Research Themes
The Kogod Center on Aging has three research themes that contribute valuable information about the aging process.
Artificial Intelligence and Quantitative Biology Theme
Approaches within the Artificial Intelligence and Quantitative Biology Theme span a diverse array of methodologies, including mathematical modeling, statistical analysis, computational simulation and machine learning.
Quantitative biology is rooted in the principles of calculus, probability theory and differential equations. It provides quantitative frameworks essential for understanding complex biological phenomena.
Our scientists in the Kogod Center on Aging use quantitative biology to decipher the dynamics of stem cell populations, cancer cells and the expression of genetic risk factors within populations. These methodologies often generate straightforward, testable predictions, which enhances our fundamental understanding of aging processes with the aim of promoting health in older age.
The artificial intelligence (AI) revolution has catalyzed the development of machine learning algorithms, empowering computers with problem-solving capabilities. Unlike quantitative biology, AI can improve healthcare outcomes without a deep understanding of underlying biological mechanisms.
AI progress is evident in various domains, such as diagnosing heart arrhythmias through smartwatches, discerning patterns within genomic data, and predicting individualized responses to medications.
However, AI and quantitative biology frequently synergize and complement each other when developed and explored together.
AI excels in processing vast datasets and identifying patterns, while quantitative biology provides the foundational understanding necessary for accurate interpretation of biological phenomena. This collaboration delivers interdisciplinary insights, leading to more robust solutions and discoveries in both fields.
The objective of this theme is to enable research in AI and quantitative biology to address the most pressing questions in aging research and promote healthy aging.
We aim to accomplish this objective by supporting interdisciplinary collaboration. Scientists from disciplines such as mathematics, computer science, engineering and physics collaborate with researchers who have deep insights into the biology of aging processes and clinicians well-versed in the clinical manifestations of aging. In addition, this theme facilitates training for quantitative researchers to embark on careers in biological aging research.
Investment in artificial intelligence and quantitative biology represents a timely effort to leverage the unparalleled health data resources Mayo Clinic is establishing within a secure environment.
Clinical Applications Theme
The theme leader is Fernanda Bellolio, M.D., M.S.
The Clinical Applications Theme fosters and supports rigorous research in several areas. These areas include epidemiology, healthcare delivery, patient-centered outcomes, implementation science, pragmatic trials, and health policy research conducted in community and real-world practice settings.
The goals of this research theme are to improve health and quality of life, reduce the burden of treatment, bolster the caregiver workforce, and eliminate health disparities among minoritized, rural and other underserved populations. This research theme supports the development and implementation of a geriatric learning health system at Mayo Clinic.
The focus of this theme is leading the development and dissemination of evidence-based, effective, equitable, and goal-concordant therapies and care models across populations to improve the health, functional status and quality of life of older adults and their caregivers.
This theme is composed of a large multispecialty and collaborative group of clinicians, epidemiologists and scientists. This group works together to advance clinical applications of research, translate research and implement research findings to the targeted populations.
Discovery Science Theme
The theme director is Joao Passos, Ph.D.
The Discovery Science Theme aims to broaden and deepen discovery science into fundamental aging processes with the potential to be therapeutically targeted to transform human health.
The Robert and Arlene Kogod Center on Aging stands at the scientific forefront of aging research, with cellular senescence as a unifying theme. The Discovery Science Theme builds on our legacy and past achievements while looking to the future by expanding our expertise into the investigation of other basic mechanisms of aging. The theme works closely with the programs within the center to advance discovery science into fundamental aging processes.
Research focus areas within the Discovery Science Theme include:
Establishing a common resource for single-cell molecular phenotyping
To fully understand the cellular specificity and complexity of tissue microenvironments during aging, it is necessary to measure molecular signatures with single-cell resolution. Ongoing technological advances are providing unprecedented opportunities to analyze the complexities of biological systems at the single-cell level, such as the proteome, transcriptome and epigenome.
We aim to be at the forefront of this technological revolution and invest in new platforms for single-cell molecular phenotyping. More importantly, we support specialized investigators capable of maintaining and building on such resources.
Expanding pioneering work on cellular senescence
Our previous work established cellular senescence as a key driver of aging and age-related pathology, which ultimately led to the development of senolytic drugs. While there is great promise in the therapeutic application of senolytic drugs, there are potential pitfalls related to specificity to different senescent subtypes and cell types.
In this theme, we're building on our previous accomplishments and investing in the design and discovery of novel, more-selective ways to target senescent cells. One potential avenue of great promise is to comprehensively identify cell-surface proteins that are specifically upregulated in senescent cells, which can then be targeted by the immune system.
The theme supports initiatives aiming to profile the surfacome of senescent cells from different cell types. In addition, our experts in this theme are partnering with experts in immunology, vaccine development and chimeric antigen receptor (CAR)-T cell therapy at Mayo Clinic and elsewhere to explore novel therapeutic avenues that target senescent cells.
Developing a bioinformatic approach to understanding aging
Probing the complexity of the aging process calls for an approach that effectively combines the strengths of the reductionist and integrative strategies. For that goal to be achieved, a multidisciplinary approach is essential, involving close collaboration among biologists and computer scientists. These interactions will be increasingly important given technological advances in single-cell molecular profiling.
This theme also seeks to further expand the center's capabilities in different aspects of computational biology, including analysis of large datasets, analytical software development and artificial intelligence. As part of this effort, we aim to establish closer partnerships with our Mayo Clinic colleagues in the Department of Artificial Intelligence and Informatics and the Biomedical Imaging Resource Core, along with external partners in the private sector.
Expanding expertise in basic mechanisms of aging
The theme aims to establish strategic partnerships with basic science departments at Mayo Clinic. We support and nurture new opportunities aiming to investigate cross-talk among diverse fundamental aging mechanisms, such as telomere dysfunction, apoptosis and autophagy impairment. We also aim to establish closer partnerships with other aging centers, both nationally and internationally, focused on discovery science.