Incorporating demographic stochasticity into multi-strain epidemic models: application to influenza A

We develop mathematical models of the transmission and evolution of multi-strain pathogens that incorporate strain extinction and the stochastic generation of new strains via mutation. The dynamics resulting from these models is then examined with the applied aim of understanding the mechanisms underpinning the evolution and dynamics of rapidly mutating pathogens, such as human influenza viruses. Our approach, while analytically relatively simple, gives results that are qualitatively similar to those obtained from much more complex individually based simulation models. We examine strain dynamics as a function of cross-immunity and key transmission parameters, and show that introducing strain extinction and modelling mutation as a stochastic process significantly changes the model dynamics, leading to lower strain diversity, reduced infection prevalence and shorter strain lifetimes. Finally, we incorporate transient strain-transcending immunity in the model and demonstrate that it reduces strain diversity further, giving patterns of sequential strain replacement similar to that seen in human influenza A viruses

Improving the realism of deterministic multi-strain models: implications for modelling influenza A

Understanding the interaction between epidemiological and evolutionary dynamics for antigenically variable pathogens remains a challenge, particularly if analytical insight is wanted. In particular, while a variety of relatively complex simulation models have reproduced the evolutionary dynamics of influenza, simpler models have given less satisfying descriptions of the patterns seen in data. Here, we develop a set of relatively simple deterministic models of the transmission dynamics of multi-strain pathogens which give increased biological realism compared with past work. We allow the intensity of cross-immunity generated against one strain given exposure to a different strain to depend on the extent of genetic difference between the strains. We show that the dynamics of this model are determined by the interplay of parameters defining the cross-immune response function and can include fully symmetric equilibria, self-organized strain structures, regular periodic and chaotic regimes. We then extend the model by incorporating transient strain-transcending immunity that acts as a density-dependent mechanism to lower overall infection prevalence and thus pathogen diversity. We conclude that while some aspects of the evolution of influenza can be captured by deterministic models, overall, the description obtainable using a purely deterministic framework is unsatisfactory, implying that stochasticity of strain generation (via mutation) and extinction needs to be captured to appropriately capture influenza dynamics

The seasonal migrations of a Siberian Roe deer population

Les auteurs rapportent les résultats d'une étude a long terme de l'écologie d'une population migratrice de Chevreuil sibérien (Capreolus pygargus Pall.) de la région de l'Amur. Des les premiers froids en septembre, et de 1 à 1,5 mois avant la formation du tapis neigeux, ces chevreuils quittent leurs territoires estivaux pour gagner des quartiers d'hiver situés à 100-200 km de distance. Ils y occupent une ou deux zones d'une superficie allant de 1800 a 14 000 hectares. Pour la plupart des individus, la migration de printemps ne débute pas avant la fin avril ou le début ma

Systems biology approach in the development of chemically-defined media for production of protein therapeutics in Chinese hamster ovary cells

Cell culture medium plays a critical role on mammalian cell growth, protein expression and quality. Typical cell culture medium formulations consist of \u3e50 components which include amino acids, vitamins, trace metals, lipids and proteins. Chinese Hamster Ovary (CHO) cells that produce biotherapeutics are propagated in specific cell culture media to ensure robust productivity and product quality. Systems biology has been applied to multiple areas of biological research to gain a better understanding of disease origins and to identify potential new drug targets. Although CHO cells are simpler systems, they share similar biochemistry and cellular pathways. Therefore, leveraging the systems biology knowledge from animal systems and applying these strategic systems biological tools to bioprocess development can be valuable in gaining better understanding of CHO cell culture performance, optimizing cell culture media, and subsequently resulting in better control of the overall production processes. In this presentation, we will present several case studies of various ‘omics tools applied to (1) optimize cell culture medium formulation for improve cell growth and productivity via metabolomics, (2) understand effects of medium components on cellular gene expression via transcriptomics, and on product quality via glycomics, and (3) identify potential cellular protein targets that are affected by stress imposed during production process via proteomics. The development of a statistical model that aims to highlight key metabolites and a machine learning model that identifies significantly important genes which are involved in monoclonal antibody production will also be discussed

Cross section for the H + H2O abstraction reaction: experiment and theory

The absolute value of the cross section for the abstraction reaction between fast H atoms and H2O has been determined experimentally at a mean collision energy of 2.46 eV. The OH population distribution at the same mean energy has also been determined. The new measurements are compared with state-ofthe- art quantum mechanical and quasiclassical scattering calculations on the most recently developed potential energy surface

Five challenges in modelling interacting strain dynamics

Population epidemiological models where hosts can be infected sequentially by different strains have the potential to help us understand many important diseases. Researchers have in recent years started to develop and use such models, but the extra layer of complexity from multiple strains brings with it many technical challenges. It is therefore hard to build models which have realistic assumptions yet are tractable. Here we outline some of the main challenges in this area. First we begin with the fundamental question of how to translate from complex small-scale dynamics within a host to useful population models. Next we consider the nature of so-called "strain space". We describe two key types of host heterogeneities, and explain how models could help generate a better understanding of their effects. Finally, for diseases with many strains, we consider the challenge of modelling how immunity accumulates over multiple exposures

Influenza A Gradual and Epochal Evolution: Insights from Simple Models

The recurrence of influenza A epidemics has originally been explained by a “continuous antigenic drift” scenario. Recently, it has been shown that if genetic drift is gradual, the evolution of influenza A main antigen, the haemagglutinin, is punctuated. As a consequence, it has been suggested that influenza A dynamics at the population level should be approximated by a serial model. Here, simple models are used to test whether a serial model requires gradual antigenic drift within groups of strains with the same antigenic properties (antigenic clusters). We compare the effect of status based and history based frameworks and the influence of reduced susceptibility and infectivity assumptions on the transient dynamics of antigenic clusters. Our results reveal that the replacement of a resident antigenic cluster by a mutant cluster, as observed in data, is reproduced only by the status based model integrating the reduced infectivity assumption. This combination of assumptions is useful to overcome the otherwise extremely high model dimensionality of models incorporating many strains, but relies on a biological hypothesis not obviously satisfied. Our findings finally suggest the dynamical importance of gradual antigenic drift even in the presence of punctuated immune escape. A more regular renewal of susceptible pool than the one implemented in a serial model should be part of a minimal theory for influenza at the population level

An Agent-Based Model to study the epidemiological and evolutionary dynamics of Influenza viruses

<p>Abstract</p> <p>Background</p> <p>Influenza A viruses exhibit complex epidemiological patterns in a number of mammalian and avian hosts. Understanding transmission of these viruses necessitates taking into account their evolution, which represents a challenge for developing mathematical models. This is because the phrasing of multi-strain systems in terms of traditional compartmental ODE models either requires simplifying assumptions to be made that overlook important evolutionary processes, or leads to complex dynamical systems that are too cumbersome to analyse.</p> <p>Results</p> <p>Here, we develop an Individual-Based Model (IBM) in order to address simultaneously the ecology, epidemiology and evolution of strain-polymorphic pathogens, using Influenza A viruses as an illustrative example.</p> <p>Conclusions</p> <p>We carry out careful validation of our IBM against comparable mathematical models to demonstrate the robustness of our algorithm and the sound basis for this novel framework. We discuss how this new approach can give critical insights in the study of influenza evolution.</p