Project Description

Over the past decade, two approaches have moved to the centre of longevity research. Epigenetic aging clocks use molecular biomarkers to estimate biological age and to gauge how an intervention shifts it. Partial cellular reprogramming can restore youthful molecular profiles and is among the most promising routes to reversing aspects of biological aging. Both have delivered remarkable results, yet they share the same limitation: they establish whether an intervention changes biological age, but not why. Because current models rely largely on DNA-methylation data and produce correlative estimates, the mechanisms that drive aging and rejuvenation across the molecular, cellular, and tissue levels remain poorly understood. Combined with limited transparency and unresolved ethical questions around sensitive genetic data, this missing mechanistic insight constrains trust and slows clinical translation.

TRAIS aims to address this gap through two coupled research streams. A technical and data stream will develop interpretable, data-driven methods that integrate multi-omics data across molecular, cellular, and tissue scales; by combining modern data science with explainable AI (XAI) and causal machine learning, it will work to separate the causal drivers of aging from mere indicators, decompose the mechanisms behind observed change, and translate its signals into explanations adapted to the needs of researchers, clinicians, and study participants. To operate at the scale of the field, the stream will be supported by an agentic AI system that continuously mines the research literature into a living knowledge graph and helps design, run, and document experiments, while the researchers make the final scientific call. A behavioral and trust stream will study the other side of the same problem – how transparency, perceived control, and prevailing narratives about longevity shape whether people trust AI-derived biomarkers and hypothetical rejuvenation interventions, and what conditions make their adoption responsible. Rigour on one stream is meant to earn justified trust on the other.

TRAIS is organised as a tandem collaboration between the TUM Heilbronn Data Science Center (HDSC) in Heilbronn and the Munich Data Science Institute (MDSI) in Garching, bringing together expertise in data science, machine learning, bioinformatics, and behavioral trust research.

Contact:

Prof. Dr. Ali Sunyaev, Felix Pietsch

Project Group:

Prof. Dr. Ali Sunyaev, Prof. Dr. Jens Großklags, Felix Pietsch, Philipp A. Toussaint

Funding:

HDSC-MDSI Tandem Projects (TUM Heilbronn Data Science Center / Munich Data Science Institute)

Partner:

TUM Heilbronn Data Science Center (HDSC), Heilbronn · Munich Data Science Institute (MDSI), Garching

Duration:

3 years (starting 2026)