Visiting Speaker
Elena N. Naumova
Tufts University
Using Fractals to Measure Immunosenescence
Dec 7, 2018
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4:00 pm
177 Huntington Ave
11th floor
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A scale-free behavior has been observed in many living systems at micro- and macro-scales. These systems continue to stimulate the interests in theoretical studies, including the understanding of aging and specifically of immunosenescence. The immune system represents the major system with a large cellular component dedicated to the generation of adaptive memory to pathogens. It is this component of immunity which is the most instructive in understanding the life stages of humans.

In the experimental studies of the adaptive immune system, we had observed a scale-free network governing the repertoire of memory T-cells (Naumov et al, 2003). At the molecular level, we observe that a memory immune response to influenza virus becomes diverse upon repeated exposures to the virus that can be modeled as a fractal self-similar system. Theoretical explanation of experimental findings has been described by the small-world construction (Ruskin and Burns, 2006) as a special case of the scale-free network (Albert and Barabasi, 2002). We then simulated the fractal behavior mimicking immune memory - its generation, maintenance and senescence (Naumova et al, 2008) and experimentally illustrated the general stability of the power-law structures and age-related changes. Our recent theoretical work confirms the assumptions that multiple expansion-contraction cycles define the robustness of immune response and correspond to memory formation (Saito and Narikiyo, 2011). Saito and Narikiyo had proposed the dynamical network of the adaptive immune system as a self-organized critical state in which the avalanche feedback reinforcement may reduce immunosenescence.

At the population level, we also observed the evidence of exposure to influenza as a marker of “immunological age.” In the cohort of healthy donors, each encounter with an infectious agent was unique for every person. Yet, the commonality in responses formed “immunological kinship” among all affected individuals, manifested by a preserved T-cell clonal pool. The diverse responses to flu and changes in diversity allow us to make an inference to “immunological kinship” and “immunological age.” Our experimental data indicate that at a certain point the continuing exposures to influenza begin to decrease the diversity of immune response. These observations lead us to explore theoretical conditions governing the “stable” and “volatile” components of the T-cell repertoires via dynamic neural networks. Such separation allowed us to detect a condition indicative of acceleration of immune aging. We derived the initial network parameters based on a specially designed anchored power-law regression fit of experimental data from middle-aged and older donors over time and illustrated age acceleration and immunosenescence in humans.

Albert, R., and Barabási, A.L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47-97.
Naumov, Y.N., Naumova, E.N., Hogan, K.T., Selin, L.K., and Gorski, J. (2003). A fractal clonotype distribution in the CD8+ memory T cell repertoire could optimize potential for immune responses. J Immunol 170, 3994-4001
Naumova, E.N., Gorski, J., and Naumov, Y.N. (2008). Simulation studies for a multistage dynamic process of immune memory response to influenza: experiment in silico. Ann Zool Fennici 45, 369-
Ruskin, H.J., and Burns, J. (2006). Weighted networks in immune system shape space. Physica A: Statistical Mechanics and its Applications 365, 549-555.
Saito, S., and O. Narikiyo, O. (2011). Scale-free dynamics of somatic adaptability in immune system. Biosystems 103: 420-424.

about the speaker
Elena N. Naumova is the Chair of the Division of Nutrition Data Science, as well as a Professor at the Friedman School. Her research activities span a broad range of research programs in emerging and re-emerging diseases, environmental epidemiology, molecular biology, nutrition, and growth. Her primary expertise is in development of analytical tools for spatio-temporal and longitudinal data analysis applied to disease surveillance, exposure assessment, and studies of growth; creation and application of statistical tools to evaluate the influence of an extreme and/or intermediate event on spatial and temporal patterns. Dr. Naumova participates in international projects collaborating with epidemiologists, immunologists, and public health professionals in India, Kenya, Ghana, Ecuador, Japan, Canada, UK, and Russia. She applies theoretical work to studies of infections sensitive to climate variations and extreme weather events and facilitates utilization of novel data sources, including remote sensing data and satellite imagery for better understanding the nature and etiology of diseases on local and global scales. She is involved in a number of observational studies, meta-analyses, and clinical trials with complex schemes of recruitments, including birth cohorts studies with staggered enrollment and randomization at a household level. She utilizes multi-sourced environmetal databases, climate data repositories, and vital and hospitalization records, including Centers for Medicare and Medicaid Services and U.S. Census databases. Naumova serves on research review panels and editorial boards of scientific journals with the goal to shape and implement institutional policies on data sharing and management, data quality assurance and information security. As a Director of the NIH-sponsored Tufts Initiative for Forecasting and Modeling of Infectious Diseases (InForMID), she has set up workshops and training programs to support field research and analytical assessment of research data, advisied over 60 PhD/MS/MPH students at Tufts and co-directed the Tufts Institute of the Environment, an outstanding supporter for research projects for Tufts graduate students.

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