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

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