Virtual patients and sensitivity analysis of the guyton model of blood pressure regulation: towards individualized models of whole-body physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a "virtual population" from which "virtual individuals" can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the "virtual individuals" that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models.     

Meta-models as a straightforward approach to the sensitivity analysis of complex models

Complex simulation models are important tools in applied ecological and conservation research. However sensitivity analysis of this important class of models can be difficult to conduct. High level interactions and non-linear responses are common in complex simulations, and this necessitates a global sensitivity analysis, where each parameter is tested at a range of values, and in combination with changes in many other parameters. We reviewed the literature, searching for population viability analyses that used simulation models. We found only 9 out of the 122 simulation population viability analysis used global sensitivity analysis. This result is typical of other simulation models in applied ecology, where global sensitivity analysis is rare. We then demonstrate how to conduct a meta-modeling sensitivity analysis, where a simpler statistically fit function (the meta-model, also known as the surrogate model or emulator) is used to approximate the behavior of the complicated simulation. This simpler meta-model is interrogated to inform on the behavior of simulation model. We fit two example meta-models, a generalized linear model and a boosted regression tree, to exemplify the approach. Our hope is that by going through these techniques thoroughly they will become more widely adopted.     

Central pattern generators for social vocalization: androgen-dependent neurophysiological mechanisms

Historically, most studies of vertebrate central pattern generators (CPGs) have focused on mechanisms for locomotion and respiration. Here, we highlight new results for ectothermic vertebrates, namely teleost fish and amphibians, showing how androgenic steroids can influence the temporal patterning of CPGs for social vocalization. Investigations of vocalizing teleosts show how androgens can rapidly (within minutes) modulate the neurophysiological output of the vocal CPG (fictive vocalizations that mimic the temporal properties of natural vocalizations) inclusive of their divergent actions between species, as well as intraspecific differences between male reproductive morphs. Studies of anuran amphibians (frogs) demonstrate that long-term steroid treatments (wks) can masculinize the fictive vocalizations of females, inclusive of its sensitivity to rapid modulation by serotonin. Given the conserved organization of vocal control systems across vertebrate groups, the vocal CPGs of fish and amphibians provide tractable models for identifying androgen-dependent events that are fundamental to the mechanisms of vocal motor patterning. These basic mechanisms can also inform our understanding of the more complex CPGs for vocalization, and social behaviors in general, that have evolved among birds and mammals.     

Synthetic,Multi-Layer,Self-Oscillating Vocal Fold Model Fabrication

Sound for the human voice is produced via flow-induced vocal fold vibration. The vocal folds consist of several layers of tissue, each with differing material properties ¹. Normal voice production relies on healthy tissue and vocal folds, and occurs as a result of complex coupling between aerodynamic, structural dynamic, and acoustic physical phenomena. Voice disorders affect up to 7.5 million annually in the United States alone ² and often result in significant financial, social, and other quality-of-life difficulties. Understanding the physics of voice production has the potential to significantly benefit voice care, including clinical prevention, diagnosis, and treatment of voice disorders.Existing methods for studying voice production include in vivo experimentation using human and animal subjects, in vitro experimentation using excised larynges and synthetic models, and computational modeling. Owing to hazardous and difficult instrument access, in vivo experiments are severely limited in scope. Excised larynx experiments have the benefit of anatomical and some physiological realism, but parametric studies involving geometric and material property variables are limited. Further, they are typically only able to be vibrated for relatively short periods of time (typically on the order of minutes).Overcoming some of the limitations of excised larynx experiments, synthetic vocal fold models are emerging as a complementary tool for studying voice production. Synthetic models can be fabricated with systematic changes to geometry and material properties, allowing for the study of healthy and unhealthy human phonatory aerodynamics, structural dynamics, and acoustics. For example, they have been used to study left-right vocal fold asymmetry ^3,4, clinical instrument development ⁵, laryngeal aerodynamics ^6-9, vocal fold contact pressure ¹⁰, and subglottal acoustics ¹¹ (a more comprehensive list can be found in Kniesburges et al. ¹²)Existing synthetic vocal fold models, however, have either been homogenous (one-layer models) or have been fabricated using two materials of differing stiffness (two-layer models). This approach does not allow for representation of the actual multi-layer structure of the human vocal folds ¹ that plays a central role in governing vocal fold flow-induced vibratory response. Consequently, one- and two-layer synthetic vocal fold models have exhibited disadvantages ^3,6,8 such as higher onset pressures than what are typical for human phonation (onset pressure is the minimum lung pressure required to initiate vibration), unnaturally large inferior-superior motion, and lack of a "mucosal wave" (a vertically-traveling wave that is characteristic of healthy human vocal fold vibration).In this paper, fabrication of a model with multiple layers of differing material properties is described. The model layers simulate the multi-layer structure of the human vocal folds, including epithelium, superficial lamina propria (SLP), intermediate and deep lamina propria (i.e., ligament; a fiber is included for anterior-posterior stiffness), and muscle (i.e., body) layers ¹. Results are included that show that the model exhibits improved vibratory characteristics over prior one- and two-layer synthetic models, including onset pressure closer to human onset pressure, reduced inferior-superior motion, and evidence of a mucosal wave.     

Verification, validation and sensitivity studies in computational biomechanics

Computational techniques and software for the analysis of problems in mechanics have naturally moved from their origins in the traditional engineering disciplines to the study of cell, tissue and organ biomechanics. Increasingly complex models have been developed to describe and predict the mechanical behavior of such biological systems. While the availability of advanced computational tools has led to exciting research advances in the field, the utility of these models is often the subject of criticism due to inadequate model verification and validation (V&V). The objective of this review is to present the concepts of verification, validation and sensitivity studies with regard to the construction, analysis and interpretation of models in computational biomechanics. Specific examples from the field are discussed. It is hoped that this review will serve as a guide to the use of V&V principles in the field of computational biomechanics, thereby improving the peer acceptance of studies that use computational modeling techniques.     

Virtual profiling: a new way to analyse phenotypes

Simulation models can be used to perform virtual profiling in order to analyse eco‐physiological processes controlling plant phenotype. To illustrate this, an eco‐physiological model has been used to compare and contrast the status of a virtual fruit system under two situations of carbon supply. The model simulates fruit growth, accumulation of sugar, citric acid and water, transpiration, respiration and ethylene emission, and was successfully tested on peach (Prunus persica L. Batsch) for two leaf‐to‐fruit ratios (6 and 18 leaves per fruit). The development stage and the variation in leaf number had large effects of the fruit model variables dealing with growth, metabolism and fruit quality. A sensitivity analysis showed that changing a single parameter value, which could correspond to a genotypic change induced by a mutation, either strongly affects most of the processes, or affects a specific process or none. Correlation analysis showed that, in a complex system such as fruit, the intensity of many physiological processes and quality traits co‐varies. It also showed unexpected co‐variations resulting from emergent properties of the system. This virtual profiling approach opens a new route to explore the impact of mutations, or naturally occurring genetic variations, under differing environmental conditions.     

Response regulator output in bacterial chemotaxis.

Chemotaxis responses in Escherichia coli are mediated by the phosphorylated response-regulator protein P-CheY. Biochemical and genetic studies have established the mechanisms by which the various components of the chemotaxis system, the membrane receptors and Che proteins function to modulate levels of CheY phosphorylation. Detailed models have been formulated to explain chemotaxis sensing in quantitative terms; however, the models cannot be adequately tested without knowledge of the quantitative relationship between P-CheY and bacterial swimming behavior. A computerized image analysis system was developed to collect extensive statistics on freeswimming and individual tethered cells. P-CheY levels were systematically varied by controlled expression of CheY in an E.coli strain lacking the CheY phosphatase, CheZ, and the receptor demethylating enzyme CheB. Tumbling frequency was found to vary with P-CheY concentration in a weakly sigmoidal fashion (apparent Hill coefficient approximately 2.5). This indicates that the high sensitivity of the chemotaxis system is not derived from highly cooperative interactions between P-CheY and the flagellar motor, but rather depends on nonlinear effects within the chemotaxis signal transduction network. The complex relationship between single flagella rotation and free-swimming behavior was examined; our results indicate that there is an additional level of information processing associated with interactions between the individual flagella. An allosteric model of the motor switching process is proposed which gives a good fit to the observed switching induced by P-CheY. Thus the level of intracellular P-CheY can be estimated from behavior determinations: approximately 30% of the intracellular pool of CheY appears to be phosphorylated in fully adapted wild-type cells.     

Songbird: a unique animal model for studying the molecular basis of disorders of vocal development and communication

Like humans, songbirds are one of the few animal groups that learn vocalization. Vocal learning requires coordination of auditory input and vocal output using auditory feedback to guide one’s own vocalizations during a specific developmental stage known as the critical period. Songbirds are good animal models for understand the neural basis of vocal learning, a complex form of imitation, because they have many parallels to humans with regard to the features of vocal behavior and neural circuits dedicated to vocal learning. In this review, we will summarize the behavioral, neural, and genetic traits of birdsong. We will also discuss how studies of birdsong can help us understand how the development of neural circuits for vocal learning and production is driven by sensory input (auditory information) and motor output (vocalization).     

Social and vocal behavior in adult greater tube-nosed bats (Murina leucogaster)

Many studies have revealed the significant influence of the social nature and ecological niche of a species on the design and complexity of their communication sounds. The knowledge of communication sounds and particularly of the flexibility in their use among mammals, however, remains patchy. Being highly vocal and social, bats are well suited for investigating vocal plasticity as well as vocal diversity. Thus, the overall aim of this study was to test the presence of structural overlap between calls used in social communication and echolocation pulses emitted during foraging in greater tube-nosed bats (Murina leucogaster). Acoustic analysis and spectrotemporal decomposition of calls revealed a rich communication repertoire comprising 12 simple syllables and 5 composites with harmonics in the ultrasonic range. Simultaneous recording of vocal and social behavior in the same species yielded a strong correspondence between distinct behaviors and specific call types in support of Morton's motivation-structure hypothesis. Spectrographic analysis of call types also revealed the presence of modified components of echolocation pulses embedded within social calls. Altogether, the data suggest that bats can parse complex sounds into structurally simpler components that are recombined within behaviorally meaningful and multifunctional contexts.     

Modelling the production of complex calls in the túngara frog (Physalaemus pustulosus)

Relatively few studies have used experimental manipulations to investigate the mechanics of vocal production in frogs and toads, even though many frogs produce complex signals with multiple components and/or nonlinearities. Modelling approaches can add to empirical studies by illuminating how various components of the vocal system interact to produce communication signals. In this study, we use bond graphs, a lumped-element modelling technique, to explore how the combination of active modulation of vocal production and the passive dynamics of a unique laryngeal structure result in the complex calls produced by males in an anuran model system. The túngara frog (Physalaemus (= Engystomops) pustulosus) produces advertisement calls with a ‘whine’ and a facultative ‘chuck’. Whines are amplitude and frequency modulated. The chuck is characterized by its spectral complexity and appears to contain a period doubling bifurcation resulting in subharmonics. In our model, we focus on how a fibrous mass attached to the vocal cords results in subharmonics in the chuck. Our models suggest that active (neural) modulation of the fibrous mass is not necessary for the transition between the spectral characteristics of the whine and chuck. Rather, it is possible that the vibratory mode of the fibrous mass and thus the vocal cords changes passively as a result of changes in airflow through the system.     

A Lightweight,Headphones-based System for Manipulating Auditory Feedback in Songbirds

Experimental manipulations of sensory feedback during complex behavior have provided valuable insights into the computations underlying motor control and sensorimotor plasticity¹. Consistent sensory perturbations result in compensatory changes in motor output, reflecting changes in feedforward motor control that reduce the experienced feedback error. By quantifying how different sensory feedback errors affect human behavior, prior studies have explored how visual signals are used to recalibrate arm movements^2,3 and auditory feedback is used to modify speech production^4-7. The strength of this approach rests on the ability to mimic naturalistic errors in behavior, allowing the experimenter to observe how experienced errors in production are used to recalibrate motor output.Songbirds provide an excellent animal model for investigating the neural basis of sensorimotor control and plasticity^8,9. The songbird brain provides a well-defined circuit in which the areas necessary for song learning are spatially separated from those required for song production, and neural recording and lesion studies have made significant advances in understanding how different brain areas contribute to vocal behavior^9-12. However, the lack of a naturalistic error-correction paradigm - in which a known acoustic parameter is perturbed by the experimenter and then corrected by the songbird - has made it difficult to understand the computations underlying vocal learning or how different elements of the neural circuit contribute to the correction of vocal errors¹³.The technique described here gives the experimenter precise control over auditory feedback errors in singing birds, allowing the introduction of arbitrary sensory errors that can be used to drive vocal learning. Online sound-processing equipment is used to introduce a known perturbation to the acoustics of song, and a miniaturized headphones apparatus is used to replace a songbird''s natural auditory feedback with the perturbed signal in real time. We have used this paradigm to perturb the fundamental frequency (pitch) of auditory feedback in adult songbirds, providing the first demonstration that adult birds maintain vocal performance using error correction¹⁴. The present protocol can be used to implement a wide range of sensory feedback perturbations (including but not limited to pitch shifts) to investigate the computational and neurophysiological basis of vocal learning.     

Stochastic matrix models for conservation and management: a comparative review of methods

Stochastic matrix models are frequently used by conservation biologists to measure the viability of species and to explore various management actions. Models are typically parameterized using two or more sets of estimated transition rates between age/size/stage classes. While standard methods exist for analyzing a single set of transition rates, a variety of methods have been employed to analyze multiple sets of transition rates. We review applications of stochastic matrix models to problems in conservation and use simulation studies to compare the performance of different analytic methods currently in use. We find that model conclusions are likely to be robust to the choice of parametric distribution used to model vital rate fluctuations over time. However, conclusions can be highly sensitive to the within-year correlation structure among vital rates, and therefore we suggest using analytical methods that provide a means of conducting a sensitivity analysis with respect to correlation parameters. Our simulation results also suggest that the precision of population viability estimates can be improved by using matrix models that incorporate environmental covariates in conjunction with experiments to estimate transition rates under a range of environmental conditions.     

Effect of joint laxity on polyethylene wear in total knee replacement

Experimental simulator studies are frequently performed to evaluate wear behavior in total knee replacement. It is vital that the simulation conditions match the physiological situation as closely as possible. To date, few experimental wear studies have examined the effects of joint laxity on wear and joint kinematics and the absence of the anterior cruciate ligament has not been sufficiently taken into account in simulator wear studies.The aim of this study was to investigate different ligament and soft tissue models with respect to wear and kinematics.A virtual soft tissue control system was used to simulate different motion restraints in a force-controlled knee wear simulator.The application of more realistic and sophisticated ligament models that considered the absence of anterior cruciate ligament lead to a significant increase in polyethylene wear (p=0.02) and joint kinematics (p<0.01). We recommend the use of more complex ligament models to appropriately simulate the function of the human knee joint and to evaluate the wear behavior of total knee replacements. A feasible simulation model is presented.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

The use of Artificial Neural Networks to classify primate vocalizations: a pilot study on black lemurs

The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5–95.6%) and perform better than either statistical analysis (76.1–88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed. Am. J. Primatol. 72:337–348, 2010. © 2009 Wiley‐Liss, Inc.     

A review of acoustic playback techniques for studying avian vocal duets

ABSTRACT Playback experiments involve the broadcast of natural or synthetic sound stimuli and provide a powerful tool for studying acoustic communication in birds. Playback is a valuable technique for exploring vocal duetting behavior because it allows investigators to test predictions of the various hypotheses for duet function. Here, we adopt a methodological perspective by considering various challenges specific to studying duetting behavior, and highlighting the utility of different playback designs for testing duet function. Single‐speaker playback experiments allow investigators to determine how duetting birds react to different stimuli, but do not simulate duets in a spatially realistic manner. Multi‐speaker playback experiments are superior to single‐speaker designs because duet stimuli are broadcast with spatial realism and unique and additional predictions can be generated for testing duet function. In particular, multi‐speaker playback allows investigators to evaluate how birds respond to male versus female duet contributions separately, based on reactions to the different loudspeakers. Interactive playback allows investigators to ask questions about the time‐ and pattern‐specific singing behavior of birds, and to understand how singing strategies correspond to physical behavior during vocal interactions. Although logistically challenging, interactive playback provides a powerful tool for examining specific elements of duets (such as the degree of coordination) and may permit greater insight into their functions from an operational perspective. Interactive playback designs where the investigator simulates half of a duet may be used to describe and investigate the function of pair‐specific and population‐wide duet codes. Regardless of experimental design, all playback experiments should be based on a sound understanding of the natural duetting behavior of the species of interest, and should aim to produce realistic and carefully controlled duet simulations. Future studies that couple playback techniques with other experimental procedures, such as Acoustic Location System recordings for monitoring the position of birds in dense vegetation or multimodal techniques that combine acoustic with visual stimuli, are expected to provide an even better understanding of these highly complex vocal displays.     

Determining Pain Detection and Tolerance Thresholds Using an Integrated,Multi-Modal Pain Task Battery

Human pain models are useful in the assessing the analgesic effect of drugs, providing information about a drug''s pharmacology and identify potentially suitable therapeutic populations. The need to use a comprehensive battery of pain models is highlighted by studies whereby only a single pain model, thought to relate to the clinical situation, demonstrates lack of efficacy. No single experimental model can mimic the complex nature of clinical pain. The integrated, multi-modal pain task battery presented here encompasses the electrical stimulation task, pressure stimulation task, cold pressor task, the UVB inflammatory model which includes a thermal task and a paradigm for inhibitory conditioned pain modulation. These human pain models have been tested for predicative validity and reliability both in their own right and in combination, and can be used repeatedly, quickly, in short succession, with minimum burden for the subject and with a modest quantity of equipment. This allows a drug to be fully characterized and profiled for analgesic effect which is especially useful for drugs with a novel or untested mechanism of action.