Assay for evaluation of proarrhythmic effects of herbal products: Case study with 12 Evodia preparations.

Guidelines for preclinical drug development reduce the occurrence of arrhythmia-related side effects. Besides ample evidence for the presence of arrhythmogenic substances in plants, there is no consensus on a research strategy for the evaluation of proarrhythmic effects of herbal products. Here, we propose a cardiac safety assay for the detection of proarrhythmic effects of plant extracts based on the experimental approaches described in the Comprehensive In vitro Proarrhythmia Assay (CiPA). Microelectrode array studies (MEAs) and voltage sensing optical technique on human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were combined with ionic current measurements in mammalian cell lines, In-silico simulations of cardiac action potentials (APs) and statistic regression analysis. Proarrhythmic effects of 12 Evodia preparations, containing different amounts of the hERG inhibitors dehydroevodiamine (DHE) and hortiamine were analysed. Extracts produced different prolongation of the AP, occurrence of early after depolarisations and triangulation of the AP in hiPSC-CMs depending on the contents of the hERG inhibitors. DHE and hortiamine dose-dependently prolonged the field potential duration in hiPSC-CMs studied with MEAs. In-silico simulations of ventricular AP support a scenario where proarrhythmic effects of Evodia extracts are predominantly caused by the content of the selective hERG inhibitors. Statistic regression analysis revealed a high torsadogenic risk for both compounds that was comparable to drugs assigned to the high-risk category in a CiPA study.

Noise-driven bursting birhythmicity in the Hindmarsh-Rose neuron model.

The stochastic Hindmarsh-Rose model is studied in the parameter region where two bursting limit cycles of different types coexist. We show that under the influence of noise, transitions between basins of attractions appear, which generates stochastic bursting oscillations of mixed modes. The formation of this new regime is accompanied by anti-coherence and coherence resonances as well as by the transition to chaos. We investigate the probabilistic mechanism of the noise-driven bursting birhythmicity using the stochastic sensitivity functions and confidence domains method.

Gut Microbiota Patterns Predicting Long-Term Weight Loss Success in Individuals with Obesity Undergoing Nonsurgical Therapy.

Background: The long-term success of nonsurgical weight reduction programs is variable; thus, predictors of outcome are of major interest. We hypothesized that the intestinal microbiota known to be linked with diet and obesity contain such predictive elements. Methods: Metagenome analysis by shotgun sequencing of stool DNA was performed in a cohort of 15 adults with obesity (mean body mass index 43.1 kg/m2) who underwent a one-year multidisciplinary weight loss program and another year of follow-up. Eight individuals were persistently successful (mean relative weight loss 18.2%), and seven individuals were not successful (0.2%). The relationship between relative abundancies of bacterial genera/species and changes in relative weight loss or body mass index was studied using three different statistical modeling methods. Results: When combining the predictor variables selected by the applied statistical modeling, we identified seven bacterial genera and eight bacterial species as candidates for predicting success of weight loss. By classification of relative weight-loss predictions for each patient using 2-5 term models, 13 or 14 out of 15 individuals were predicted correctly. Conclusions: Our data strongly suggest that gut microbiota patterns allow individual prediction of long-term weight loss success. Prediction accuracy seems to be high but needs confirmation by larger prospective trials.

Cold Atmospheric Plasma Reduces Vessel Density and Increases Vascular Permeability and Apoptotic Cell Death in Solid Tumors.

Cold atmospheric plasma (CAP) has demonstrated promising anti-cancer effects in numerous in vitro and in vivo studies. Despite their relevance for the treatment of solid tumors, effects of CAP on tumor vasculature and microcirculation have only rarely been investigated. Here, we report the reduction of vessel density and an increase in vascular permeability and tumor cell apoptosis after CAP application. Solid tumors in the chorioallantoic membrane of chicken embryos were treated with CAP and evaluated with respect to effects of CAP on embryo survival, tumor size, and tumor morphology. Furthermore, intratumoral blood vessel density, apoptotic cell death and the tumor-associated microcirculation were investigated and compared to sham treatment. Treatment with CAP significantly reduced intratumoral vessel density while increasing the rate of intratumoral apoptosis in solid tumors. Furthermore, CAP treatment increased vascular permeability and attenuated the microcirculation by causing vessel occlusions in the tumor-associated vasculature. These effects point out the potential of CAP as a promising and yet underrated therapeutic modality for addressing the tumor vasculature in the treatment of solid tumors.

The role of wage beliefs in the decision to become a nurse.

In light of skilled-labor shortage, the effect of a change in the wage of nurses on their labor supply is intensely discussed in recent literature. Using extensive data of German 14- to 15-year-olds, I analyze the role of the beliefs about a nurse's wage in the decision to become one. To estimate a partial effect, I select controls and their functional form using post-double-selection, which is a data-driven selection method based on regression shrinkage. Highlighting the importance of wages at the extensive margin of labor supply, the wage beliefs play a positive and statistically significant role. Although information is publicly available, educational choices knowingly suffer from misinformation. I find that especially those who do not become a nurse understate the wage. The results lead to two important policy implications. First, increasing the wage may help to overcome the shortage observed in many countries. Second, providing more information on the (relative) wage may be a successful strategy to attract more individuals into this profession. To assess the sensitivity of the results regarding omitted variable bias, I apply a novel approach. It turns out that potential unobserved confounders would have to be strong to overrule the conclusions.

Predictive modeling of the early stages of semi-solid food ripening: Spatio-temporal dynamics in semi-solid casein matrices.

A mechanistic, spatio-temporal model to predict early stage semi-solid food ripening, exemplary for semi-solid casein matrices, was created using software based on the finite element method (FEM). The model was refined and validated by experimental data obtained during 8 wk of ripening of a casein matrix that was inoculated by one single central injection of starter culture. The resulting spatio-temporal distributions of lactococci strains, lactose, lactic acid/lactate and pH allowed us to optimize a number of parameters of the predictive model. Using the optimized model, the agreement between simulation and experiment was found to be satisfactory, with the pH matching best. The predictive model unveiled that effective diffusion of substrate and metabolites were crucial for an eventual homogeneous distribution of the measured substances. Hence, while using the optimized parameters from the single injection model, an injection technology for starter culture to inoculate and ferment casein matrices homogeneously was developed by means of solving another optimization problem with respect to injection positions. The casein matrix inoculated by the proposed injection pattern (21 injections, distance = 19 mm) showed sufficient homogeneity (bacterial activity and pH distribution) after the early stages of ripening, demonstrating the potential of application of the injection technology for fermentation of casein-based foods e.g. cheese.

Monitoring of tumor growth and vascularization with repetitive ultrasonography in the chicken chorioallantoic-membrane-assay.

The chorioallantoic-membrane (CAM)-assay is an established model for in vivo tumor research. Contrary to rodent-xenograft-models, the CAM-assay does not require breeding of immunodeficient strains due to native immunodeficiency. This allows xenografts to grow on the non-innervated CAM without pain or impairment for the embryo. Considering multidirectional tumor growth, limited monitoring capability of tumor size is the main methodological limitation of the CAM-assay for tumor research. Enclosure of the tumor by the radiopaque eggshell and the small structural size only allows monitoring from above and challenges established imaging techniques. We report the eligibility of ultrasonography for repetitive visualization of tumor growth and vascularization in the CAM-assay. After tumor ingrowth, ultrasonography was repetitively performed in ovo using a commercial ultrasonographic scanner. Finally, the tumor was excised and histologically analyzed. Tumor growth and angiogenesis were successfully monitored and findings in ultrasonographic imaging significantly correlated with results obtained in histological analysis. Ultrasonography is cost efficient and widely available. Tumor imaging in ovo enables the longitudinal monitoring of tumoral development, yet allowing high quantitative output due to the CAM-assays simple and cheap methodology. Thus, this methodological novelty improves reproducibility in the field of in vivo tumor experimentation emphasizing the CAM-assay as an alternative to rodent-xenograft-models.

Comparison of in vitro and computational experiments on the relation of inter-beat interval and duration of repolarization in a specific type of human induced pluripotent stem cell-derived cardiomyocytes.

We compared a published computational model of the action potential of a specific type of human induced pluripotent stem cell -derived cardiomyocytes (hiPSC-CM) with experimental field potential data with regard to their inter-beat interval and the duration of repolarization. In particular, concomitant changes in inter-beat interval and duration of repolarization were calculated after reduction and/or augmentation of specific ion channel conductances as a surrogate for pharmacological manipulation. The observed mismatches between calculations and experimental data indicate that there is information missing about the cellular test system. Based on our results we hypothesize that, among other currents, the actual If ("funny current") may deviate from the prediction. We show that replacement of the If formulation by alternative equations causes the model predictions to change qualitatively, however, none of the available formulations is actually achieving a satisfactory match with experimental data. We suggest a strategy to clarify whether the mismatch can be completely resolved at all using single cell models and, if yes, how this goal could be reached.

Early afterdepolarizations in cardiac action potentials as mixed mode oscillations due to a folded node singularity.

Early afterdepolarizations (EADs) are pathological voltage oscillations during the repolarization phase of cardiac action potentials. They are considered as potential precursors to cardiac arrhythmias and have recently gained much attention in the context of preclinical drug safety testing under the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm. From the viewpoint of multiple time scales theory, the onset of EADs has previously been studied by means of mathematical action potential models with one slow ion channel gating variable. In this article, we for the first time associate EADs with mixed mode oscillations in dynamical systems with two slow gating variables and present a folded node singularity of the slow flow as a novel mechanism for EADs genesis. We derive regions of the pharmacology parameter space in which EADs occur using both the folded node analysis and a full system bifurcation analysis, and we suggest the normal distance to the boundary of the EADs region as a mechanism-based risk metric to computationally estimate a drug's proarrhythmic liability.

Period doubling cascades of limit cycles in cardiac action potential models as precursors to chaotic early Afterdepolarizations.

BACKGROUND: Early afterdepolarizations (EADs) are pathological voltage oscillations during the repolarization phase of cardiac action potentials (APs). EADs are caused by drugs, oxidative stress or ion channel disease, and they are considered as potential precursors to cardiac arrhythmias in recent attempts to redefine the cardiac drug safety paradigm. The irregular behaviour of EADs observed in experiments has been previously attributed to chaotic EAD dynamics under periodic pacing, made possible by a homoclinic bifurcation in the fast subsystem of the deterministic AP system of differential equations. RESULTS: In this article we demonstrate that a homoclinic bifurcation in the fast subsystem of the action potential model is neither a necessary nor a sufficient condition for the genesis of chaotic EADs. We rather argue that a cascade of period doubling (PD) bifurcations of limit cycles in the full AP system paves the way to chaotic EAD dynamics across a variety of models including a) periodically paced and spontaneously active cardiomyocytes, b) periodically paced and non-active cardiomyocytes as well as c) unpaced and spontaneously active cardiomyocytes. Furthermore, our bifurcation analysis reveals that chaotic EAD dynamics may coexist in a stable manner with fully regular AP dynamics, where only the initial conditions decide which type of dynamics is displayed. CONCLUSIONS: EADs are a potential source of cardiac arrhythmias and hence are of relevance both from the viewpoint of drug cardiotoxicity testing and the treatment of cardiomyopathies. The model-independent association of chaotic EADs with period doubling cascades of limit cycles introduced in this article opens novel opportunities to study chaotic EADs by means of bifurcation control theory and inverse bifurcation analysis. Furthermore, our results may shed new light on the synchronization and propagation of chaotic EADs in homogeneous and heterogeneous multicellular and cardiac tissue preparations.

Early Afterdepolarizations with Growing Amplitudes via Delayed Subcritical Hopf Bifurcations and Unstable Manifolds of Saddle Foci in Cardiac Action Potential Dynamics.

Early afterdepolarizations (EADs) are pathological oscillations in cardiac action potentials during the repolarization phase and may be caused by drug side effects, ion channel disease or oxidative stress. The most widely observed EAD pattern is characterized by oscillations with growing amplitudes. So far, its occurence has been explained in terms of a supercritical Hopf bifurcation in the fast subsystem of the action potential dynamics from which stable limit cycles with growing amplitudes emerge. The novel contribution of this article is the introduction of two alternative explanations of EAD genesis with growing amplitudes that do not involve stable limit cycles in fast subsystems. In particular, we demonstrate that EAD patterns with growing amplitudes may alternatively arise due to a delayed subcritical Hopf bifurcation or an unstable manifold of a saddle focus fixed point in the full fast-slow system modelling the action potential. Our work extends the list of possible dynamical EAD mechanisms and may contribute to a classification of drug effects in preclinical cardiotoxicity testing.

Practical Guidelines for Incorporating Knowledge-Based and Data-Driven Strategies into the Inference of Gene Regulatory Networks.

Modeling gene regulatory networks (GRNs) is essential for conceptualizing how genes are expressed and how they influence each other. Typically, a reverse engineering approach is employed; this strategy is effective in reproducing possible fitting models of GRNs. To use this strategy, however, two daunting tasks must be undertaken: one task is to optimize the accuracy of inferred network behaviors; and the other task is to designate valid biological topologies for target networks. Although existing studies have addressed these two tasks for years, few of the studies can satisfy both of the requirements simultaneously. To address these difficulties, we propose an integrative modeling framework that combines knowledge-based and data-driven input sources to construct biological topologies with their corresponding network behaviors. To validate the proposed approach, a real dataset collected from the cell cycle of the yeast S. cerevisiae is used. The results show that the proposed framework can successfully infer solutions that meet the requirements of both the network behaviors and biological structures. Therefore, the outcomes are exploitable for future in vivo experimental design.

Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions.

Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.

Differentiation between genomic and non-genomic feedback controls yields an HPA axis model featuring hypercortisolism as an irreversible bistable switch.

BACKGROUND: The hypothalamic-pituitary-adrenal axis (HPA axis) is a major part of the neuroendocrine system responsible for the regulation of the response to physical or mental stress and for the control of the synthesis of the stress hormone cortisol. Dysfunctions of the HPA axis characterized by either low (hypocortisolism) or increased (hypercortisolism) cortisol levels are implicated in various pathological conditions. Their understanding and therapeutic correction may be supported by mathematical modeling and simulation of the HPA axis. METHODS: Mass action and Michaelis Menten enzyme kinetics were used to provide a mechanistic description of the feedback mechanisms within the pituitary gland cells by which cortisol inhibits its own production. A separation of the nucleus from the cytoplasm by compartments enabled a differentiation between slow genomic and fast non-genomic processes. The model in parts was trained against time resolved ACTH stress response data from an in vitro cell culture of murine AtT-20 pituitary tumor cells and analyzed by bifurcation discovery tools. RESULTS: A recently found pituitary gland cell membrane receptor that mediates rapid non-genomic actions of glucocorticoids has been incorporated into our model of the HPA axis. As a consequence of the distinction between genomic and non-genomic feedback processes our model possesses an extended dynamic repertoire in comparison to existing HPA models. In particular, our model exhibits limit cycle oscillations and bistable behavior associated to hypocortisolism but also features a (second) bistable switch which captures irreversible transitions in hypercortisolism to elevated cortisol levels. CONCLUSIONS: Model predictive control and inverse bifurcation analysis have been previously applied in the simulation-based design of therapeutic strategies for the correction of hypocortisolism. Given the HPA model extension presented in this paper, these techniques may also be used in the study of hypercortisolism. As an example, we show how sparsity enforcing penalization may suggest network interventions that allow the return from elevated cortisol levels back to nominal ones.

In vitro detection of adrenocorticotropic hormone levels by fluorescence correlation spectroscopy immunoassay for mathematical modeling of glucocorticoid-mediated feedback mechanisms.

Performing quantitative, highly sensitive measurements at a single molecule level is often necessary to address specific issues related to complex molecular and biochemical systems. For that purpose, we present a technique exploiting both the flexibility of immunoassays as well as the low operating costs and high throughput rates of the fluorescence correlation spectroscopy (FCS) method. That way we have established a quantitative measurement technique providing accurate and flexibly time resolved data of single molecules. Nanomolar changes in adrenocorticotropic hormone (ACTH) levels have been detected in a short time-frame that are caused by fast feedback actions in AtT-20 anterior pituitary glands in vitro. Especially with respect to clinical diagnostic or mathematical modeling this improved FCS setup may be of high relevance in order to accurately quantify the amounts of peptide hormones-such as ACTH-as well as signaling molecules, transcription factors, etc., being involved in intra- and extracellular reaction networks.

Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models.

The inference of reaction rate parameters in biochemical network models from time series concentration data is a central task in computational systems biology. Under the assumption of well mixed conditions the network dynamics are typically described by the chemical master equation, the Fokker Planck equation, the linear noise approximation or the macroscopic rate equation. The inverse problem of estimating the parameters of the underlying network model can be approached in deterministic and stochastic ways, and available methods often compare individual or mean concentration traces obtained from experiments with theoretical model predictions when maximizing likelihoods, minimizing regularized least squares functionals, approximating posterior distributions or sequentially processing the data. In this article we assume that the biological reaction network can be observed at least partially and repeatedly over time such that sample moments of species molecule numbers for various time points can be calculated from the data. Based on the chemical master equation we furthermore derive closed systems of parameter dependent nonlinear ordinary differential equations that predict the time evolution of the statistical moments. For inferring the reaction rate parameters we suggest to not only compare the sample mean with the theoretical mean prediction but also to take the residual of higher order moments explicitly into account. Cost functions that involve residuals of higher order moments may form landscapes in the parameter space that have more pronounced curvatures at the minimizer and hence may weaken or even overcome parameter sloppiness and uncertainty. As a consequence both deterministic and stochastic parameter inference algorithms may be improved with respect to accuracy and efficiency. We demonstrate the potential of moment fitting for parameter inference by means of illustrative stochastic biological models from the literature and address topics for future research.

Monitoring single-channel water permeability in polarized cells.

So far the determination of unitary permeability (p(f)) of water channels that are expressed in polarized cells is subject to large errors because the opening of a single water channel does not noticeably increase the water permeability of a membrane patch above the background. That is, in contrast to the patch clamp technique, where the single ion channel conductance may be derived from a single experiment, two experiments separated in time and/or space are required to obtain the single-channel water permeability p(f) as a function of the incremental water permeability (P(f,c)) and the number (n) of water channels that contributed to P(f,c). Although the unitary conductance of ion channels is measured in the native environment of the channel, p(f) is so far derived from reconstituted channels or channels expressed in oocytes. To determine the p(f) of channels from live epithelial monolayers, we exploit the fact that osmotic volume flow alters the concentration of aqueous reporter dyes adjacent to the epithelia. We measure these changes by fluorescence correlation spectroscopy, which allows the calculation of both P(f,c) and osmolyte dilution within the unstirred layer. Shifting the focus of the laser from the aqueous solution to the apical and basolateral membranes allowed the FCS-based determination of n. Here we validate the new technique by determining the p(f) of aquaporin 5 in Madin-Darby canine kidney cell monolayers. Because inhibition and subsequent activity rescue are monitored on the same sample, drug effects on exocytosis or endocytosis can be dissected from those on p(f).

No facilitator required for membrane transport of hydrogen sulfide.

Hydrogen sulfide (H(2)S) has emerged as a new and important member in the group of gaseous signaling molecules. However, the molecular transport mechanism has not yet been identified. Because of structural similarities with H(2)O, it was hypothesized that aquaporins may facilitate H(2)S transport across cell membranes. We tested this hypothesis by reconstituting the archeal aquaporin AfAQP from sulfide reducing bacteria Archaeoglobus fulgidus into planar membranes and by monitoring the resulting facilitation of osmotic water flow and H(2)S flux. To measure H(2)O and H(2)S fluxes, respectively, sodium ion dilution and buffer acidification by proton release (H(2)S left arrow over right arrow H(+) + HS(-)) were recorded in the immediate membrane vicinity. Both sodium ion concentration and pH were measured by scanning ion-selective microelectrodes. A lower limit of lipid bilayer permeability to H(2)S, P(M,H(2)S) >or = 0.5 +/- 0.4 cm/s was calculated by numerically solving the complete system of differential reaction diffusion equations and fitting the theoretical pH distribution to experimental pH profiles. Even though reconstitution of AfAQP significantly increased water permeability through planar lipid bilayers, P(M,H(2)S) remained unchanged. These results indicate that lipid membranes may well act as a barrier to water transport although they do not oppose a significant resistance to H(2)S diffusion. The fact that cholesterol and sphingomyelin reconstitution did not turn these membranes into an H(2)S barrier indicates that H(2)S transport through epithelial barriers, endothelial barriers, and membrane rafts also occurs by simple diffusion and does not require facilitation by membrane channels.

Different pathways for activation and deactivation in CaV1.2: a minimal gating model.

Point mutations in pore-lining S6 segments of CaV1.2 shift the voltage dependence of activation into the hyperpolarizing direction and significantly decelerate current activation and deactivation. Here, we analyze theses changes in channel gating in terms of a circular four-state model accounting for an activation R-A-O and a deactivation O-D-R pathway. Transitions between resting-closed (R) and activated-closed (A) states (rate constants x(V) and y(V)) and open (O) and deactivated-open (D) states (u(V) and w(V)) describe voltage-dependent sensor movements. Voltage-independent pore openings and closures during activation (A-O) and deactivation (D-R) are described by rate constants alpha and beta, and gamma and delta, respectively. Rate constants were determined for 16-channel constructs assuming that pore mutations in IIS6 do not affect the activating transition of the voltage-sensing machinery (x(V) and y(V)). Estimated model parameters of 15 CaV1.2 constructs well describe the activation and deactivation processes. Voltage dependence of the "pore-releasing" sensor movement ((x(V)) was much weaker than the voltage dependence of "pore-locking" sensor movement (y(V)). Our data suggest that changes in membrane voltage are more efficient in closing than in opening CaV1.2. The model failed to reproduce current kinetics of mutation A780P that was, however, accurately fitted with individually adjusted x(V) and y(V). We speculate that structural changes induced by a proline substitution in this position may disturb the voltage-sensing domain.

Parameter identification for chemical reaction systems using sparsity enforcing regularization: a case study for the chlorite-iodide reaction.

Complex chemical reactions are commonly described by systems of nonlinear ordinary differential equations. Rate and equilibrium constants of these models are usually not directly accessible and have to be indirectly inferred from experimental observations of the system. As a consequence, parameter identification problems have to be formulated and computationally solved. Because of a limited amount of information and uncertainties in the data, the solutions to such parameter identification problems typically lack uniqueness and stability properties and hence cannot be found in a reliable way by a pure minimization of the data mismatch (i.e., the discrepancy between experimental observations and simulated model output). To overcome these difficulties, so-called regularization methods have to be used. In this article, we suggest a sparsity promoting regularization approach that eliminates unidentifiable model parameters (i.e., parameters of low or no sensitivity to the given data). That way, the model is reduced to a core reaction mechanism with manageable interpretation while still remaining in accordance with the experimental observations. For the computational realization, we utilize the adjoint state technique for an efficient calculation of the gradient of the objective with respect to model parameters as well as uncertain initial and experimental conditions. Illustrations of our approach are given by means of the chlorite-iodide reaction for which reference parameter values are available.