Robi Tacutu, Ph.D.
Group: Systems Biology of AgingDepartment: Bioinformatics & Structural Biochemistry
Currently working on
Robi continued his research as a postdoc (2011-2015) 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.
Since 2016, Robi leads the Systems Biology of Aging Group (a group of more than 15 people) at the Institute of Biochemistry and 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.
Robi has expertise in Biology of Ageing, Bioinformatics, Systems Biology, Network Biology, and Synthetic Biology.
- . Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals. International journal of molecular sciences, 2021, 22(3).IF=4.56
- . Gray whale transcriptome reveals longevity adaptations associated with DNA repair and ubiquitination. Aging cell, 2020, 19(7):e13158.IF=7.24
- . SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes. Scientific data, 2020, 7(1):366.IF=5.54
- . MetaboAge DB: a repository of known ageing-related changes in the human metabolome. Biogerontology, 2020, 21(6):763-771.IF=3.77
- . LRRpredictor-A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers. Genes (Basel), 2020, 11(3):286.IF=3.33
- . Learning flat representations with artificial neural networks. Applied Intelligence, 2020.IF=3.33
- . A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome biology, 2020, 21(1):91.IF=10.81
- . Human Ageing Genomic Resources: new and updated databases. Nucleic acids research, 2018, 46(D1):D1083-D1090.IF=11.15
- . Wide-scale comparative analysis of longevity genes and interventions. Aging cell, 2017, 16(6):1267-1275.IF=7.63
- . A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans. Aging cell, 2016, 15(2):256-66.IF=6.71
- . Systematic analysis of the gerontome reveals links between aging and age-related diseases. Human molecular genetics, 2016, 25(21):4804-4818.IF=5.34
- . Tissue repair genes: the TiRe database and its implication for skin wound healing. Oncotarget, 2016, 7(16):21145-55.IF=5.17
- . MitoAge: a database for comparative analysis of mitochondrial DNA, with a special focus on animal longevity. Nucleic acids research, 2016, 44(D1):D1262-5.IF=10.16
- . The Digital Ageing Atlas: integrating the diversity of age-related changes into a unified resource. Nucleic acids research, 2015, 43(Database issue):D873-8.IF=9.20
- . Transcriptome analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging. Genome biology, 2015, 16:285.IF=11.31
- . Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic acids research, 2013, 41(Database issue):D1027-33.IF=8.81
- . LongevityMap: a database of human genetic variants associated with longevity. Trends in genetics : TIG, 2013, 29(10):559-60.IF=11.60
- . Gadd45 proteins: relevance to aging, longevity and age-related pathologies. Ageing research reviews, 2012, 11(1):51-66.IF=5.95
- . Prediction of C. elegans longevity genes by human and worm longevity networks. PloS one, 2012, 7(10):e48282.IF=3.73
- . Molecular links between cellular senescence, longevity and age-related diseases - a systems biology perspective. Aging, 2011, 3(12):1178-91.IF=5.13
- . Is rate of skin wound healing associated with aging or longevity phenotype?. Biogerontology, 2011, 12(6):591-7.IF=3.34
- . MicroRNA-regulated protein-protein interaction networks: how could they help in searching for pro-longevity targets?. Rejuvenation research, 2010, 13(2-3):373-7.IF=4.22
- . The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. Biogerontology, 2010, 11(4):513-22.IF=3.41
- . The signaling hubs at the crossroad of longevity and age-related disease networks. The international journal of biochemistry & cell biology, 2009, 41(3):516-20.IF=4.89
- . Common gene signature of cancer and longevity. Mechanisms of ageing and development, 2009, 130(1-2):33-9.IF=4.18
- . Explaining Health Across the Sciences in Healthy Biological Systems, 2020(5), Springer, Cham:53-78.
- . Handbook of the Biology of Aging in Integrative Genomics of Aging, 2016(9), Elsevier, Academic Press:263-285.
- . Glycosilation in Structural Assessment of Glycosylation Sites Database - SAGS – An Overall View on N-Glycosylation, 2012(1), InTech:3-20.
Starting 02.09.2016, the Institute of Biochemistry of the Romanian Academy is implementing the project “Multi-omics prediction system for prioritization of gerontological interventions”, co-funded through European Fund for Regional Development, in accordance with the funding contract signed by the Ministry of National Education and Scientific Research. The total funding for the project is 8.524.757,50 lei, of which 8.502.557,50 lei represent non-reimbursable funding. The project’s duration is 48 months.
The Systems Biology of Aging team is grateful for the "Microsoft Azure for Research" sponsorship awarded to our group. We have received cloud computing resources worth the equivalent of 20,000$ credits, and this has greatly helped us to speed up some of our research projects.
Starting 01.06.2019, the Institute of Biochemistry of the Romanian Academy is implementing the EMBED project, funded by UEFISCDI (contract 103, from 01.06.2019), through the ERA-NET COFUND-NEURON III grant call. The project aims to assess the shared molecular links between pre- and post-natal, metabolic and psychosocial stress, and the risks of depression later in life, and its duration will be 36 months.
The project aims to analyze and compare the age-related transcriptomics signatures in variuos tisues, both in healthy and pathological individuals, in order to identify shared or unique aging signature that drive aging or age-related diseases.
The project aims to experimentally develop an integrated and automated solution for screening drugs and genetic interventions for neurodegenerative diseases, using the nematode C. elegans and ageing-related data.