UCLA Institute for Pure & Applied Mathematics (IPAM) Microbiome Seminar
Jan 23 – Jan 24 all-day

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The study of the human microbiome has seen an explosive growth in the past decade, primarily driven by advances in sequencing technologies and computational resources.

The microbial cells that colonize the human body, including intestinal and skin environments, are at least as abundant as our somatic cells and contain a much greater number of genes than the human genome. However, different people harbor radically different collections of microbes and we do not yet understand how the variation within a person over time or that between different people influences wellness, the preservation of health or the risk for or onset of disease. Experimental studies reveal possible patho-physiologies linked to abnormalities in the microbiome such as cancer, diseases of the skin, metabolism, malnutrition, food allergies, autoimmune and psychiatric disorders. Many of these disorders have been increasing in prevalence during the past 50 years and have been linked to changes in lifestyle, diet, the use of antibiotics and the resulting decline in microbial diversity.

Current concepts about the health of the microbiome are based on ecological and systems biological concepts of diversity, abundance, resilience and resistance to perturbations. In this workshop we will explore mathematical approaches to better frame, explore and attempt to answer several fundamental questions related to the microbiome. For example, how can we quantitatively define a healthy microbiome? What inputs or environmental changes will it respond to? How do these responses change in individuals over time, as the microbiome reproduces, or as a function of its early phases (prenatal to the first three years of infancy)? Are there memory effects? Can we identify target parameters or metrics that can be optimized to say, engineer an optimal aspect of the microbiome? We will also discuss state of the art microbiome data (metagenomics, metatranscriptomics, metabolomics), comprehensive metadata (diet, medication use, life style, brain imaging, autonomic nervous system recording, etc) and possible big data analysis of the above datasets.