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Background: Acute myelogenous leukemia (AML) progresses uniquely in each patient. However, patients are typically treated with the same types of chemotherapy, despite biological differences that lead to differential responses to treatment., Results: Here we present a multi-lineage multi-compartment model of the hematopoietic system that captures patient-to-patient variation in both the concentration and rates of change of hematopoietic cell populations. By constraining the model against clinical hematopoietic cell recovery data derived from patients who have received induction chemotherapy, we identified trends for parameters that must be met by the model; for example, the mitosis rates and the probability of self-renewal of progenitor cells are inversely related. Within the data-consistent models, we found 22,796 parameter sets that meet chemotherapy response criteria. Simulations of these parameter sets display diverse dynamics in the cell populations. To identify large trends in these model outputs, we clustered the simulated cell population dynamics using k-means clustering and identified thirteen 'representative patient' dynamics. In each of these patient clusters, we simulated AML and found that clusters with the greatest mitotic capacity experience clinical cancer outcomes more likely to lead to shorter survival times. Conversely, other parameters, including lower death rates or mobilization rates, did not correlate with survival times., Conclusions: Using the multi-lineage model of hematopoiesis, we have identified several key features that determine leukocyte homeostasis, including self-renewal probabilities and mitosis rates, but not mobilization rates. Other influential parameters that regulate AML model behavior are responses to cytokines/growth factors produced in peripheral blood that target the probability of self-renewal of neutrophil progenitors. Finally, our model predicts that the mitosis rate of cancer is the most predictive parameter for survival time, followed closely by parameters that affect the self-renewal of cancer stem cells; most current therapies target mitosis rate, but based on our results, we propose that additional therapeutic targeting of self-renewal of cancer stem cells will lead to even higher survival rates.

This model-based design of experiments (MBDOE) method determines the input magnitudes of an experimental stimuli to apply and the associated measurements that should be taken to optimally constrain the uncertain dynamics of a biological system under study. The ideal global solution for this experiment design problem is generally computationally intractable because of parametric uncertainties in the mathematical model of the biological system. Others have addressed this issue by limiting the solution to a local estimate of the model parameters. Here we present an approach that is independent of the local parameter constraint. This approach is made computationally efficient and tractable by the use of: (1) sparse grid interpolation that approximates the biological system dynamics, (2) representative parameters that uniformly represent the data-consistent dynamical space, and (3) probability weights of the represented experimentally distinguishable dynamics. Our approach identifies data-consistent representative parameters using sparse grid interpolants, constructs the optimal input sequence from a greedy search, and defines the associated optimal measurements using a scenario tree. We explore the optimality of this MBDOE algorithm using a 3-dimensional Hes1 model and a 19-dimensional T-cell receptor model. The 19-dimensional T-cell model also demonstrates the MBDOE algorithm's scalability to higher dimensions. In both cases, the dynamical uncertainty region that bounds the trajectories of the target system states were reduced by as much as 86% and 99% respectively after completing the designed experiments in silico. Our results suggest that for resolving dynamical uncertainty, the ability to design an input sequence paired with its associated measurements is particularly important when limited by the number of measurements.

The kinase Syk is intricately involved in early signaling events in B cells and is required for proper response when antigens bind to B cell receptors (BCRs). Experiments using an analog-sensitive version of Syk (Syk-AQL) have better elucidated its role, but have not completely characterized its behavior. We present a computational model for BCR signaling, using dynamical systems, which incorporates both wild-type Syk and Syk-AQL. Following the use of sensitivity analysis to identify significant reaction parameters, we screen for parameter vectors that produced graded responses to BCR stimulation as is observed experimentally. We demonstrate qualitative agreement between the model and dose response data for both mutant and wild-type kinases. Analysis of our model suggests that the level of NF- κ B activation, which is reduced in Syk-AQL cells relative to wild-type, is more sensitive to small reductions in kinase activity than Erkp activation, which is essentially unchanged. Since this profile of high Erkp and reduced NF- κ B is consistent with anergy, this implies that anergy is particularly sensitive to small changes in catalytic activity. Also, under a range of forward and reverse ligand binding rates, our model of Erkp and NF- κ B activation displays a dependence on a power law affinity: the ratio of the forward rate to a non-unit power of the reverse rate. This dependence implies that B cells may respond to certain details of binding and unbinding rates for ligands rather than simple affinity alone.

Mathematical modeling of developmental signaling networks has played an increasingly important role in the identification of regulatory mechanisms by providing a sandbox for hypothesis testing and experiment design. Whether these models consist of an equation with a few parameters or dozens of equations with hundreds of parameters, a prerequisite to model-based discovery is to bring simulated behavior into agreement with observed data via parameter estimation. These parameters provide insight into the system (e.g., enzymatic rate constants describe enzyme properties). Depending on the nature of the model fit desired - from qualitative (relative spatial positions of phosphorylation) to quantitative (exact agreement of spatial position and concentration of gene products) - different measures of data-model mismatch are used to estimate different parameter values, which contain different levels of usable information and/or uncertainty. To facilitate the adoption of modeling as a tool for discovery alongside other tools such as genetics, immunostaining, and biochemistry, careful consideration needs to be given to how well a model fits the available data, what the optimized parameter values mean in a biological context, and how the uncertainty in model parameters and predictions plays into experiment design. The core discussion herein pertains to the quantification of model-to-data agreement, which constitutes the first measure of a model's performance and future utility to the problem at hand. Integration of this experimental data and the appropriate choice of objective measures of data-model agreement will continue to drive modeling forward as a tool that contributes to experimental discovery. The Drosophila melanogaster gap gene system, in which model parameters are optimized against in situ immunofluorescence intensities, demonstrates the importance of error quantification, which is applicable to a wide array of developmental modeling studies., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects.

Objective: Bystander cardiopulmonary resuscitation (CPR) provides treatment for out-of-hospital cardiac arrest since perfusion of vital organs is critical to resuscitation. Alternatives to standard CPR are evaluated for effectiveness based upon outcome predictive metrics and survival studies. This study focuses on evaluating the performance of rhythmic-only abdominal compression CPR (OAC-CPR) relative to chest compression (CC-CPR) using a complementary suite of mechanistically based CPR outcome predictors. Combined, these predictors provide insight on the transduction of compression-induced pressures into flow perfusing vital organs., Methods: Intrasubject comparisons between the CPR techniques were made during multiple 2-min intervals of induced fibrillation in 17 porcine subjects. Arterial pO2, cardiac output, carotid blood flow, coronary perfusion pressure (CPP), minute alveolar ventilation (MAV), end-tidal CO2, and time from defibrillation to the return of spontaneous circulation (ROSC) were recorded. Organ damage was assessed by necropsy., Results: Compared with CC-CPR, OAC-CPR had higher pressure and ventilation metrics with increased relative CPP (+16 mm Hg), MAV (+75/ml/min/kg) and a lower reduction in arterial pO2(-22% baseline), but suffered from lower carotid flows (-9.3 ml/min). No significant difference was found comparing cardiac outputs. Furthermore, resuscitation was qualitatively more difficult after OAC-CPR, with a longer time to ROSC (+70 s). No abdominal damage was observed over short periods of OAC-CPR., Conclusions: Although OAC-CPR appeared superior to CC-CPR by pressure and ventilation metrics, lower carotid flow and longer delay until ROSC raise concerns about overall performance. These paradoxical observations suggest that the evaluation of efficacious alternative CPR techniques may require more direct measurements of vital organ perfusion., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Computational approaches to tune the activation of intracellular signal transduction pathways both predictably and selectively will enable researchers to explore and interrogate cell biology with unprecedented precision. Techniques to control complex nonlinear systems typically involve the application of control theory to a descriptive mathematical model. For cellular processes, however, measurement assays tend to be too time consuming for real-time feedback control and models offer rough approximations of the biological reality, thus limiting their utility when considered in isolation. We overcome these problems by combining nonlinear model predictive control with a novel adaptive weighting algorithm that blends predictions from multiple models to derive a compromise open-loop control sequence. The proposed strategy uses weight maps to inform the controller of the tendency for models to differ in their ability to accurately reproduce the system dynamics under different experimental perturbations (i.e. control inputs). These maps, which characterize the changing model likelihoods over the admissible control input space, are constructed using preexisting experimental data and used to produce a model-based open-loop control framework. In effect, the proposed method designs a sequence of control inputs that force the signaling dynamics along a predefined temporal response without measurement feedback while mitigating the effects of model uncertainty. We demonstrate this technique on the well-known Erk/MAPK signaling pathway in T cells. In silico assessment demonstrates that this approach successfully reduces target tracking error by 52% or better when compared with single model-based controllers and non-adaptive multiple model-based controllers. In vitro implementation of the proposed approach in Jurkat cells confirms a 63% reduction in tracking error when compared with the best of the single-model controllers. This study provides an experimentally-corroborated control methodology that utilizes the knowledge encoded within multiple mathematical models of intracellular signaling to design control inputs that effectively direct cell behavior in open-loop.

Discovery in developmental biology is often driven by intuition that relies on the integration of multiple types of data such as fluorescent images, phenotypes, and the outcomes of biochemical assays. Mathematical modeling helps elucidate the biological mechanisms at play as the networks become increasingly large and complex. However, the available data is frequently under-utilized due to incompatibility with quantitative model tuning techniques. This is the case for stem cell regulation mechanisms explored in the Drosophila germarium through fluorescent immunohistochemistry. To enable better integration of biological data with modeling in this and similar situations, we have developed a general parameter estimation process to quantitatively optimize models with qualitative data. The process employs a modified version of the Optimal Scaling method from social and behavioral sciences, and multi-objective optimization to evaluate the trade-off between fitting different datasets (e.g. wild type vs. mutant). Using only published imaging data in the germarium, we first evaluated support for a published intracellular regulatory network by considering alternative connections of the same regulatory players. Simply screening networks against wild type data identified hundreds of feasible alternatives. Of these, five parsimonious variants were found and compared by multi-objective analysis including mutant data and dynamic constraints. With these data, the current model is supported over the alternatives, but support for a biochemically observed feedback element is weak (i.e. these data do not measure the feedback effect well). When also comparing new hypothetical models, the available data do not discriminate. To begin addressing the limitations in data, we performed a model-based experiment design and provide recommendations for experiments to refine model parameters and discriminate increasingly complex hypotheses.

We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in W. Dong et al. (PLoS ONE 3(8):e3105, 2008) and independently in M.M. Donahue et al. (IET Syst. Biol. 4:249-262, 2010). In this approach, existing data is used to define a probability distribution on the parameters; the next measurement point is the one that yields the largest model output variance with this distribution. Building upon this approach, we introduce the Expected Dynamics Estimator (EDE), which is the expected value using this distribution of the output as a function of time. We prove the consistency of this estimator (uniform convergence to true dynamics) even when the chosen experiments cluster in a finite set of points. We extend this proof of consistency to various practical assumptions on noisy data and moderate levels of model mismatch. Through the derivation and proof, we develop a relaxed version of MINE that is more computationally tractable and robust than the original formulation. The results are illustrated with numerical examples on two nonlinear ordinary differential equation models of biomolecular and cellular processes.

A computationally efficient model-based design of experiments (MBDOE) strategy is developed to plan an optimal experiment by specifying the experimental stimulation magnitudes and measurement points. The strategy is extended from previous work which optimized the experimental design over a space of measurable species and time points. We include system inputs (stimulation conditions) in the experiment design search to investigate if the addition of perturbations enhances the ability of the MBDOE method to resolve uncertainties in system dynamics. The MBDOE problem is made computationally tractable by using a sparse-grid approximation of the model output dynamics, pre-specifying the time points at which the input or experimental perturbations can be applied, and creating scenario trees to explore the endogenous uncertainty. Consecutive scenario trees are used to determine the best input magnitudes and select the optimal associated measurement species and time points. We demonstrate the effectiveness of this strategy on a T-Cell Receptor (TCR) signaling pathway model.

The hypothalamic-pituitary-adrenal (HPA) axis is critical in maintaining homeostasis under physical and psychological stress by modulating cortisol levels in the body. Dysregulation of cortisol levels is linked to numerous stress-related disorders. In this paper, an automated treatment methodology is proposed, employing a variant of nonlinear model predictive control (NMPC), called explicit MPC (EMPC). The controller is informed by an unknown input observer (UIO), which estimates various hormonal levels in the HPA axis system in conjunction with the magnitude of the stress applied on the body, based on measured concentrations of adreno-corticotropic hormones (ACTH). The proposed closed-loop control strategy is tested on multiple in silico patients and the effectiveness of the controller performance is demonstrated.

Model-based design of experiments (MBDOE) assists in the planning of highly effective and efficient experiments. Although the foundations of this field are well-established, the application of these techniques to understand cellular processes is a fertile and rapidly advancing area as the community seeks to understand ever more complex cellular processes and systems. This review discusses the MBDOE paradigm along with applications and challenges within the context of cellular processes and systems. It also provides a brief tutorial on Fisher information matrix (FIM)-based and Bayesian experiment design methods along with an overview of existing software packages and computational advances that support MBDOE application and adoption within the Systems Biology community. As cell-based products and biologics progress into the commercial sector, it is anticipated that MBDOE will become an essential practice for design, quality control, and production., (Copyright © 2013 Wiley Periodicals, Inc.)

Model-based experiment design specifies the data to be collected that will most effectively characterize the biological system under study. Existing model-based design of experiment algorithms have primarily relied on Fisher Information Matrix-based methods to choose the best experiment in a sequential manner. However, these are largely local methods that require an initial estimate of the parameter values, which are often highly uncertain, particularly when data is limited. In this paper, we provide an approach to specify an informative sequence of multiple design points (parallel design) that will constrain the dynamical uncertainty of the biological system responses to within experimentally detectable limits as specified by the estimated experimental noise. The method is based upon computationally efficient sparse grids and requires only a bounded uncertain parameter space; it does not rely upon initial parameter estimates. The design sequence emerges through the use of scenario trees with experimental design points chosen to minimize the uncertainty in the predicted dynamics of the measurable responses of the system. The algorithm was illustrated herein using a T cell activation model for three problems that ranged in dimension from 2D to 19D. The results demonstrate that it is possible to extract useful information from a mathematical model where traditional model-based design of experiments approaches most certainly fail. The experiments designed via this method fully constrain the model output dynamics to within experimentally resolvable limits. The method is effective for highly uncertain biological systems characterized by deterministic mathematical models with limited data sets. Also, it is highly modular and can be modified to include a variety of methodologies such as input design and model discrimination.

Quantitative methods such as model-based predictive control are known to facilitate the design of strategies to manipulate biological systems. This study develops a sparse-grid-based adaptive model predictive control (MPC) strategy to direct HL60 cellular differentiation. Sparse-grid sampling and interpolation support a computationally efficient adaptive MPC scheme in which multiple data-consistent regions of the model parameter space are identified and used to calculate a control compromise. The algorithm is evaluated in silico with structural model mismatch. Simulations demonstrate how the multiscenario control strategy more effectively manages the mismatch compared to a single scenario approach. Furthermore, the controller is evaluated in vitro to differentiate HL60 cells in both normal and perturbed environments. The controller-derived input sequence successfully achieves and sustains the specified target level of granulocytes when implemented in the laboratory. The results and analysis given here imply that adoption of this experiment planning technique to direct cell differentiation within more complex tissue engineered constructs will require the use of a reasonably accurate mathematical model and an extension of this algorithm to multiobjective controller design., (© 2011 IEEE)

The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results.

Mitochondrial permeability transition (MPT) is a highly regulated complex phenomenon that is a type of ischemia/reperfusion injury that can lead to cell death and ultimately organ dysfunction. A novel population transition and detailed permeability transition pore regulation model were integrated with an existing bioenergetics model to describe MPT induction under a variety of conditions. The framework of the MPT induction model includes the potential states of the mitochondria (aggregated, orthodox and post-transition), their transitions from one state to another as well as their interaction with the extra-mitochondrial environment. The model encodes the three basic necessary conditions for MPT: a high calcium load, alkaline matrix pH and circumstances which favor de-energization. The MPT induction model was able to reproduce the expected bioenergetic trends observed in a population of mitochondria subjected to conditions that favor MPT. The model was corroborated and used to predict that MPT in an acidic environment is mitigated by an increase in activity of the mitochondrial potassium/hydrogen exchanger. The model was also used to present the beneficial impact of reducing the duration mitochondria spend in the orthodox state on preserving the extra-mitochondrial ATP levels. The model serves as a tool for investigators to use to understand the MPT induction phenomenon, explore alternative hypotheses for PTP regulation, as well as identify endogenous pharmacological targets and evaluate potential therapeutics for MPT mitigation., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

The sparse grid-based experiment design algorithm sequentially selects an experimental design point to discriminate between hypotheses for given experimental conditions. Sparse grids efficiently screen the global uncertain parameter space to identify acceptable parameter subspaces. Clustering the located acceptable parameter vectors by the similarity of the simulated model trajectories characterises the data-compatible model dynamics. The experiment design algorithm capitalizes on the diversity of the experimentally distinguishable system output dynamics to select the design point that best discerns between competing model-structure and parameter-encoded hypotheses. As opposed to designing the experiments to explicitly reduce uncertainty in the model parameters, this approach selects design points to differentiate between dynamical behaviours. This approach further differs from other experimental design methods in that it simultaneously addresses both parameter- and structural-based uncertainty that is applicable to some ill-posed problems where the number of uncertain parameters exceeds the amount of data, places very few requirements on the model type, available data and a priori parameter estimates, and is performed over the global uncertain parameter space. The experiment design algorithm is demonstrated on a mitogen-activated protein kinase cascade model. The results show that system dynamics are highly uncertain with limited experimental data. Nevertheless, the algorithm requires only three additional experimental data points to simultaneously discriminate between possible model structures and acceptable parameter values. This sparse grid-based experiment design process provides a systematic and computationally efficient exploration over the entire uncertain parameter space of potential model structures to resolve the uncertainty in the non-linear systems biology model dynamics.

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective., (Copyright (c) 2010 Elsevier Ltd. All rights reserved.)

Mathematical models of mitochondrial bioenergetics provide powerful analytical tools to help interpret experimental data and facilitate experimental design for elucidating the supporting biochemical and physical processes. As a next step towards constructing a complete physiologically faithful mitochondrial bioenergetics model, a mathematical model was developed targeting the cardiac mitochondrial bioenergetic based upon previous efforts, and corroborated using both transient and steady state data. The model consists of several modified rate functions of mitochondrial bioenergetics, integrated calcium dynamics and a detailed description of the K(+)-cycle and its effect on mitochondrial bioenergetics and matrix volume regulation. Model simulations were used to fit 42 adjustable parameters to four independent experimental data sets consisting of 32 data curves. During the model development, a certain network topology had to be in place and some assumptions about uncertain or unobserved experimental factors and conditions were explicitly constrained in order to faithfully reproduce all the data sets. These realizations are discussed, and their necessity helps contribute to the collective understanding of the mitochondrial bioenergetics.

Obstructive uropathy can cause irreversible renal damage. It has been hypothesized that elevated hydrostatic pressure within renal tubules and/or renal ischemia contributes to cellular injury following obstruction. However, these assaults are essentially impossible to isolate in vivo. Therefore, we developed a novel pressure system to evaluate the isolated and coordinated effects of elevated hydrostatic pressure and ischemic insults on renal cells in vitro. Cells were subjected to: (1) elevated hydrostatic pressure (80 cm H(2)O); (2) ischemic insults (hypoxia (0% O(2)), hypercapnia (20% CO(2)), and 0 mM glucose media); and (3) elevated pressure + ischemic insults. Cellular responses including cell density, lactate dehydrogenase (LDH) release, and intracellular LDH (LDH(i)), were recorded after 24 h of insult and following recovery. Data were analyzed to assess the primary effects of ischemic insults and elevated pressure. Unlike pressure, ischemic insults exerted a primary effect on nearly all response measurements. We also evaluated the data for insult interactions and identified significant interactions between ischemic insults and pressure. Altogether, findings indicate that pressure may sub-lethally effect cells and alter cellular metabolism (LDH(i)) and membrane properties. Results suggest that renal ischemia may be the primary, but not the sole, cause of cellular injury induced by obstructive uropathy.

Objectives: Standard chest-compression CPR has an out-of-hospital resuscitation rate of less than 10% and can result in rib fractures or mouth-to-mouth transfer of infection. Recently, we introduced a new CPR method that utilizes only rhythmic abdominal compressions (OAC-CPR). The present study compares ventilation and hemodynamics produced by chest and abdominal compression CPR., Methods: Twelve swine (29-34kg) were anesthetized, intubated and allowed to breathe spontaneously. Physiologic dead space, resting tidal volume, compression-induced lung air flow, and blood pressures were recorded. Ventricular fibrillation (VF) was electrically induced and subjects were treated with either standard CPR or OAC-CPR at various force and rate settings. Minute alveolar ventilation (MAV) and mean coronary perfusion pressure (CPP) were compared., Results: For OAC-CPR, ventilation per compression tended to increase with increasing force and decreasing rate. Chest only compressions produced no MAV, while OAC-CPR at 80cycles/min or less, matched the MAV for spontaneous respiration. For all rates, abdominal compressions met, or exceeded, the CPP of chest compressions performed at 100lbs., Conclusions: OAC-CPR generated ventilatory volumes significantly greater than the dead space and produced equivalent, or larger, CPP than with chest compressions. Thus, OAC-CPR ventilates a subject, eliminating the need for mouth-to-mouth breathing, and effectively circulates blood during VF without breaking ribs. Furthermore, this technique is simple to perform, can be administered by a single rescuer, and should reduce bystander reluctance to administer CPR.

Background: It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models., Results: This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface., Conclusion: SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.

A methodology is developed that determines age-specific transition rates between cell cycle phases during balanced growth by utilizing age-structured population balance equations. Age-distributed models are the simplest way to account for varied behavior of individual cells. However, this simplicity is offset by difficulties in making observations of age distributions, so age-distributed models are difficult to fit to experimental data. Herein, the proposed methodology is implemented to identify an age-structured model for human leukemia cells (Jurkat) based only on measurements of the total number density after the addition of bromodeoxyuridine partitions the total cell population into two subpopulations. Each of the subpopulations will temporarily undergo a period of unbalanced growth, which provides sufficient information to extract age-dependent transition rates, while the total cell population remains in balanced growth. The stipulation of initial balanced growth permits the derivation of age densities based on only age-dependent transition rates. In fitting the experimental data, a flexible transition rate representation, utilizing a series of cubic spline nodes, finds a bimodal G(0)/G(1) transition age probability distribution best fits the experimental data. This resolution may be unnecessary as convex combinations of more restricted transition rates derived from normalized Gaussian, lognormal, or skewed lognormal transition-age probability distributions corroborate the spline predictions, but require fewer parameters. The fit of data with a single log normal distribution is somewhat inferior suggesting the bimodal result as more likely. Regardless of the choice of basis functions, this methodology can identify age distributions, age-specific transition rates, and transition-age distributions during balanced growth conditions., (Copyright 2007 Wiley Periodicals, Inc.)

Objectives: This study investigated sustained abdominal compression as a means to improve coronary perfusion pressure (CPP) during cardiopulmonary resuscitation (CPR) and compared the resulting CPP augmentation with that achieved using vasopressor drugs., Method: During electrically induced ventricular fibrillation in anesthetized, 30kg juvenile pigs, Thumper CPR was supplemented at intervals either by constant abdominal compression at 100-500mmHg using an inflated contoured cuff or by the administration of vasopressor drugs (epinephrine, vasopressin, or glibenclamide). CPP before and after cuff inflation or drug administration was the end point., Results: Sustained abdominal compression at >200mmHg increases CPP during VF and otherwise standard CPR by 8-18mmHg. The effect persists over practical ranges of chest compression force and duty cycle and is similar to that achieved with vasopressor drugs. Constant abdominal compression also augments CPP after prior administration of epinephrine or vasopressin., Conclusions: During CPR noninvasive abdominal compression with the inflatable contoured cuff rapidly elevates the CPP, sustains the elevated CPP as long as the device is inflated, and is immediately and controllably reversible upon device deflation. Physical control of peripheral vascular resistance during CPR by abdominal compression has some advantages over pharmacological manipulation and deserves serious reconsideration, now that the limitations of pressor drugs during CPR have become better understood, including post-resuscitation myocardial depression and the need for intravenous access.

Ischemia has elicited a great deal of interest among the scientific community due to its role in life-threatening pathologies such as cancer, stroke, acute renal failure, and myocardial infarction. Oxygen deprivation (hypoxia) associated with ischemia has recently become a subject of intense scrutiny. New investigators may find it challenging to induce hypoxic injury in vitro. Researchers may not always be aware of the experimental barriers that contribute to this phenomenon. Furthermore, ischemia is associated with other major insults, such as excess carbon dioxide (hypercapnia), nutrient deprivation, and accumulation of cellular wastes. Ideally, these conditions should also be incorporated into in vitro models. Therefore, the motivation behind this review is to: i. delineate major in vivo ischemic insults; ii. identify and explain critical in vitro parameters that need to be considered when simulating ischemic pathologies; iii. provide recommendations to improve experiments; and as a result, iv. enhance the validity of in vitro results for understanding clinical ischemic pathologies. Undoubtedly, it is not possible to completely replicate the in vivo environment in an ex vivo model system. In fact, the primary goal of many in vitro studies is to elucidate the role of specific stimuli during in vivo pathological events. This review will present methodologies that may be implemented to improve the applicability of in vitro models for understanding the complex pathological mechanisms of ischemia. Finally, although these topics will be discussed within the context of renal ischemia, many are pertinent for cellular models of other organ systems and pathologies.

Objective: The objective of this study was to document the biochemical changes during ventricular fibrillation (VF) with cardiopulmonary resuscitation (CPR), and to identify factors associated with postdefibrillation pulseless electrical activity (PD-PEA)., Background: It has been reliably estimated that as much as 60% of out-of-hospital sudden cardiac death can be attributed to the onset of PD-PEA (Niemann JT, Cruz B, Garner D et al. Immediate countershock versus CPR before countershock in a 5-minute swine model of ventricular fibrillation arrest. Ann Emerg Med 2000;36:543-6). Previous attempts to treat reversible causes of pulseless electrical activity have not been successful clinically (Niemann JT, Stratton SJ, Cruz B, Lewis RJ. Outcome of out-of-hospital postcountershock asystole and pulseless electrical activity versus primary asystole and pulseless electrical activity. Crit Care Med 2001;29:2366-70)., Methods: This investigation used 22 studies on 14 anesthetized pigs breathing 100% oxygen. Ventricular fibrillation was induced with a right ventricular catheter electrode, and the chest was compressed with a pneumatically driven Chest Thumper (Michigan Instruments) (80-100 lb at 60/min). The electrocardiogram and aortic pressure were recorded continuously. Arterial pH, P(O2), P(CO2), Na+, K+, Ca2+, Cl-, SaO2, glucose, hematocrit, and hemoglobin level were measured at selected times. Ventricular defibrillation was achieved with transchest electrodes., Results: Typically, during VF with CPR, mean aortic pressure was 20 to 25 mm Hg. In all cases aortic P(O2) decreased to about 20% of the initial value in 10 minutes, and aortic blood K+ increased by 50% in 6 minutes. By 5 to 8 minutes, the incidence of PD-PEA was 50%., Conclusion: Ventricular fibrillation duration, arterial K+, and arterial P(CO2) were statistically correlated with the onset of PD-PEA in this study. In addition, trends suggest an association of mean arterial blood pressure and arterial P(O2) with the onset of PD-PEA.

Current research efforts to improve immunoassay-biosensor functionality have centered on detection through the optimal design of microfluidic chambers, electrical circuitry, optical sensing elements, and so on. To date, little attention has been paid to the immunoassay-biosensor membrane surface on which interactions between antibodies and antigens must occur. For this reason, the objective of the present study was to manipulate the nanometer surface roughness of a model immunoassay-biosensor membrane to determine its role on sensitivity and specificity. It was hypothesized that surface roughness characteristics similar to those used by the body's own immune system with B-lymphocyte cell membranes would promote antigen-antibody interactions and minimize non-specific binding. To test this hypothesis, polystyrene 96-well plate surfaces were modified to possess similar topographies as those of B-lymphocyte cell membranes. This was accomplished by immobilizing Protein A conjugated gold particles and Protein A conjugated polystyrene particles ranging in sizes from 40 to 860 nm to the bottom of polystyrene wells. Atomic force microscopy results provided evidence of well-dispersed immunoassay-biosensor surfaces for all particles tested with high degrees of biologically inspired nanometer roughness. Testing the functionality of these immunosurfaces using antigenic fluorescent microspheres showed that specific antigen capture increased with greater nanometer surface roughness while nonspecific antigen capture did not correlate with surface roughness. In this manner, results from this study suggest that large degrees of biologically inspired nanometer surface roughness not only increases the amount of immobilized antibodies onto the immunosurface membrane, but it also enhances the functionality of those antibodies for optimal antigen capture, criteria critical for improving immunoassay-biosensor sensitivity and specificity.

Xenogeneic extracellular matrices (ECMs) have been shown to be effective as naturally occurring scaffolds for soft-tissue repair. As acellular tissue substitutes at the time of surgical implantation, ECMs are subjected to the mechanical forces and micro-environmental conditions representative of the anatomical location in which they are placed. Ideally such natural scaffolds would possess mechanical properties that allow for normal tissue function in and around the implant site. The ball-burst test was used to simulate biaxial forces and to determine the strength of the ECM scaffold under a relevant physiological loading condition. The ball-burst test, in itself, does not quantify intrinsic mechanical properties and therefore a methodology was developed to determine the maximum stress resultant tangent modulus (MSRTM) or the maximum stress tangent modulus (MSTM), stress to failure (sigma(f)), failure stress resultant (N(f)), ball-burst pressure (P), and maximum elongation (lambda(max)) from the raw ball-burst data obtained at a constant-rate of transverse. The analytical methodology was compared to finite element simulations and showed good correlation with the analytical solution presented. The proposed approximations were used to compute biaxial failure properties for a variety of multilaminate ECM devices with varying number of layers, disinfection and sterilization, and organ origin.

Xenogeneic extracellular matrix (ECM) can be harvested and configured to function as a bioscaffold for tissue and organ reconstruction. The mechanical properties of the ECM vary depending upon the tissue from which it is harvested. Likewise, the manufacturing steps required to develop ECMs into medical grade devices will affect the surface morphology and the mechanical properties of the bioscaffold; important properties for constructive tissue remodeling. The present study compared the ball-burst strength of five different ECM scaffolds before and after treatment with peracetic acid (PAA): porcine small intestinal submucosa (SIS), porcine urinary bladder submucosa (UBS), porcine urinary bladder matrix (UBM), a composite of UBS + UBM, and canine stomach submucosa (SS). This study also compared the mechanical properties of 2- and 4-layer ECM scaffolds. Results showed 2-layer SS devices had the highest ball-burst value of all 2-layer ECM devices. Moreover, all 4-layer ECM devices had similar ball-burst strength except for 4-layer UBM devices which was the weakest. PAA-treatment decreased the ball-burst strength of SS and increased the ball-burst strength of UBS 2-layer devices. This study showed the material properties of the ECM scaffolds could be engineered to mimic those of native soft tissues (i.e. vascular, musculotendinous, etc) by varying the number of layers and modifying the disinfection/sterilization treatments used for manufacturing.

This paper delineates a systematic method for determining "optimal" intravenous drug delivery strategies for patients having illnesses that primarily evoke a humoral immune response and are treatable by antibiotics. The method derives from a nonlinear, distributed predator-prey model that captures the dominant antigen and antibody interaction. This model is developed from relevant physiology, past predator-prey-type modeling work, available data, and pertinent parameter identification. Embedding this predator-prey model into a larger class of uncertain systems, by a finite dimensional approximation and a transformation to a linear fractional representation, enables the application of robust control based on linear matrix inequality optimization techniques. The optimization problem is solved by minimizing an upper bound on a measure of the total drug delivered subject to patient recovery (stability to healthy equilibrium state). Specifically, the paper addresses the treatment of Haemophilus influenzae through modeling, controller development, and simulations of infected adult patients subjected to typical and proposed intravenous antibiotic treatments. Through simulations the proposed intravenous drug strategy shortens patient recovery time, lowers peak drug concentrations and decreases the total drug administered when compared to standard antibiotic strategies.