac cellular energetics

Multiscale modeling is essential to integrating knowledge of human physiology starting from genomics, molecular biology, and the environment through the levels of cells, tissues, and organs all the way to integrated systems behavior. The lowest levels concern biophysical and biochemical events. The higher levels of organization in tissues, organs, and organism are complex, representing the dynamically varying behavior of billions of cells interacting together. Models integrating cellular events into tissue and organ behavior are forced to resort to simplifications to minimize computational complexity, thus reducing the model's ability to respond correctly to dynamic changes in external conditions. Adjustments at protein and gene regulatory levels shortchange the simplified higher-level representations. Our cell primitive is composed of a set of subcellular modules, each defining an intracellular function (action potential, tricarboxylic acid cycle, oxidative phosphorylation, glycolysis, calcium cycling, contraction, etc.), composing what we call the "eternal cell," which assumes that there is neither proteolysis nor protein synthesis. Within the modules are elements describing each particular component (i.e., enzymatic reactions of assorted types, transporters, ionic channels, binding sites, etc.). Cell subregions are stirred tanks, linked by diffusional or transporter-mediated exchange. The modeling uses ordinary differential equations rather than stochastic or partial differential equations. This basic model is regarded as a primitive upon which to build models encompassing gene regulation, signaling, and long-term adaptations in structure and function. During simulation, simpler forms of the model are used, when possible, to reduce computation. However, when this results in error, the more complex and detailed modules and elements need to be employed to improve model realism. The processes of error recognition and of mapping between different levels of model form complexity are challenging but are essential for successful modeling of large-scale systems in reasonable time. Currently there is to this end no established methodology from computational sciences.