A network model of successive partitioning-limited solute diffusion through the stratum corneum

As the most exposed point of contact with the external environment, the skin is an important barrier to many chemical exposures, including medications, potentially toxic chemicals and cosmetics. Traditional dermal absorption models treat the stratum corneum lipids as a homogenous medium through which solutes diffuse according to Fick's first law of diffusion. This approach does not explain non-linear absorption and irregular distribution patterns within the stratum corneum lipids as observed in experimental data. A network model, based on successive partitioning-limited solute diffusion through the stratum corneum, where the lipid structure is represented by a large, sparse, and regular network where nodes have variable characteristics, offers an alternative, efficient, and flexible approach to dermal absorption modeling that simulates non-linear absorption data patterns. Four model versions are presented: two linear models, which have unlimited node capacities, and two non-linear models, which have limited node capacities. The non-linear model outputs produce absorption to dose relationships that can be best characterized quantitatively by using power equations, similar to the equations used to describe non-linear experimental data.     

An investigation into the influence of boundary condition specification in finite difference methods on the behaviour of passive and active neuronal models

Neuronal models provide a major aid to understanding the behaviour of individual neurons and networks of neurons. The solution of the model equations by finite difference methods is widespread because of the inherent simplicity of the technique. Error in the finite difference approach due to spatial and temporal discretisation is shown to be equivalent to a mis-specification of membrane current density. The effect of this mis-specification on the accuracy of the solution to the model equations is shown to depend on the structure of the model and its input, as well as the size of the discretisation intervals themselves. Through a theoretical analysis, illustrated by a number of examples on passive and active dendrites, this article demonstrates that the accuracy with which core current is implemented numerically at segment end-points in elementary models influences the behaviour of the numerical solution of these models, and consequently any physiological conclusions drawn from them.     

Maximum likelihood estimation in dynamical models of HIV

The study of dynamical models of HIV infection, based on a system of nonlinear ordinary differential equations (ODE), has considerably improved the knowledge of its pathogenesis. While the first models used simplified ODE systems and analyzed each patient separately, recent works dealt with inference in non-simplified models borrowing strength from the whole sample. The complexity of these models leads to great difficulties for inference and only the Bayesian approach has been attempted by now. We propose a full likelihood inference, adapting a Newton-like algorithm for these particular models. We consider a relatively complex ODE model for HIV infection and a model for the observations including the issue of detection limits. We apply this approach to the analysis of a clinical trial of antiretroviral therapy (ALBI ANRS 070) and we show that the whole algorithm works well in a simulation study.     

Joint regression analysis of correlated data using Gaussian copulas

Summary .  This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration.     

Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease

In genetic association testing, failure to properly control for population structure can lead to severely inflated type 1 error and power loss. Meanwhile, adjustment for relevant covariates is often desirable and sometimes necessary to protect against spurious association and to improve power. Many recent methods to account for population structure and covariates are based on linear mixed models (LMMs), which are primarily designed for quantitative traits. For binary traits, however, LMM is a misspecified model and can lead to deteriorated performance. We propose CARAT, a binary-trait association testing approach based on a mixed-effects quasi-likelihood framework, which exploits the dichotomous nature of the trait and achieves computational efficiency through estimating equations. We show in simulation studies that CARAT consistently outperforms existing methods and maintains high power in a wide range of population structure settings and trait models. Furthermore, CARAT is based on a retrospective approach, which is robust to misspecification of the phenotype model. We apply our approach to a genome-wide analysis of Crohn disease, in which we replicate association with 17 previously identified regions. Moreover, our analysis on 5p13.1, an extensively reported region of association, shows evidence for the presence of multiple independent association signals in the region. This example shows how CARAT can leverage known disease risk factors to shed light on the genetic architecture of complex traits.     

One-step analysis of seed storage data and the longevity of Arabidopsis thaliana seeds

Seeds of two ecotypes of Arabidopsis thaliana, NW20 and N1601, were aged over a range of saturated salt solutions at temperatures between 6 degrees C and 55 degrees C. For each ecotype, the results from 37 storage experiments were summarized using the Ellis and Roberts viability equations and a modified version of these equations which allows for a proportion of 'non-respondents'. For both models, two approaches were taken in order to model the effect of moisture content (MC) and temperature on seed longevity. The first, a two-step approach, involved fitting individual survival curves and then multiple regression analysis of the fitted parameters on moisture content and temperature. For the second approach, the full viability models were fitted in one step, including the multiple regression for the effects of MC and temperature within the generalized linear model used to describe each survival curve. This one-step approach takes into account the full variability of the data and provides the best predictions of seed longevity based on the original assumptions of the Ellis and Roberts viability equations. As a consequence of taking into account all the variation, this one-step approach is more sensitive and thus more likely to detect changes due to reducing the number of parameters in the model as being significant. Whilst both approaches indicated that seeds from the two Arabidopsis ecotypes have the same response to MC and temperature, parameter values did differ between the approaches, with the one-step approach providing the better fit. The best model for these two ecotypes, from the one-step approach, confirmed a quadratic relationship between temperature and longevity, but the magnitude of the non-linearity is not as large as indicated by the universal value for the quadratic term.     

Predictive modeling of mixed microbial populations in food products: evaluation of two-species models

Predictive microbiology is an emerging research domain in which biological and mathematical knowledge is combined to develop models for the prediction of microbial proliferation in foods. To provide accurate predictions, models must incorporate essential factors controlling microbial growth. Current models often take into account environmental conditions such as temperature, pH and water activity. One factor which has not been included in many models is the influence of a background microflora, which brings along microbial interactions. The present research explores the potential of autonomous continuous-time/two-species models to describe mixed population growth in foods. A set of four basic requirements, which a model should satisfy to be of use for this particular application, is specified. Further, a number of models originating from research fields outside predictive microbiology, but all dealing with interacting species, are evaluated with respect to the formulated model requirements by means of both graphical and analytical techniques. The analysis reveals that of the investigated models, the classical Lotka-Volterra model for two species in competition and several extensions of this model fulfill three of the four requirements. However, none of the models is in agreement with all requirements. Moreover, from the analytical approach, it is clear that the development of a model satisfying all requirements, within a framework of two autonomous differential equations, is not straightforward. Therefore, a novel prototype model structure, extending the Lotka-Volterra model with two differential equations describing two additional state variables, is proposed to describe mixed microbial populations in foods.     

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.     

A critical review of mathematical models and data used in diabetology

The literature dealing with mathematical modelling for diabetes is abundant. During the last decades, a variety of models have been devoted to different aspects of diabetes, including glucose and insulin dynamics, management and complications prevention, cost and cost-effectiveness of strategies and epidemiology of diabetes in general. Several reviews are published regularly on mathematical models used for specific aspects of diabetes. In the present paper we propose a global overview of mathematical models dealing with many aspects of diabetes and using various tools. The review includes, side by side, models which are simple and/or comprehensive; deterministic and/or stochastic; continuous and/or discrete; using ordinary differential equations, partial differential equations, optimal control theory, integral equations, matrix analysis and computer algorithms.     

Philosophical Aspects of Measurements, Equations and Inferences in Plant Growth Studies

Growth analysis is based on equations that are ‘identities’because they are algebraically self-evident, whereas the moredeterministic models of plant growth are based on ‘conditionalequations’ that represent quantitative hypotheses. Growthanalytical studies consequently focus on the values of the componentsand not on the validity of the equations, whereas ‘validation’is a prime concern for other growth models. Implications ofmeasurement theory, of dependent and independent variables andof compensating components arise in the use of both types ofequation for quantifying growth. There is now available a rangeof approaches, from traditional growth analysis, through variousdevelopments of growth analysis including light conversion analysis,to complex mechanistic models of growth. Growth analysis, yield component analysis, light conversion analysis, mathematical models, measurement theory, derived quantities, independent variables, equations of growth     

The mathematical modelling of population dynamics taking into account the adaptive behavior of individuals

The general approach for modelling of abundance dynamic of biological populations and communities is offered. The mechanisms of individual adaptation in changing environment are considered. The approach is detailed for population models without structure and with age structure. The property of solutions are investigated. As examples the author studies the concrete definitions of general models by analogy with models of Ricker and May. Theoretical analysis and calculations shows that survival of model population in extreme situation increases if adaptive behaviour is taking into account.     

Application of theoretical models in ecology.

Certain general prey-predator models are compared with a particular ecosystem simulation. The adequacy of the models is considered in relation to the structure of the equations; to the representation of ecological features by terms in these equations; and to the need to incorporate resource equations in such models.     

A multiscale model for red blood cell mechanics

The objective of this article is the derivation of a continuum model for mechanics of red blood cells via multiscale analysis. On the microscopic level, we consider realistic discrete models in terms of energy functionals defined on networks/lattices. Using concepts of Γ-convergence, convergence results as well as explicit homogenisation formulae are derived. Based on a characterisation via energy functionals, appropriate macroscopic stress–strain relationships (constitutive equations) can be determined. Further, mechanical moduli of the derived macroscopic continuum model are directly related to microscopic moduli. As a test case we consider optical tweezers experiments, one of the most common experiments to study mechanical properties of cells. Our simulations of the derived continuum model are based on finite element methods and account explicitly for membrane mechanics and its coupling with bulk mechanics. Since the discretisation of the continuum model can be chosen freely, rather than it is given by the topology of the microscopic cytoskeletal network, the approach allows a significant reduction of computational efforts. Our approach is highly flexible and can be generalised to many other cell models, also including biochemical control.     

Dynamic domain threading

A modeling method is described that avoids the need to consider the domain structure of the template used for modeling, and automatically extracts compact fragments of structure that would be of a suitable size to build the model. This aids automation as the size or nature of the template structure can be ignored and does not have to be broken into domain (or multi-domain) units beforehand. The approach leads to the generation of a large number of models each based on slightly differing domain definitions and this variation was further increased by considering alternative secondary structure predictions. Each model, of which there may be thousands, takes the form of a complete alpha-carbon trace and some methods (including residue burial) were investigated for their power to discriminate good models from bad models using decoys. The method is also compared to an earlier retroviral capsid modeling problem for which the X-ray structure is now known. Some potential extensions of the approach to more distant modeling problems are discussed.     

Global stability in discrete population models with delayed-density dependence

We address the global stability issue for some discrete population models with delayed-density dependence. Applying a new approach based on the concept of the generalized Yorke conditions, we establish several criteria for the convergence of all solutions to the unique positive steady state. Our results support the conjecture stated by Levin and May in 1976 affirming that the local asymptotic stability of the equilibrium of some delay difference equations (including Ricker's and Pielou's equations) implies its global stability. We also discuss the robustness of the obtained results with respect to perturbations of the model.     

Probing Xist RNA Structure in Cells Using Targeted Structure-Seq

The long non-coding RNA (lncRNA) Xist is a master regulator of X-chromosome inactivation in mammalian cells. Models for how Xist and other lncRNAs function depend on thermodynamically stable secondary and higher-order structures that RNAs can form in the context of a cell. Probing accessible RNA bases can provide data to build models of RNA conformation that provide insight into RNA function, molecular evolution, and modularity. To study the structure of Xist in cells, we built upon recent advances in RNA secondary structure mapping and modeling to develop Targeted Structure-Seq, which combines chemical probing of RNA structure in cells with target-specific massively parallel sequencing. By enriching for signals from the RNA of interest, Targeted Structure-Seq achieves high coverage of the target RNA with relatively few sequencing reads, thus providing a targeted and scalable approach to analyze RNA conformation in cells. We use this approach to probe the full-length Xist lncRNA to develop new models for functional elements within Xist, including the repeat A element in the 5’-end of Xist. This analysis also identified new structural elements in Xist that are evolutionarily conserved, including a new element proximal to the C repeats that is important for Xist function.     

Deterministic epidemiological models at the individual level

In many fields of science including population dynamics, the vast state spaces inhabited by all but the very simplest of systems can preclude a deterministic analysis. Here, a class of approximate deterministic models is introduced into the field of epidemiology that reduces this state space to one that is numerically feasible. However, these reduced state space master equations do not in general form a closed set. To resolve this, the equations are approximated using closure approximations. This process results in a method for constructing deterministic differential equation models with a potentially large scope of application including dynamic directed contact networks and heterogeneous systems using time dependent parameters. The method is exemplified in the case of an SIR (susceptible-infectious-removed) epidemiological model and is numerically evaluated on a range of networks from spatially local to random. In the context of epidemics propagated on contact networks, this work assists in clarifying the link between stochastic simulation and traditional population level deterministic models.