Soil mobility of synthetic and virus-based model nanopesticides.

Large doses of chemical pesticides are required to achieve effective concentrations in the rhizosphere, which results in the accumulation of harmful residues. Precision farming is needed to improve the efficacy of pesticides, but also to avoid environmental pollution, and slow-release formulations based on nanoparticles offer one solution. Here, we tested the mobility of synthetic and virus-based model nanopesticides by combining soil column experiments with computational modelling. We found that the tobacco mild green mosaic virus and cowpea mosaic virus penetrate soil to a depth of at least 30 cm, and could therefore deliver nematicides to the rhizosphere, whereas the Physalis mosaic virus remains in the first 4 cm of soil and would be more useful for the delivery of herbicides. Our experiments confirm that plant viruses are superior to synthetic mesoporous silica nanoparticles and poly(lactic-co-glycolic acid) for the delivery and controlled release of pesticides, and could be developed as the next generation of pesticide delivery systems.

Whole-body iron transport and metabolism: Mechanistic, multi-scale model to improve treatment of anemia in chronic kidney disease.

Iron plays vital roles in the human body including enzymatic processes, oxygen-transport via hemoglobin and immune response. Iron metabolism is characterized by ~95% recycling and minor replenishment through diet. Anemia of chronic kidney disease (CKD) is characterized by a lack of synthesis of erythropoietin leading to reduced red blood cell (RBC) formation and aberrant iron recycling. Treatment of CKD anemia aims to normalize RBC count and serum hemoglobin. Clinically, the various fluxes of iron transport and accumulation are not measured so that changes during disease (e.g., CKD) and treatment are unknown. Unwanted iron accumulation in patients is known to lead to adverse effects. Current whole-body models lack the mechanistic details of iron transport related to RBC maturation, transferrin (Tf and TfR) dynamics and assume passive iron efflux from macrophages. Hence, they are not predictive of whole-body iron dynamics and cannot be used to design individualized patient treatment. For prediction, we developed a mechanistic, multi-scale computational model of whole-body iron metabolism incorporating four compartments containing major pools of iron and RBC generation process. The model accounts for multiple forms of iron in vivo, mechanisms involved in iron uptake and release and their regulation. Furthermore, the model is interfaced with drug pharmacokinetics to allow simulation of treatment dynamics. We calibrated our model with experimental and clinical data from peer-reviewed literature to reliably simulate CKD anemia and the effects of current treatment involving combination of epoietin-alpha and iron dextran. This in silico whole-body model of iron metabolism predicts that a year of treatment can potentially lead to 90% downregulation of ferroportin (FPN) levels, 15-fold increase in iron stores with only a 20% increase in iron flux from the reticulo-endothelial system (RES). Model simulations quantified unmeasured iron fluxes, previously unknown effects of treatment on FPN-level and iron stores in the RES. This mechanistic whole-body model can be the basis for future studies that incorporate iron metabolism together with related clinical experiments. Such an approach could pave the way for development of effective personalized treatment of CKD anemia.

Mathematical modelling of glycosaminoglycan production by stem cell aggregates incorporated with growth factor-releasing polymer microspheres.

Systems composed of high density cells incorporated with growth factor-releasing polymer microspheres have recently been shown to promote chondrogenic differentiation and cartilage formation. Within these systems, the effects of spatial and temporal patterning of growth factor release on hyaline cartilage-specific extracellular matrix production have been examined. However, at present, it is unclear which microsphere densities and growth factor delivery profiles are optimal for inducing human mesenchymal stem cell differentiation and glycosaminoglycan production. A mathematical model to describe glycosaminoglycan production as a function of initial microsphere loading and microsphere degradation rate over a period of 3 weeks is presented. Based on predictions generated by this model, it may be feasible to design a bioactive microsphere system with specific spatiotemporal growth factor presentation characteristics to promote glycosaminoglycan production at controllable rates. Copyright © 2014 John Wiley & Sons, Ltd.

Diffusion and Uptake of Tobacco Mosaic Virus as Therapeutic Carrier in Tumor Tissue: Effect of Nanoparticle Aspect Ratio.

Nanoparticle-based technologies, including platforms derived from plant viruses, hold great promise for targeting and delivering cancer therapeutics to solid tumors by overcoming dose-limiting toxicities associated with chemotherapies. A growing body of data indicates advantageous margination and penetration properties of high aspect-ratio nanoparticles, which enhance payload delivery, resulting in increased efficacy. Our lab has demonstrated that elongated rod-shaped and filamentous macromolecular nucleoprotein assemblies from plant viruses have higher tissue diffusion rates than spherical particles. In this study, we developed a mathematical model to quantify diffusion and uptake of tobacco mosaic virus (TMV) in a spheroid system approximating a capillary-free segment of a solid tumor. Model simulations predict TMV concentration distribution with time in a tumor spheroid for different sizes and cell densities. From simulations of TMV concentration distribution, we can quantify the effect of TMV aspect ratio (e.g., nanorod length-to-width) with and without cellular uptake by modulated surface chemistry. This theoretical analysis can be applied to other viral or nonviral delivery systems to complement the experimental development of the next generation of nanotherapeutics.

Relating tissue/organ energy expenditure to metabolic fluxes in mouse and human: experimental data integrated with mathematical modeling.

Mouse models of human diseases are used to study the metabolic and physiological processes leading to altered whole-body energy expenditure (EE), which is the sum of EE of all body organs and tissues. Isotopic techniques, arterio-venous difference of substrates, oxygen, and blood flow measurements can provide essential information to quantify tissue/organ EE and substrate oxidation. To complement and integrate experimental data, quantitative mathematical model analyses have been applied in the design of experiments and evaluation of metabolic fluxes. In this study, a method is presented to quantify the energy expenditure of the main mouse organs using metabolic flux measurements. The metabolic fluxes and substrate utilization of the main metabolic pathways of energy metabolism in the mouse tissue/organ systems and the whole body are quantified using a mathematical model based on mass and energy balances. The model is composed of six organ/tissue compartments: brain, heart, liver, gastrointestinal tract, muscle, and adipose tissue. Each tissue/organ is described with a distinct system of metabolic reactions. This model quantifies metabolic and energetic characteristics of mice under overnight fasting conditions. The steady-state mass balances of metabolites and energy balances of carbohydrate and fat are integrated with available experimental data to calculate metabolic fluxes, substrate utilization, and oxygen consumption in each tissue/organ. The model serves as a paradigm for designing experiments with the minimal reliable measurements necessary to quantify tissue/organs fluxes and to quantify the contributions of tissue/organ EE to whole-body EE that cannot be easily determined currently.

Computational modeling and analysis of iron release from macrophages.

A major process of iron homeostasis in whole-body iron metabolism is the release of iron from the macrophages of the reticuloendothelial system. Macrophages recognize and phagocytose senescent or damaged erythrocytes. Then, they process the heme iron, which is returned to the circulation for reutilization by red blood cell precursors during erythropoiesis. The amount of iron released, compared to the amount shunted for storage as ferritin, is greater during iron deficiency. A currently accepted model of iron release assumes a passive-gradient with free diffusion of intracellular labile iron (Fe2+) through ferroportin (FPN), the transporter on the plasma membrane. Outside the cell, a multi-copper ferroxidase, ceruloplasmin (Cp), oxidizes ferrous to ferric ion. Apo-transferrin (Tf), the primary carrier of soluble iron in the plasma, binds ferric ion to form mono-ferric and di-ferric transferrin. According to the passive-gradient model, the removal of ferrous ion from the site of release sustains the gradient that maintains the iron release. Subcellular localization of FPN, however, indicates that the role of FPN may be more complex. By experiments and mathematical modeling, we have investigated the detailed mechanism of iron release from macrophages focusing on the roles of the Cp, FPN and apo-Tf. The passive-gradient model is quantitatively analyzed using a mathematical model for the first time. A comparison of experimental data with model simulations shows that the passive-gradient model cannot explain macrophage iron release. However, a facilitated-transport model associated with FPN can explain the iron release mechanism. According to the facilitated-transport model, intracellular FPN carries labile iron to the macrophage membrane. Extracellular Cp accelerates the oxidation of ferrous ion bound to FPN. Apo-Tf in the extracellular environment binds to the oxidized ferrous ion, completing the release process. Facilitated-transport model can correctly predict cellular iron efflux and is essential for physiologically relevant whole-body model of iron metabolism.

Mechanistic, mathematical model to predict the dynamics of tissue genesis in bone defects via mechanical feedback and mediation of biochemical factors.

The link between mechanics and biology in the generation and the adaptation of bone has been well studied in context of skeletal development and fracture healing. Yet, the prediction of tissue genesis within - and the spatiotemporal healing of - postnatal defects, necessitates a quantitative evaluation of mechano-biological interactions using experimental and clinical parameters. To address this current gap in knowledge, this study aims to develop a mechanistic mathematical model of tissue genesis using bone morphogenetic protein (BMP) to represent of a class of factors that may coordinate bone healing. Specifically, we developed a mechanistic, mathematical model to predict the dynamics of tissue genesis by periosteal progenitor cells within a long bone defect surrounded by periosteum and stabilized via an intramedullary nail. The emergent material properties and mechanical environment associated with nascent tissue genesis influence the strain stimulus sensed by progenitor cells within the periosteum. Using a mechanical finite element model, periosteal surface strains are predicted as a function of emergent, nascent tissue properties. Strains are then input to a mechanistic mathematical model, where mechanical regulation of BMP-2 production mediates rates of cellular proliferation, differentiation and tissue production, to predict healing outcomes. A parametric approach enables the spatial and temporal prediction of endochondral tissue regeneration, assessed as areas of cartilage and mineralized bone, as functions of radial distance from the periosteum and time. Comparing model results to histological outcomes from two previous studies of periosteum-mediated bone regeneration in a common ovine model, it was shown that mechanistic models incorporating mechanical feedback successfully predict patterns (spatial) and trends (temporal) of bone tissue regeneration. The novel model framework presented here integrates a mechanistic feedback system based on the mechanosensitivity of periosteal progenitor cells, which allows for modeling and prediction of tissue regeneration on multiple length and time scales. Through combination of computational, physical and engineering science approaches, the model platform provides a means to test new hypotheses in silico and to elucidate conditions conducive to endogenous tissue genesis. Next generation models will serve to unravel intrinsic differences in bone genesis by endochondral and intramembranous mechanisms.

Modeling and experimental methods to predict oxygen distribution in bone defects following cell transplantation.

We have developed a mathematical model that allows simulation of oxygen distribution in a bone defect as a tool to explore the likely effects of local changes in cell concentration, defect size or geometry, local oxygen delivery with oxygen-generating biomaterials (OGBs), and changes in the rate of oxygen consumption by cells within a defect. Experimental data for the oxygen release rate from an OGB and the oxygen consumption rate of a transplanted cell population are incorporated into the model. With these data, model simulations allow prediction of spatiotemporal oxygen concentration within a given defect and the sensitivity of oxygen tension to changes in critical variables. This information may help to minimize the number of experiments in animal models that determine the optimal combinations of cells, scaffolds, and OGBs in the design of current and future bone regeneration strategies. Bone marrow-derived nucleated cell data suggest that oxygen consumption is dependent on oxygen concentration. OGB oxygen release is shown to be a time-dependent function that must be measured for accurate simulation. Simulations quantify the dependency of oxygen gradients in an avascular defect on cell concentration, cell oxygen consumption rate, OGB oxygen generation rate, and OGB geometry.

Distinguishing the effects of convective and diffusive O2 delivery on VO2 on-kinetics in skeletal muscle contracting at moderate intensity.

With current techniques, experimental measurements alone cannot characterize the effects of oxygen blood-tissue diffusion on muscle oxygen uptake (Vo2) kinetics in contracting skeletal muscle. To complement experimental studies, a computational model is used to quantitatively distinguish the contributions of convective oxygen delivery, diffusion into cells, and oxygen utilization to Vo2 kinetics. The model is validated using previously published experimental Vo2 kinetics in response to slowed blood flow (Q) on-kinetics in canine muscle (τQ = 20 s, 46 s, and 64 s) [Goodwin ML, Hernández A, Lai N, Cabrera ME, Gladden LB. J Appl Physiol. 112:9-19, 2012]. Distinctive effects of permeability-surface area or diffusive conductance (PS) and Q on Vo2 kinetics are investigated. Model simulations quantify the relationship between PS and Q, as well as the effects of diffusion associated with PS and Q dynamics on the mean response time of Vo2. The model indicates that PS and Q are linearly related and that PS increases more with Q when convective delivery is limited by slower Q dynamics. Simulations predict that neither oxygen convective nor diffusive delivery are limiting Vo2 kinetics in the isolated canine gastrocnemius preparation under normal spontaneous conditions during transitions from rest to moderate (submaximal) energy demand, although both operate close to the tipping point.

Computational Model of Cellular Metabolic Dynamics in Skeletal Muscle Fibers during Moderate Intensity Exercise.

Human skeletal muscles have different fiber types with distinct metabolic functions and physiological properties. The quantitative metabolic responses of muscle fibers to exercise provide essential information for understanding and modifying the regulatory mechanisms of skeletal muscle. Since in vivo data from skeletal muscle during exercise is limited, a computational, physiologically based model has been developed to quantify the dynamic metabolic responses of many key chemical species. This model distinguishes type I and II muscle fibers, which share the same blood supply. An underlying hypothesis is that the recruitment and metabolic activation of the two main types of muscle fibers differ depending on the pre-exercise state and exercise protocols. Here, activation measured by metabolic response (or enzymatic activation) in single fibers is considered linked but distinct from fiber recruitment characterized by the number (or mass) of each fiber type involved during a specific exercise. The model incorporates species transport processes between blood and muscle fibers and most of the important reactions/pathways in cytosol and mitochondria within each fiber type. Model simulations describe the dynamics of intracellular species concentrations and fluxes in muscle fibers during moderate intensity exercise according to various experimental protocols and conditions. This model is validated by comparing model simulations with experimental data in single muscle fibers and in whole muscle. Model simulations demonstrate that muscle-fiber recruitment and metabolic activation patterns in response to exercise produce significantly distinctive effects depending on the exercise conditions.

Model analysis of the relationship between intracellular PO2 and energy demand in skeletal muscle.

On the basis of experimental studies, the intracellular O(2) (iPo(2))-work rate (WR) relationship in skeletal muscle is not unique. One study found that iPo(2) reached a plateau at 60% of maximal WR, while another found that iPo(2) decreased linearly at higher WR, inferring capillary permeability-surface area (PS) and blood-tissue O(2) gradient, respectively, as alternative dominant factors for determining O(2) diffusion changes during exercise. This relationship is affected by several factors, including O(2) delivery and oxidative and glycolytic capacities of the muscle. In this study, these factors are examined using a mechanistic, mathematical model to analyze experimental data from contracting skeletal muscle and predict the effects of muscle contraction on O(2) transport, glycogenolysis, and iPo(2). The model describes convection, O(2) diffusion, and cellular metabolism, including anaerobic glycogenolysis. Consequently, the model simulates iPo(2) in response to muscle contraction under a variety of experimental conditions. The model was validated by comparison of simulations of O(2) uptake with corresponding experimental responses of electrically stimulated canine muscle under different O(2) content, blood flow, and contraction intensities. The model allows hypothetical variation of PS, glycogenolytic capacity, and blood flow and predictions of the distinctive effects of these factors on the iPo(2)-contraction intensity relationship in canine muscle. Although PS is the main factor regulating O(2) diffusion rate, model simulations indicate that PS and O(2) gradient have essential roles, depending on the specific conditions. Furthermore, the model predicts that different convection and diffusion patterns and metabolic factors may be responsible for different iPo(2)-WR relationships in humans.

Coding region polyadenylation generates a truncated tRNA synthetase that counters translation repression.

Posttranscriptional regulatory mechanisms superimpose "fine-tuning" control upon "on-off" switches characteristic of gene transcription. We have exploited computational modeling with experimental validation to resolve an anomalous relationship between mRNA expression and protein synthesis. The GAIT (gamma-interferon-activated inhibitor of translation) complex repressed VEGF-A synthesis to a low, constant rate independent of VEGF-A mRNA expression levels. Dynamic model simulations predicted an inhibitory GAIT-element-interacting factor to account for this relationship and led to the identification of a truncated form of glutamyl-prolyl tRNA synthetase (EPRS), a GAIT constituent that mediates binding to target transcripts. The truncated protein, EPRS(N1), shields GAIT-element-bearing transcripts from the inhibitory GAIT complex, thereby dictating a "translational trickle" of GAIT target proteins. EPRS(N1) mRNA is generated by polyadenylation-directed conversion of a Tyr codon in the EPRS-coding sequence to a stop codon (PAY(∗)). Genome-wide analysis revealed multiple candidate PAY(∗) targets, including the authenticated target RRM1, suggesting a general mechanism for production of C terminus-truncated regulatory proteins.

Experimental studies and modeling of drug release from a tunable affinity-based drug delivery platform.

An affinity-based drug delivery platform for controlling drug release is analyzed by a combination of experimental studies and mathematical modeling. This platform has the ability to form selective interactions between a therapeutic agent and host matrix that yields advantages over systems that employ nonselective methods. The incorporation of molecular interactions in drug delivery can increase the therapeutic lifetime of drug delivery implants and limit the need for multiple implants in treatment of chronic illnesses. To analyze this complex system for rational design of drug delivery implants, we developed a mechanistic mathematical model to quantify the molecular events and processes. With a ß-cyclodextrin hydrogel host matrix, defined release rates were obtained using a fluorescent model drug. The key processes were the complexation between the drug and cyclodextrin and diffusion of the drug in the hydrogel. Optimal estimates of the model parameters were obtained by minimizing the difference between model simulation and experimentally measured drug release kinetics. Model simulations could predict the drug release dynamics under a wide range of experimental conditions.

Regulation of cytosolic and mitochondrial oxidation via malate-aspartate shuttle: an observation using dynamic ¹³C NMR spectroscopy.

The malate-aspartate (M-A) shuttle provides an important mechanism of metabolic communication between the cytosol and the mitochondria. In this study, dynamic (13)C NMR spectroscopy was combined with a multi-domain model of cardiac metabolism for direct quantification of metabolic fluxes through the tricarboxylic acid (TCA) cycle (VTCA) and the M-A shuttle (VM-A) in intact heart. The sensitivity of this approach to altered M-A shuttle activity was examined at different cytosolic redox states. Dynamic (13)C NMR spectra were acquired from isolated rat hearts perfused with (13)C labeled fatty acid at either low (fatty acid only) or high cytosolic redox state induced by exogenous glucose and lactate. VTCA and VM-A were determined by least-square fitting of the model to NMR data. Our results showed that while VTCA was similar, VM-A increased by 75% at high cytosolic redox state. Therefore, our proposed method provides the opportunity for direct quantification of metabolic communication between subcellular compartments via the M-A shuttle.

Hemoglobin and myoglobin contributions to skeletal muscle oxygenation in response to exercise.

The quantitative contributions of hemoglobin and myoglobin oxygenation in skeletal muscle depend on physiological factors, especially muscle blood flow (Q( m )) and capillary permeability-surface area (PS). Near-infrared spectroscopy (NIRS) can be used to quantify total heme oxidation, but it is unable to distinguish between hemoglobin and myoglobin. Therefore, a mechanistic computational model has been developed to distinguish the contributions of oxygenated hemoglobin and myoglobin to the total NIRS signal. Model simulations predict how Q( m ) and PS can affect oxygenated hemoglobin and myoglobin.Although both hemoglobin and myoglobin oxygenation decrease with impaired Q( m ), simulations show that myoglobin provides a greater contribution to the overall NIRS signal. A decrease of PS primarily affects myoglobin oxygenation. Based on model simulations, the contribution of myoglobin oxygenation to the total NIRS signal can be significantly different under pathophysiological conditions, such as diabetes and peripheral arterial disorder.

Regulation of Adipose Tissue Metabolism in Humans: Analysis of Responses to the Hyperinsulinemic-Euglycemic Clamp Experiment.

The suppression of lipolysis is one of the key metabolic responses of the adipose tissue during hyperinsulinemia. The failure to respond and resulting increase in plasma fatty acids could contribute to the development of insulin resistance and perturbations in the fuel homeostasis in the whole body. In this study, a mechanistic, computational model of adipose tissue metabolism in vivo has been enhanced to simulate the physiological responses during hyperinsulinemic-euglycemic clamp experiment in humans. The model incorporates metabolic intermediates and pathways that are important in the fed state. In addition, it takes into account the heterogeneity of triose phosphate pools (glycolytic vs. glyceroneogenic), within the adipose tissue. The model can simulate not only steady-state responses at different insulin levels, but also concentration dynamics of major metabolites in the adipose tissue venous blood in accord with the in vivo data. Simulations indicate that (1) regulation of lipoprotein lipase (LPL) reaction is important when the intracellular lipolysis is suppressed by insulin; (2) intracellular diglyceride levels can affect the regulatory mechanisms; and (3) glyceroneogenesis is the dominant pathway for glycerol-3-phosphate synthesis even in the presence of increased glucose uptake by the adipose tissue. Reduced redox and increased phosphorylation states provide a favorable milieu for glyceroneogenesis in response to insulin. A parameter sensitivity analysis predicts that insulin-stimulated glucose uptake would be more severely affected by impairment of GLUT4 translocation and glycolysis than by impairment of glycogen synthesis and pyruvate oxidation. Finally, simulations predict metabolic responses to altered expression of phosphoenolpyruvate carboxykinase (PEP-CK). Specifically, the increase in the rate of re-esterification of fatty acids observed experimentally with the overexpression of PEPCK in the adipose tissue would be accompanied by the up-regulation of acyl Co-A synthase.

Modeling of laser coagulation of tissue with MRI temperature monitoring.

Light energy from a laser source that is delivered into body tissue via a fiber-optic probe with minimal invasiveness has been used to ablate solid tumors. This thermal coagulation process can be guided and monitored accurately by continuous magnetic resonance imaging (MRI) since the laser energy delivery system does not interfere with MRI. This report deals with mathematical modeling and analysis of laser coagulation of tissue. This model is intended for "real-time" analysis of magnetic resonance images obtained during the coagulation process to guide clinical treatment. A mathematical model is developed to simulate the thermal response of tissue to a laser light heating source. For fast simulation, an approximate solution of the thermal model is used to predict the dynamics of temperature distribution and tissue damage induced by a laser energy line source. The validity of these simulations is tested by comparison with MRI-based temperature data acquired from in vivo experiments in rabbits. The model-simulated temperature distribution and predicted lesion dynamics correspond closely with MRI-based data. These results demonstrate the potential for using this combination of fast modeling and MRI technologies during laser heating of tissue for online prediction of tumor lesion size during laser heating.

Computational model of cellular metabolic dynamics: effect of insulin on glucose disposal in human skeletal muscle.

Identifying the mechanisms by which insulin regulates glucose metabolism in skeletal muscle is critical to understanding the etiology of insulin resistance and type 2 diabetes. Our knowledge of these mechanisms is limited by the difficulty of obtaining in vivo intracellular data. To quantitatively distinguish significant transport and metabolic mechanisms from limited experimental data, we developed a physiologically based, multiscale mathematical model of cellular metabolic dynamics in skeletal muscle. The model describes mass transport and metabolic processes including distinctive processes of the cytosol and mitochondria. The model simulated skeletal muscle metabolic responses to insulin corresponding to human hyperinsulinemic-euglycemic clamp studies. Insulin-mediated rate of glucose disposal was the primary model input. For model validation, simulations were compared with experimental data: intracellular metabolite concentrations and patterns of glucose disposal. Model variations were simulated to investigate three alternative mechanisms to explain insulin enhancements: Model 1 (M.1), simple mass action; M.2, insulin-mediated activation of key metabolic enzymes (i.e., hexokinase, glycogen synthase, pyruvate dehydrogenase); or M.3, parallel activation by a phenomenological insulin-mediated intracellular signal that modifies reaction rate coefficients. These simulations indicated that models M.1 and M.2 were not sufficient to explain the experimentally measured metabolic responses. However, by application of mechanism M.3, the model predicts metabolite concentration changes and glucose partitioning patterns consistent with experimental data. The reaction rate fluxes quantified by this detailed model of insulin/glucose metabolism provide information that can be used to evaluate the development of type 2 diabetes.

Modeling oxygenation in venous blood and skeletal muscle in response to exercise using near-infrared spectroscopy.

Noninvasive, continuous measurements in vivo are commonly used to make inferences about mechanisms controlling internal and external respiration during exercise. In particular, the dynamic response of muscle oxygenation (Sm(O(2))) measured by near-infrared spectroscopy (NIRS) is assumed to be correlated to that of venous oxygen saturation (Sv(O(2))) measured invasively. However, there are situations where the dynamics of Sm(O(2)) and Sv(O(2)) do not follow the same pattern. A quantitative analysis of venous and muscle oxygenation dynamics during exercise is necessary to explain the links between different patterns observed experimentally. For this purpose, a mathematical model of oxygen transport and utilization that accounts for the relative contribution of hemoglobin (Hb) and myoglobin (Mb) to the NIRS signal was developed. This model includes changes in microvascular composition within skeletal muscle during exercise and integrates experimental data in a consistent and mechanistic manner. Three subjects (age 25.6 +/- 0.6 yr) performed square-wave moderate exercise on a cycle ergometer under normoxic and hypoxic conditions while muscle oxygenation (C(oxy)) and deoxygenation (C(deoxy)) were measured by NIRS. Under normoxia, the oxygenated Hb/Mb concentration (C(oxy)) drops rapidly at the onset of exercise and then increases monotonically. Under hypoxia, C(oxy) decreases exponentially to a steady state within approximately 2 min. In contrast, model simulations of venous oxygen concentration show an exponential decrease under both conditions due to the imbalance between oxygen delivery and consumption at the onset of exercise. Also, model simulations that distinguish the dynamic responses of oxy-and deoxygenated Hb (HbO(2), HHb) and Mb (MbO(2), HMb) concentrations (C(oxy) = HbO(2) + MbO(2); C(deoxy) = HHb + HMb) show that Hb and Mb contributions to the NIRS signal are comparable. Analysis of NIRS signal components during exercise with a mechanistic model of oxygen transport and metabolism indicates that changes in oxygenated Hb and Mb are responsible for different patterns of Sm(O(2)) and Sv(O(2)) dynamics observed under normoxia and hypoxia.