Robi Tacutu, Ph.D.
Group: Systems Biology of AgingDepartment: Bioinformatics & Structural Biochemistry
Group leader, CS II
Currently working on
A thorough, analytical and curiosity-driven scientist, with a multidisciplinary background in biology and computer science, Robi received his BSc in computer science from the University Politehnica Bucharest, and his MSc in biochemistry and molecular biology from the University of Bucharest. He has a long-term commitment and interest in the field of biogerontology (since 2005) and received his PhD in 2013 from the Ben-Gurion University of the Negev, in the lab of Prof. Vadim Fraifeld. His thesis was on an ageing-related topic: studying the 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. 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.
. "Targeting EDEM protects against ER stress and improves development and survival in C. elegans", PLoS genetics 18(2): e1010069, (2022)
IF: 5.90AI: 2.47
. "Small molecules for cell reprogramming: a systems biology analysis", Aging 13(24): 25739-25762, (2021)
IF: 5.68AI: 1.16
. "Knock-down of odr-3 and ife-2 additively extends lifespan and healthspan in C. elegans", Aging 13(17): 21040-21065, (2021)
IF: 5.60AI: 1.16
. "Learning flat representations with artificial neural networks", Applied Intelligence(51): 2456–2470, (2021)
IF: 5.09AI: 0.69
. "Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals", International journal of molecular sciences 22(3): 1073, (2021)
IF: 4.56AI: 1.06
. "Systems biology analysis of lung fibrosis-related genes in the bleomycin mouse model", Scientific reports 11(1): 19269, (2021)
IF: 4.38AI: 1.21
. "A multidimensional systems biology analysis of cellular senescence in aging and disease", Genome biology 21(1): 91, (2020)
IF: 10.81AI: 9.03
. "Gray whale transcriptome reveals longevity adaptations associated with DNA repair and ubiquitination", Aging cell 19(7): e13158, (2020)
IF: 7.24AI: 2.61
. "SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes", Scientific data 7(1): 366, (2020)
IF: 5.54AI: 3.25
. "MetaboAge DB: a repository of known ageing-related changes in the human metabolome", Biogerontology 21(6): 763-771, (2020)
IF: 3.77AI: 1.13
. "LRRpredictor-A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers", Genes (Basel) 11(3): 286, (2020)
. "Human Ageing Genomic Resources: new and updated databases", Nucleic acids research 46(D1): D1083-D1090, (2018)
IF: 11.15AI: 4.48
. "Wide-scale comparative analysis of longevity genes and interventions", Aging cell 16(6): 1267-1275, (2017)
IF: 7.63AI: 2.30
. "MitoAge: a database for comparative analysis of mitochondrial DNA, with a special focus on animal longevity", Nucleic acids research 44(D1): D1262-5, (2016)
IF: 10.16AI: 3.84
. "A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans", Aging cell 15(2): 256-66, (2016)
IF: 6.71AI: 2.41
. "Systematic analysis of the gerontome reveals links between aging and age-related diseases", Human molecular genetics 25(21): 4804-4818, (2016)
IF: 5.34AI: 2.42
. "Tissue repair genes: the TiRe database and its implication for skin wound healing", Oncotarget 7(16): 21145-55, (2016)
IF: 5.17AI: 1.18
. "Transcriptome analysis in calorie-restricted rats implicates epigenetic and post-translational mechanisms in neuroprotection and aging", Genome biology 16: 285, (2015)
IF: 11.31AI: 6.82
. "The Digital Ageing Atlas: integrating the diversity of age-related changes into a unified resource", Nucleic acids research 43(Database issue): D873-8, (2015)
IF: 9.20AI: 3.65
. "LongevityMap: a database of human genetic variants associated with longevity", Trends in genetics : TIG 29(10): 559-60, (2013)
IF: 11.60AI: 5.00
. "Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing", Nucleic acids research 41(Database issue): D1027-33, (2013)
IF: 8.81AI: 3.40
. "Gadd45 proteins: relevance to aging, longevity and age-related pathologies", Ageing research reviews 11(1): 51-66, (2012)
IF: 5.95AI: 1.90
. "Prediction of C. elegans longevity genes by human and worm longevity networks", PloS one 7(10): e48282, (2012)
IF: 3.73AI: 1.50
. "Molecular links between cellular senescence, longevity and age-related diseases - a systems biology perspective", Aging 3(12): 1178-91, (2011)
IF: 5.13AI: 1.30
. "Is rate of skin wound healing associated with aging or longevity phenotype?", Biogerontology 12(6): 591-7, (2011)
IF: 3.34AI: 0.70
. "MicroRNA-regulated protein-protein interaction networks: how could they help in searching for pro-longevity targets?", Rejuvenation research 13(2-3): 373-7, (2010)
IF: 4.22AI: 0.60
. "The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes", Biogerontology 11(4): 513-22, (2010)
IF: 3.41AI: 0.80
. "The signaling hubs at the crossroad of longevity and age-related disease networks", The international journal of biochemistry & cell biology 41(3): 516-20, (2009)
IF: 4.89AI: 1.70
. "Common gene signature of cancer and longevity", Mechanisms of ageing and development 130(1-2): 33-9, (2009)
IF: 4.18AI: 1.40
. "Integrative Genomics of Aging", pp 151-171, Handbook of the Biology of Aging, Academic Press, Elsevier, (2021).
. "Healthy Biological Systems", pp 53-78, Explaining Health Across the Sciences, Springer, Cham, (2020).
. "Integrative Genomics of Aging", pp 263-285, Handbook of the Biology of Aging, Elsevier, Academic Press, (2016).
. "Structural Assessment of Glycosylation Sites Database - SAGS – An Overall View on N-Glycosylation", pp 3-20, Glycosilation, InTech, (2012).
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.