Modeling Tuberculosis Dynamics,Detection and Control in Cattle Herds

Epidemiological models are key tools for designing and evaluating detection and control strategies against animal infectious diseases. In France, after decades of decrease of bovine tuberculosis (bTB) incidence, the disease keeps circulating. Increasing prevalence levels are observed in several areas, where the detection and control strategy could be adapted. The objective of this work was to design and calibrate a model of the within-herd transmission of bTB. The proposed model is a stochastic model operating in discrete-time. Three health states were distinguished: susceptible, latent and infected. Dairy and beef herd dynamics and bTB detection and control programs were explicitly represented. Approximate Bayesian computation was used to estimate three model parameters from field data: the transmission parameter when animals are inside (β_inside) and outside (β_outside) buildings, and the duration of the latent phase. An independent dataset was used for model validation. The estimated median was 0.43 [0.16–0.84] month⁻¹ for β_inside and 0.08 [0.01–0.32] month⁻¹ for β_outside. The median duration of the latent period was estimated 3.5 [2]–[8] months. The sensitivity analysis showed only minor influences of fixed parameter values on these posterior estimates. Validation based on an independent dataset showed that in more than 80% of herds, the observed proportion of animals with detected lesions was between the 2.5% and 97.5% percentiles of the simulated distribution. In the absence of control program and once bTB has become enzootic within a herd, the median effective reproductive ratio was estimated to be 2.2 in beef herds and 1.7 in dairy herds. These low estimates are consistent with field observations of a low prevalence level in French bTB-infected herds.     

Modeling the Genetic Control of HIV-1 Dynamics After Highly Active Antiretroviral Therapy

The progression of HIV disease has been markedly slowed by the use of highly active antiretroviral therapy (HAART). However, substantial genetic variation was observed to occur among different people in the decay rate of viral loads caused by HAART. The characterization of specific genes involved in HIV dynamics is central to design personalized drugs for the prevention of this disease, but usually cannot be addressed by experimental methods alone rather than require the help of mathematical and statistical methods. A novel statistical model has been recently developed to detect genetic variants that are responsible for the shape of HAART-induced viral decay curves. This model was employed to an HIV/AIDS trial, which led to the identification of a major genetic determinant that triggers an effect on HIV dynamics. This detected major genetic determinant also affects several clinically important parameters, such as half-lives of infected cells and HIV eradication times.Key Words: Hardy-weinberg equilibrium, bi-exponential function, quantitative trait loci, HIV dynamics, functional mapping.     

Modeling the Dynamics of Sensory Reweighting

Reweighting sensory information adaptively is considered critical for flexible postural control, but little is known of the time scale of the reweighting process. We analyzed the transient dynamics of sensory reweighting in a previously published nonlinear adaptive model of sensory integration in the human postural control system. The model’s dynamics of adaptation were tested in response to abrupt changes in the amplitude of the motion of the visual surround. In addition to qualitatively reproducing the correct asymptotic response to such changes in visual amplitude, as previously found, the model qualitatively reproduced the asymmetric transient response elucidated in recent experiments (Oie et al. in Gait Posture 2005). In particular, the model adapts at an initially rapid rate to a switch from low to high amplitude visual motion, but at an initially slower rate upon the return to low amplitude motion. The observed temporal asymmetry has potential functional value. Rapid downweighting of a visual stimulus that suddenly increases is necessary to prevent loss of upright equilibrium. A visual stimulus that decreases in amplitude does not pose a threat to upright balance, allowing for slower upweighting without functional consequence.     

Sampling or Intermittency in Hand Control System Dynamics

A hand control model is proposed. Investigation of the hand's intermittency synchronization shows it corresponds to an input-synchronized sampler rather than the clock-synchronized sampler more typical of engineering systems. A velocity control mechanism, similar to that in an eye tracking system is shown to be absent in the hand. A quantitative transfer function for predictable inputs serves further to define the hand's input adaptive characteristics. Stability margin adjustments of a linear reduced model enabled us to match the available quantitative data. The most exciting result of this study is the evidence for intermittency: a refractory period shown in the short pulse experiment, peaks in the frequency response experiments, and a saccadic sequence of steps in response to an open loop step input and to a closed loop ramp input.     

Modeling the Underlying Dynamics of the Spread of Crime

The spread of crime is a complex, dynamic process that calls for a systems level approach. Here, we build and analyze a series of dynamical systems models of the spread of crime, imprisonment and recidivism, using only abstract transition parameters. To find the general patterns among these parameters—patterns that are independent of the underlying particulars—we compute analytic expressions for the equilibria and for the tipping points between high-crime and low-crime equilibria in these models. We use these expressions to examine, in particular, the effects of longer prison terms and of increased incarceration rates on the prevalence of crime, with a follow-up analysis on the effects of a Three-Strike Policy.     

Modeling the Dynamics of Disease States in Depression

Major depressive disorder (MDD) is a common and costly disorder associated with considerable morbidity, disability, and risk for suicide. The disorder is clinically and etiologically heterogeneous. Despite intense research efforts, the response rates of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. Here we use computational modeling to advance our understanding of MDD. First, we propose a systematic and comprehensive definition of disease states, which is based on a type of mathematical model called a finite-state machine. Second, we propose a dynamical systems model for the progression, or dynamics, of MDD. The model is abstract and combines several major factors (mechanisms) that influence the dynamics of MDD. We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters. Our computational modeling suggests several predictions about MDD. Patients who suffer from depression can be divided into two sub-populations: a high-risk sub-population that has a high risk of developing chronic depression and a low-risk sub-population, in which patients develop depression stochastically with low probability. The success of antidepressant treatment is stochastic, leading to widely different times-to-remission in otherwise identical patients. While the specific details of our model might be subjected to criticism and revisions, our approach shows the potential power of computationally modeling depression and the need for different type of quantitative data for understanding depression.     

Imaging and Modeling the Dynamics of Clathrin-Mediated Endocytosis

Clathrin-mediated endocytosis (CME) plays a central role in cellular homeostasis and is mediated by clathrin-coated pits (CCPs). Live-cell imaging has revealed a remarkable heterogeneity in CCP assembly kinetics, which can be used as an intrinsic source of mechanistic information on CCP regulation but also poses several major problems for unbiased analysis of CME dynamics. The backbone of unveiling the molecular control of CME is an imaging-based inventory of the full diversity of individual CCP behaviors, which requires detection and tracking of structural fiduciaries and regulatory proteins with an accuracy of >99.9%, despite very low signals. This level of confidence can only be achieved by combining appropriate imaging modalities with self-diagnostic computational algorithms for image analysis and data mining.Clathrin-mediated endocytosis (CME) drives the uptake of diverse receptor-bound macromolecules and is one of the main endocytic mechanisms constitutively active in all mammalian cells. Clathrin-coated vesicles (CCVs) were the first transport vesicles to be isolated (Pearse 1975), which subsequently led to the identification of clathrin and the heterotetrameric adaptor protein AP2 as the major coat components (Pearse 1976, 1978). Further research in this area was spurred by the discovery that familial hypercholesterolemia is caused by a single substitution of a cysteine for a tyrosine in the cytoplasmic tail of the low-density lipoprotein receptor (LDLR), which disrupts its endocytic internalization motif and prevents its concentration in clathrin-coated pits (CCPs) (Anderson et al. 1977). In the following decades, biochemistry combined with molecular biology and electron microscopy (EM) have revealed much about the molecular players involved in CME (reviewed by Conner and Schmid 2003; Schmid and McMahon 2007; McMahon and Boucrot 2011; Boettner et al. 2012). Today, we know that CME is initiated via assembly of clathrin and AP2 to form CCPs and that receptor–ligand complexes (referred to as “cargo”) are concentrated in CCPs via direct interactions between endocytic motifs within their cytoplasmic domains and adaptor molecules that recruit clathrin. With the aid of a multitude of endocytic accessory proteins (EAPs)—many with as-yet poorly defined functions—CCPs undergo stabilization, maturation, and invagination. Finally, membrane fission, catalyzed by the GTPase dynamin, pinches off the CCV carrying its cargo into the cell.Although powerful and invaluable, bulk biochemical assays can only report cumulative and ensemble-averaged effects on CME, whereas EM only provides static snapshots of highly dynamic structures. Both approaches are not sufficient to resolve critical, rate-limiting stages of CCP maturation and alternative outcomes that prevent CCV internalization. They are also not sufficient to probe the frequently overlapping functions of individual components in CCP formation and maturation. Perturbation of molecular players in a system with such redundancy may lead to no detectable shifts, or to detectable shifts that merely represent system adaptation, and thus may not reveal the actual function of the targeted component itself. Moreover, perturbing CME may globally interfere with cell homeostasis, which can also elicit phenotypes unrelated to the actual functions of the target. To remedy these issues, it is necessary to follow the dynamics of CME at the level of individual CCPs and to correlate these behaviors with differential patterns of cargo and EAP recruitment and activity.These goals became approachable with the “GFP revolution” in the 1990s, which was paralleled by leaps in the sensitivity of digital light microscopy. For CME, the power of these technologies was first shown by Keen and colleagues, who used a green fluorescent protein (GFP) fusion of the clathrin light chain (CLC) to image clathrin dynamics by time-lapse wide-field epifluorescence microscopy (Gaidarov et al. 1999). Since then, numerous live-cell imaging studies have revealed remarkable heterogeneity in CCP assembly kinetics and internalization (Rappoport and Simon 2003; Ehrlich et al. 2004; Keyel et al. 2004; Merrifield et al. 2005; Loerke et al. 2009; Taylor et al. 2011). Although the physiological and molecular bases for this heterogeneity remain to be uncovered, the working hypothesis is that CCP heterogeneity arises from variations in molecular composition, in cortical membrane mechanics, and in differences between cell types. More recent advances in imaging and computational image data analyses have made it possible to determine the order in which EAPs are incorporated or released from growing CCPs. Thus, multidimensional live-cell imaging and mathematical models, in combination with very mild chemical, molecular, and mechanical perturbations, may uncover how the molecular composition of an assembling CCP affects its behavior. In the following we describe the developments of imaging modalities and image analysis methods that have led to the current state of the art in quantitative imaging of CME.     

Kinetic Modeling of Mitochondrial-Reticular Network Dynamics

Biophysics - This study involves the mitochondrial-reticular network, which is functionally integrated into the processes that provide energy to all systems of an organism as an open, dynamic,...     

Dynamics Modeling and Simulation of a Bionic Swim Bladder System in Underwater Robotics

While considering that fish could suspend themselves under water and could enhance their mobility by adjusting its swim bladder, we have carried out research on a bionic swim bladder system in underwater robotics, which could amend the underwater robotics' static balance and controllability conditions even if the depth of water changes. First, this paper introduces the bio-swim bladder's structure and function. Second, it works out the dynamic model of the bionic swim bladder, and then it analyses the dynamic characteristic and effect of the bionic swim bladder system with the software Matlab/simulink. Finally, considering about the nonlinear relationship of the parameters in the model, this paper brings forward a dual-speed control method, which could make the effect of the bionic swim bladder non-coupling. The result of the simulation reveals that the bionic swim bladder could change the buoyancy and centroid distribution of the underwater robotics effectively and independently, bringing it into a balance state, under which the control and maneuverability could be enhanced.     

Mesoscale Modeling of Amphiphilic Fluid Dynamics

Abstract

So-called “vector models”, in which surfactant molecules retain only translational and orientational degrees of freedom, have been used to study the equilibrium properties of amphiphilic fluids for nearly a decade now. We demonstrate that hydrodynamic lattice-gas automata provide an effective means of coupling the Hamiltonian of such vector models to hydrodynamic flow with conserved momentum, thereby providing a self-consistent treatment of the hydrodynamics of amphiphilic fluids. In this “talk”, we describe these hydrodynamic lattice-gas models in two and three dimensions, and present their application to problems of amphiphilic-fluid hydrodynamics, including the dynamics of phase separation and the shear-induced sponge-to-lamellar phase transition.     

End-Structure of Afferent Axons Injured in the Peripheral and Central Nervous System

The end-structure of afferent axons chronically severed in the rat sciatic nerve or dorsal column (DC) was visualized by centrifugal transport of horseradish peroxidase (HRP) or wheatgerm agglutinin conjugated to HRP (WGA:HRP) injected into the L₄ or L₅ dorsal root ganglion. Nerve regeneration was prevented and neuroma formation encouraged by tightly ligating the cut nerve end. For the first few weeks postoperative, the time during which afferents trapped in a nerve-end neuroma generate their most intense ectopic impulse barrage, the developing neuroma was dominated by swollen terminal end-bulbs. There was some axonal dying-back, retrograde fiber growth, and terminal sprouting, but little preterminal branching. The rich tangle of fine preterminal branches usually thought of in relation to nerve-end neuromas did not elaborate until several months postoperative, a time when the neuroma is relatively quiescent electrically. Afferents cut in the DC, which never develop dramatic ectopic electrical activity, showed morphological peculiarities similar to nerve-end neuromas during the early postoperative period, including retrograde fiber growth and minimal sprouting. They did not, however, go on to form luxuriant branches. These data provide preliminary clues as to the structure of the ectopic impulse-generating mechanism thought to underlie paresthesias and pain associated with peripheral nerve injury.     

Quantitative Modeling of Tumor Dynamics and Radiotherapy

Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.     

Modeling the Dynamics of a Non-Limited and a Self-Limited Gene Drive System in Structured Aedes aegypti Populations

Recently there have been significant advances in research on genetic strategies to control populations of disease-vectoring insects. Some of these strategies use the gene drive properties of selfish genetic elements to spread physically linked anti-pathogen genes into local vector populations. Because of the potential of these selfish elements to spread through populations, control approaches based on these strategies must be carefully evaluated to ensure a balance between the desirable spread of the refractoriness-conferring genetic cargo and the avoidance of potentially unwanted outcomes such as spread to non-target populations. There is also a need to develop better estimates of the economics of such releases. We present here an evaluation of two such strategies using a biologically realistic mathematical model that simulates the resident Aedes aegypti mosquito population of Iquitos, Peru. One strategy uses the selfish element Medea, a non-limited element that could permanently spread over a large geographic area; the other strategy relies on Killer-Rescue genetic constructs, and has been predicted to have limited spatial and temporal spread. We simulate various operational approaches for deploying these genetic strategies, and quantify the optimal number of released transgenic mosquitoes needed to achieve definitive spread of Medea-linked genes and/or high frequencies of Killer-Rescue-associated elements. We show that for both strategies the most efficient approach for achieving spread of anti-pathogen genes within three years is generally to release adults of both sexes in multiple releases over time. Even though females in these releases should not transmit disease, there could be public concern over such releases, making the less efficient male-only release more practical. This study provides guidelines for operational approaches to population replacement genetic strategies, as well as illustrates the use of detailed spatial models to assist in safe and efficient implementation of such novel genetic strategies.     

System Dynamics Modeling in the Evaluation of Delays of Care in ST-Segment Elevation Myocardial Infarction Patients within a Tiered Health System

Background

Mortality rates amongst ST segment elevation myocardial infarction (STEMI) patients remain high, especially in developing countries. The aim of this study was to evaluate the factors related with delays in the treatment of STEMI patients to support a strategic plan toward structural and personnel modifications in a primary hospital aligning its process with international guidelines.

Methods and Findings

The study was conducted in a primary hospital localized in Foz do Iguaçu, Brazil. We utilized a qualitative and quantitative integrated analysis including on-site observations, interviews, medical records analysis, Qualitative Comparative Analysis (QCA) and System Dynamics Modeling (SD). Main cause of delays were categorized into three themes: a) professional, b) equipment and c) transportation logistics. QCA analysis confirmed four main stages of delay to STEMI patient’s care in relation to the ‘Door-in-Door-out’ time at the primary hospital. These stages and their average delays in minutes were: a) First Medical Contact (From Door-In to the first contact with the nurse and/or physician): 7 minutes; b) Electrocardiogram acquisition and review by a physician: 28 minutes; c) ECG transmission and Percutaneous Coronary Intervention Center team feedback time: 76 minutes; and d) Patient’s Transfer Waiting Time: 78 minutes. SD baseline model confirmed the system’s behavior with all occurring delays and the need of improvements. Moreover, after model validation and sensitivity analysis, results suggested that an overall improvement of 40% to 50% in each of these identified stages would reduce the delay.

Conclusions

This evaluation suggests that investment in health personnel training, diminution of bureaucracy, and management of guidelines might lead to important improvements decreasing the delay of STEMI patients’ care. In addition, this work provides evidence that SD modeling may highlight areas where health system managers can implement and evaluate the necessary changes in order to improve the process of care.     

Modeling the Dynamics of Cdc42 Oscillation in Fission Yeast

Regulation of polarized cell growth is essential for many cellular processes, including spatial coordination of cell morphology changes during growth and division. We present a mathematical model of the core mechanism responsible for the regulation of polarized growth dynamics by the small GTPase Cdc42. The model is based on the competition of growth zones of Cdc42 localized at the cell tips for a common substrate (inactive Cdc42) that diffuses in the cytosol. We consider several potential ways of implementing negative feedback between Cd42 and its GEF in this model that would be consistent with the observed oscillations of Cdc42 in fission yeast. We analyze the bifurcations in this model as the cell length increases, and total amount of Cdc42 and GEF increase. Symmetric antiphase oscillations at two tips emerge via saddle-homoclinic bifurcations or Hopf bifurcations. We find that a stable oscillation and a stable steady state can coexist, which is consistent with the experimental finding that only 50% of bipolar cells oscillate. The mean amplitude and period can be tuned by parameters involved in the negative feedback. We link modifications in the parameters of the model to observed mutant phenotypes. Our model suggests that negative feedback is more likely to be acting through inhibition of GEF association rather than upregulation of GEF dissociation.     

On the Markovian Approach for Modeling the Dynamics of Nosocomial Infections

We analyze the dynamics of nosocomial infections in intensive care units (ICUs) by using a Markov chain model. Since population size in the ICU is small, in contrast to previous studies, we concentrate on the analytical solution rather than using simulation. We investigate how changes in the system parameters affect to some important behavioral indicators of the spread of the pathogen. We also present an exact measure of the number of secondary cases of infection produced by one colonized patient.