Developing Mathematical Models for Immune Interactions in Cancer, Cachexia, and the Microbiome

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Mathematical models can tease apart the nonlinear dynamics of complex biological interactions by examining individual elements in a controlled manner. Interactions between human immunity and physiology are difficult to examine through traditional animal or cell line models due to the heterogeneous populations and many confounding variables. This research program creates new mathematical models describing the interactions between the immune system, normal tissues, and cancer, resulting in the creation of new model formulations and improved understanding of the biological system. ******This proposal contains three main objectives that all involve the creation of novel multi-compartment systems of differential equations to describe the interactions of the immune system with cancer or other host tissues. The first objective examines the immune-cancer relationship, including inflammation and aging effects on immune function. The second objective examines the loss of muscle or adipose tissue in cancer cachexia. While the third objective examines the relationship between the gut microbiome, immune system, and cancer. Newly developed differential equation-based models will describe the cellular interactions in a formulation that can be analyzed computationally. Genomic data analyses will be used to suggest possible biological mechanisms responsible for the cellular dysfunctions resulting from cancer presence. These mechanisms will be encoded into the model formulations where they can be rigorously studied. Experimental data will be used for model construction and validation, guaranteeing the creation of good quality predictive tools. ******The mathematical models created in objective three, describing immune-microbiome-cancer interactions, will be the first of their kind. The formulations used in objectives one and two are unique to this research program, are more biologically accurate than existing models, and are worthy of further research and development. Newly created models will extend the mathematical atlas available to mathematical biologist, evolutionary ecologists, and others who study nonlinear population dynamics. This work will formalize methods of data integration into mathematical modelling by distilling genomic data into fundamental biological mechanisms that can be encoded into model forms and parameters. The developed models may lead to future experimental or clinical applications where implications to human health and cancer treatment can be made. Indeed, improved understanding of the nonlinear dynamics occurring between the immune system, host physiology, and cancer, through mathematical modelling, can only lead to improved interventions for health maintenance and disease treatment, including immunotherapy and intestinal microbiome manipulation.