Dynamics of Immunological Models

We analyse the effect of the regulatory T cells (Tregs) in the local control of the immune responses by T cells. We obtain an explicit formula for the level of antigenic stimulation of T cells as a function of the concentration of T cells and the parameters of the model. The relation between the concentration of the T cells and the antigenic stimulation of T cells is an hysteresis, that is unfold for some parameter values. We study the appearance of autoimmunity from cross-reactivity between a pathogen and a self antigen or from bystander proliferation. We also study an asymmetry in the death rates. With this asymmetry we show that the antigenic stimulation of the Tregs is able to control locally the population size of Tregs. Other effects of this asymmetry are a faster immune response and an improvement in the simulations of the bystander proliferation. The rate of variation of the levels of antigenic stimulation determines if the outcome is an immune response or if Tregs are able to maintain control due to the presence of a transcritical bifurcation for some tuning between the antigenic stimuli of T cells and Tregs. This behavior is explained by the presence of a transcritical bifurcation.     

Dynamics of Epidemiological Models

We study the SIS and SIRI epidemic models discussing different approaches to compute the thresholds that determine the appearance of an epidemic disease. The stochastic SIS model is a well known mathematical model, studied in several contexts. Here, we present recursively derivations of the dynamic equations for all the moments and we derive the stationary states of the state variables using the moment closure method. We observe that the steady states give a good approximation of the quasi-stationary states of the SIS model. We present the relation between the SIS stochastic model and the contact process introducing creation and annihilation operators. For the spatial stochastic epidemic reinfection model SIRI, where susceptibles S can become infected I, then recover and remain only partial immune against reinfection R, we present the phase transition lines using the mean field and the pair approximation for the moments. We use a scaling argument that allow us to determine analytically an explicit formula for the phase transition lines in pair approximation.     

Models for Bounded Systems with Continuous Dynamics

Summary .  Models for natural nonlinear processes, such as population dynamics, have been given much attention in applied mathematics. For example, species competition has been extensively modeled by differential equations. Often, the scientist has preferred to model the underlying dynamical processes (i.e., theoretical mechanisms) in continuous time. It is of both scientific and mathematical interest to implement such models in a statistical framework to quantify uncertainty associated with the models in the presence of observations. That is, given discrete observations arising from the underlying continuous process, the unobserved process can be formally described while accounting for multiple sources of uncertainty (e.g., measurement error, model choice, and inherent stochasticity of process parameters). In addition to continuity, natural processes are often bounded; specifically, they tend to have nonnegative support. Various techniques have been implemented to accommodate nonnegative processes, but such techniques are often limited or overly compromising. This article offers an alternative to common differential modeling practices by using a bias-corrected truncated normal distribution to model the observations and latent process, both having bounded support. Parameters of an underlying continuous process are characterized in a Bayesian hierarchical context, utilizing a fourth-order Runge–Kutta approximation.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Score Tests for Exploring Complex Models: Application to HIV Dynamics Models

In biostatistics, more and more complex models are being developed. This is particularly the case in system biology. Fitting complex models can be very time‐consuming, since many models often have to be explored. Among the possibilities are the introduction of explanatory variables and the determination of random effects. The particularity of this use of the score test is that the null hypothesis is not itself very simple; typically, some random effects may be present under the null hypothesis. Moreover, the information matrix cannot be computed, but only an approximation based on the score. This article examines this situation with the specific example of HIV dynamics models. We examine the score test statistics for testing the effect of explanatory variables and the variance of random effect in this complex situation. We study type I errors and the statistical powers of this score test statistics and we apply the score test approach to a real data set of HIV‐infected patients.     

森林动态模型概论

讨论了模拟森林林分动态变化的模型,并把模型分为森林生长模型和演替模型。森林生长模型包括：全林分模型、林分级模型和单木模型;演替模型包括马尔可夫类模型和林窗模型。文中给出了演替模型的基本原理和适用性,在比较早期和最新发展的林窗模型后,叙述了林窗模型的新进展。生长和演替模型的结构和数据要求不同决定了它们的在时间和空间上的适应性,最后指出模型将向综合总体方向发展。     

The Obstacle-Set Method for Representing Muscle Paths in Musculoskeletal Models

A computational method is introduced for modeling the paths of muscles in the human body. The method is based on the premise that the resultant muscle force acts along the locus of the transverse cross-sectional centroids of the muscle. The path of the muscle is calculated by idealizing its centroid path as a frictionless elastic band, which moves freely over neighboring anatomical constraints such as bones and other muscles. The anatomical constraints, referred to as obstacles, are represented in the model by regular-shaped, rigid bodies such as spheres and cylinders. The obstacles, together with the muscle path, define an obstacle set. It is proposed that the path of any muscle can be modeled using one or more of the following four obstacle sets: single sphere, single cylinder, double cylinder, and sphere-capped cylinder. Assuming that the locus of the muscle centroids is known for an arbitrary joint configuration, the obstacle-set method can be used to calculate the path of the muscle for all other joint configurations. The obstacle-set method accounts not only for the interaction between a muscle and a neighboring anatomical constraint, but also for the way in which this interaction changes with joint configuration. Consequently, it is the only feasible method for representing the paths of muscles which cross joints with multiple degrees of freedom such as the deltoid at the shoulder.     

Practical Identifiability of HIV Dynamics Models

We study the practical identifiability of parameters, i.e., the accuracy of the estimation that can be hoped, in a model of HIV dynamics based on a system of non-linear Ordinary Differential Equations (ODE). This depends on the available information such as the schedule of the measurements, the observed components, and the measurement precision. The number of patients is another way to increase it by introducing an appropriate statistical “population” framework. The impact of each improvement of the experimental condition is not known in advance but it can be evaluated via the Fisher Information Matrix (FIM). If the non-linearity of the biological model, as well as the complex statistical framework makes computation of the FIM challenging, we show that the particular structure of these models enables to compute it as precisely as wanted. In the HIV model, measuring HIV viral load and total CD4+ count were not enough to achieve identifiability of all the parameters involved. However, we show that an appropriate statistical approach together with the availability of additional markers such as infected cells or activated cells should considerably improve the identifiability and thus the usefulness of dynamical models of HIV.     

Comparative Study of Injury Models for Studying Muscle Regeneration in Mice

Background

A longstanding goal in regenerative medicine is to reconstitute functional tissus or organs after injury or disease. Attention has focused on the identification and relative contribution of tissue specific stem cells to the regeneration process. Relatively little is known about how the physiological process is regulated by other tissue constituents. Numerous injury models are used to investigate tissue regeneration, however, these models are often poorly understood. Specifically, for skeletal muscle regeneration several models are reported in the literature, yet the relative impact on muscle physiology and the distinct cells types have not been extensively characterised.

Methods

We have used transgenic Tg:Pax7nGFP and Flk1^GFP/+ mouse models to respectively count the number of muscle stem (satellite) cells (SC) and number/shape of vessels by confocal microscopy. We performed histological and immunostainings to assess the differences in the key regeneration steps. Infiltration of immune cells, chemokines and cytokines production was assessed in vivo by Luminex^®.

Results

We compared the 4 most commonly used injury models i.e. freeze injury (FI), barium chloride (BaCl₂), notexin (NTX) and cardiotoxin (CTX). The FI was the most damaging. In this model, up to 96% of the SCs are destroyed with their surrounding environment (basal lamina and vasculature) leaving a “dead zone” devoid of viable cells. The regeneration process itself is fulfilled in all 4 models with virtually no fibrosis 28 days post-injury, except in the FI model. Inflammatory cells return to basal levels in the CTX, BaCl₂ but still significantly high 1-month post-injury in the FI and NTX models. Interestingly the number of SC returned to normal only in the FI, 1-month post-injury, with SCs that are still cycling up to 3-months after the induction of the injury in the other models.

Conclusions

Our studies show that the nature of the injury model should be chosen carefully depending on the experimental design and desired outcome. Although in all models the muscle regenerates completely, the trajectories of the regenerative process vary considerably. Furthermore, we show that histological parameters are not wholly sufficient to declare that regeneration is complete as molecular alterations (e.g. cycling SCs, cytokines) could have a major persistent impact.     

Muscle Dynamics in Fish During Steady Swimming

SYNOPSIS. Recent research in fish locomotion has been dominatedby an interest in the dynamic mechanical properties of the swimmingmusculature. Prior observations have indicated that waves ofmuscle activation travel along the body of an undulating fishfaster than the resulting waves of muscular contraction, suggestingthat the phase relation between the muscle strain cycle andits activation must vary along the body. Since this phase relationis critical in determining how the muscle performs in cycliccontractions, the possibility has emerged that dynamic musclefunction may change with axial position in swimming fish. Quantificationof muscle contractile properties in cyclic contractions relieson in vitro experiments using strain and activation data collectedin vivo. In this paper we discuss the relation between theseparameters and body kinematics. Using videoradiographic datafrom swimming mackerel we demonstrate that red muscle straincan be accurately predicted from midline curvature but not fromlateral displacement. Electromyographic recordings show neuronalactivation patterns that are consistent with red muscle performingnet positive work at all axial positions. The relatively constantcross-section of red muscle along much of the body suggeststhat positive power for swimming is generated fairly uniformlyalong the length of the fish.     

Mathematical Models for Cell Migration with Real-Time Cell Cycle Dynamics

The fluorescent ubiquitination-based cell cycle indicator, also known as FUCCI, allows the visualization of the G1 and S/G2/M cell cycle phases of individual cells. FUCCI consists of two fluorescent probes, so that cells in the G1 phase fluoresce red and cells in the S/G2/M phase fluoresce green. FUCCI reveals real-time information about cell cycle dynamics of individual cells, and can be used to explore how the cell cycle relates to the location of individual cells, local cell density, and different cellular microenvironments. In particular, FUCCI is used in experimental studies examining cell migration, such as malignant invasion and wound healing. Here we present, to our knowledge, new mathematical models that can describe cell migration and cell cycle dynamics as indicated by FUCCI. The fundamental model describes the two cell cycle phases, G1 and S/G2/M, which FUCCI directly labels. The extended model includes a third phase, early S, which FUCCI indirectly labels. We present experimental data from scratch assays using FUCCI-transduced melanoma cells, and show that the predictions of spatial and temporal patterns of cell density in the experiments can be described by the fundamental model. We obtain numerical solutions of both the fundamental and extended models, which can take the form of traveling waves. These solutions are mathematically interesting because they are a combination of moving wavefronts and moving pulses. We derive and confirm a simple analytical expression for the minimum wave speed, as well as exploring how the wave speed depends on the spatial decay rate of the initial condition.     

A Class of Pairwise Models for Epidemic Dynamics on Weighted Networks

In this paper, we study the SIS (susceptible–infected–susceptible) and SIR (susceptible–infected–removed) epidemic models on undirected, weighted networks by deriving pairwise-type approximate models coupled with individual-based network simulation. Two different types of theoretical/synthetic weighted network models are considered. Both start from non-weighted networks with fixed topology followed by the allocation of link weights in either (i) random or (ii) fixed/deterministic way. The pairwise models are formulated for a general discrete distribution of weights, and these models are then used in conjunction with stochastic network simulations to evaluate the impact of different weight distributions on epidemic thresholds and dynamics in general. For the SIR model, the basic reproductive ratio R ₀ is computed, and we show that (i) for both network models R ₀ is maximised if all weights are equal, and (ii) when the two models are ‘equally-matched’, the networks with a random weight distribution give rise to a higher R ₀ value. The models with different weight distributions are also used to explore the agreement between the pairwise and simulation models for different parameter combinations.     

Simplifying gene trees for easier comprehension

Background  

In the genomic age, gene trees may contain large amounts of data making them hard to read and understand. Therefore, an automated simplification is important.     

由微分方程所描述的微生物连续培养动力系统(Ⅰ)

陆续介绍微生物连续培养(Chemostat)的基本原理,以单种微生物连续培养模型为基础,较详细地介绍几类由微分方程所描述的微生物连续培养动力系统模型,涉及的问题有解的稳定性,系统的持久性,周期解和Hopf分支等．     

Asymptotic Distribution of Density-Dependent Stage-Grouped Population Dynamics Models

Matrix models are widely used in biology to predict the temporal evolution of stage-structured populations. One issue related to matrix models that is often disregarded is the sampling variability. As the sample used to estimate the vital rates of the models are of finite size, a sampling error is attached to parameter estimation, which has in turn repercussions on all the predictions of the model. In this study, we address the question of building confidence bounds around the predictions of matrix models due to sampling variability. We focus on a density-dependent Usher model, the maximum likelihood estimator of parameters, and the predicted stationary stage vector. The asymptotic distribution of the stationary stage vector is specified, assuming that the parameters of the model remain in a set of the parameter space where the model admits one unique equilibrium point. Tests for density-dependence are also incidentally provided. The model is applied to a tropical rain forest in French Guiana.