Computational Biology of Aging Group
brand new lab, awesome international team, cool tools and models
Aging is a biological process defined by a progressive loss of viability and an exponential increase in fragility and vulnerability. Age is also the main risk factor for many diseases, including most types of cancers, heart and vascular diseases, type 2 diabetes, neurodegenerative diseases, etc. This is not surprising, as at the molecular level, age-related diseases share many genetic components and molecular pathways with the “normal” aging process.
Understanding the aging process, and the mechanisms underlining it, is one of the major biological and biomedical challenges of our century and could result in much higher dividends to society in our capacity to extend lifespan and more importantly healthspan (i.e. the interval of healthy, productive years in a person’s life).
With the current advances in high-throughput technologies many of the genetic and molecular aspects of aging can now be easily screened at various “omics” levels, using a wide range of models and starting from various hypotheses. While all the existing datasets are extremely valuable by themselves, they pose an incomparably higher potential if analyzed together. In terms of data integration however, more efforts are required to achieve a cohesive approach and an integrative view on how these molecular measurements are all interconnected and manifest as aging and/or age-related diseases. Such a multi-model integration, combined with systems biology approaches will be of paramount importance in the coming years and will maximize the amount of knowledge that we can gather from gerontological observational studies.
Our aim is to 1) integrate and analyze large-scale datasets from different biological levels, like genomics, transcriptomics, or epigenomics, and 2) to use frontier systems biology approaches, network biology, machine learning, and artificial intelligence, to create mathematical and computational models of aging.
Using these data, models and algorithms, we aim to predict novel genetic and epigenetic interventions that would have the highest potential to impact lifespan in model organisms.
Our projects include both computational aspects (data aggregation and processing, multidimensional data analysis, network-based methods, systems theory approaches, deep learning, etc.) as well as wet-lab experiments (in particular in-vivo testing of the computationally predicted interventions), with a highly multi-disciplinary team.
If you would like to learn more about our projects or any available jobs/internships in our group, just drop us a line.
The Computational Biology of Aging Group
The Computational Biology of Aging Group was founded by Dr. Robi Tacutu in 2016, and is mainly funded by a recently awarded EUR 2 million EU grant, for the project Multi-omics Prediction System for Prioritization of Gerontological Interventions.
Dr. Tacutu has a multidisciplinary background in computer science (BSc from University Politehnica Bucharest) and in molecular biology (MSc from University of Bucharest), and a long-term commitment and experience (10+ years) in the field of biogerontology. He received his PhD from the Ben-Gurion University of the Negev (in the lab of Prof. Vadim Fraifeld), studying relationships between aging and age-related diseases with the use of bioinformatics and network biology approaches, and developing computational methods to predict novel genetic determinants of longevity.
After his PhD, Dr. Tacutu continued his research as a postdoc at the University of Liverpool (in the Integrative Genomics of Ageing Group, led by Dr. Joao Pedro de Magalhaes). Here, he had a role in developing and curating the Human Ageing Genomic Resources collection of databases relevant to ageing research, and was later awarded a EU FP7 Marie Curie fellowship for developing and interrogating an integrated model of ageing to identify causal relationships between hormonal changes and gene expression changes.
Currently, Dr. Tacutu's research focuses on using computational methods in conjunction with large screening datasets to understand the genetic, cellular, and molecular mechanisms behind ageing, longevity and age-related diseases.
For a full list of publications, please go to: tacutu@pubmed