| Authors<\/th> | Poster<\/th> | Symposium<\/th> | Keywords<\/th>\n<\/tr>\n<\/thead>\n |
|---|---|---|---|
| Simonov R.A., Mikhaleva E.A., Ivannikova A.Y., Shevelyov Y.Y.<\/td> | Depletion of nucleoporin Elys does not result in cell cycle defects in Drosophila S2 cells<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | cell cycle, Drosophila, Elys, kinetochore, nucleoporin<\/td>\n<\/tr>\n |
| Mutin A.D., Saranchina A.E., Rzhechitskiy Y.A., Pomazkin V.K., Timofeyev M.A., Drozdova P.B.<\/td> | Web application speCOIdent for the analysis of genotyping results for cryptic species of Baikal amphipods belonging to the genus Eulimnogammarus<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | amphipods, Baikal, cytochrome c-oxidase (subunit I), molecular phylogenetics, speCOIden<\/td>\n<\/tr>\n |
| Grushina V., Pintus S.<\/td> | Prediction and annotation of alternative transcription starts in the chicken genome<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | CAGE, Gallus gallus genome, promotor shift, transcription start sites<\/td>\n<\/tr>\n |
| Penzin N., Penzar D.<\/td> | Application of evolutionary augmentations to work with genomic sequences<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | Augmentation, EvoAug, LegNet<\/td>\n<\/tr>\n |
| Ponomarenko M., Chadaeva I., Filonov S., etc<\/td> | RatDEGdb: a knowledge base of differentially expressed genes in the rat as a model object in biomedical research<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | animal models of human diseases, DEG, knowledge base, microarrays, PCR, Rattus norvegicus, RNA-seq<\/td>\n<\/tr>\n |
| Tverdokhleb N., Chadaeva I., Filonov S., etc<\/td> | Candidate SNP markers significantly altering the affinity of TATA-binding protein for the promoters of human hub genes for atherogenesis, atherosclerosis and atheroprotection<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | atherogenesis, atheroprotection, atherosclerosis, candidate SNP marker, gene expression change, hub gene, in silico verification, natural selection, SNP, TBP<\/td>\n<\/tr>\n |
| Podkolodnaya O, Chadaeva I., Zolotareva K., etc<\/td> | Promoters of genes encoding \u03b2-amylase, albumin, and globulin in food plants have weaker affinity for TATA-binding protein as compared to non-food plants: in silico analysis<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | albumin, allergen, food, globulin, plants, spontaneous artificial selection, \u03b2-amylase<\/td>\n<\/tr>\n |
| Zolotareva K., Chadaeva I., Khandaev B., etc<\/td> | AtSNP_TATAdb: a knowledge base of uniform guess of how promoter proximal SNPs alter Arabidopsis gene expression<\/a><\/td> | 1. \u201cGenomics, transcriptomics and bioinformatics\u201d<\/td> | SNP<\/td>\n<\/tr>\n |
| Pavlov S.R., Gursky V.V., Samsonova M.G., Kanapin A.A., Samsonova A.A.<\/td> | Model of energy balance in cells influenced by the cancer-specific abnormal activity of retrotransposons<\/a><\/td> | 2. \u201cSystems Computational Biology\u201d<\/td> | anticancer therapy, apoptosis, energy balance, mobile genetic elements, retrotransposons<\/td>\n<\/tr>\n |
| Volyanskaya A.R., Osik N.A., Yanshole L.V., Tsentalovich Yu.P., Gusev O.A., Kolpakov F.A., Akberdin I.R.<\/td> | Structural peculiarities in the FOXO1 signaling pathway mediated differences in the dynamics of neuropeptides expression regulating feeding behavior in slow- and fast-growing chickens<\/a><\/td> | 2. \u201cSystems Computational Biology\u201d<\/td> | mathematical modeling, poultry, feed intake regulation, AMPK\/mTOR signaling pathway<\/td>\n<\/tr>\n |
| Sushkov R.V., Mochalov A.V., Orlov Y.L., Savina E.A.<\/td> | Database queries for prediction and analysis of the spatial structure of the protein disulfide isomerase enzyme of the A4 family of humans and animals<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | correlation of protein structures, databases, medical informatics, phylogeny medical informatics, phylogeny, protein disulfide isomerase, statistical processing<\/td>\n<\/tr>\n |
| Bogdanova E., Chernukhin A., Shaitan K., Novoseletsky V.<\/td> | Using a neural network algorithm to predict binding affinity in ACE2-RBD protein-protein complexes<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | binding affinity, coronavirus, neural networks, protein-protein complexes<\/td>\n<\/tr>\n |
| Tereshkina K.B., Tereshkin E.V., Kovalenko V.V., Krupyanskii Y.F., Loiko N.G.<\/td> | Dynamics of DNA binding to stress-associated DNA-stabilizing bacterial proteins<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | all-atom molecular modeling, bacterial DNA stabilization, DNA-Dps crystals, Dps protein, ferritin-like proteins in bacteria<\/td>\n<\/tr>\n |
| Kalitvanskaya M.A., Makarov D.A., Khandazhinskaya A.L., Anashkina A. A.<\/td> | Search for possible molecular targets of new anti-tuberculosis compounds<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | anti-tuberculosis compounds, targets, tuberculosis<\/td>\n<\/tr>\n |
| Mokrushnikov P., Rudyak V.<\/td> | Formation of protein-lipid domains in plasma membranes<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | protein-lipid domains, protein-protein and protein-lipid interactions, structural changes in plasma membranes, \u03b2-structures<\/td>\n<\/tr>\n |
| Hamad M. S., Mikhail A. Pokrovskii, Hamad S.S., Usenov K., Andrey G. Pokrovskii, Elizaveta M. Shinkarenko, Anastasia O. Finke, Maksim E. Mironov, Elvira E. Shults<\/td> | In vitro cytotoxic potential in human cancer cell line (C33a HPV-negative counterparts cervical cancer cell) of plant alkaloid, terpenoid and steroid derivatives <\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | Anticancer, Antitumor, apoptosis, Cytotoxicity, Herbal medicine, Medicinal plant<\/td>\n<\/tr>\n |
| Martin C., Calder\u00f3n R., Nahuat, N., Conde, L., Gonz\u00e1lez M. R., Kant\u00fan N.<\/td> | Revealing G-quadruplex DNA structures in HPV16 using quantitative PCR stop and ligands<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | DNA, G-quadruplexes, HPV 16, Ligands, qPCR Stop<\/td>\n<\/tr>\n |
| Krivosheev A.S., Golovin A.V.<\/td> | Binding free energy of small molecule\/GLP-1R complexes calculations with funnel metadynamics<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | binding free energy, diabetes mellitus, funnel metadynamics, GLP-1R, molecular dynamics, obesity, small molecules<\/td>\n<\/tr>\n |
| Chuiko Ya.V., Golovin A. V.<\/td> | Calculating of SARS-CoV-2 RBD and antibody complex energy by metadynamics in MARTINI 3 for evaluating complex free energy<\/a><\/td> | 3. \u201cStructural biology and pharmacology: computational and experimental approaches\u201d<\/td> | antibody design, generative models, metadynamics, molecular dynamics<\/td>\n<\/tr>\n |
| Goncharova I.A., Zarubin A.A., Shipulina S.A., Panfilov D.S., Kozlov B.N., Nazarenko M.S.<\/td> | \nDNA methylation of non-coding RNA genes in aneurysm and atherosclerosis of the ascending aorta<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | aneurysm and atherosclerosis of ascending aorta, DNA methylation, non-coding RNA<\/td>\n<\/tr>\n |
| Pristyazhnyuk I., Minina J., Voinova V., Safonova M., Lagarkova M., Menzorov A.<\/td> | \nGeneration of three iPSC lines from fibroblasts of a patient with Cohen syndrome<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | Cohen syndrome, iPSCs<\/td>\n<\/tr>\n |
| Mednova I.A., Pozhidaev I.V., Paderina D.Z., Petkun D.A., Kornetova E.G., Ivanova S.A.<\/td> | Association between eNOS rs753482 polymorphism and abdominal obesity in patients with schizophrenia taking atypical antipsychotics<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | metabolic disorders, pharmacogenetics, schizophrenia, single nucleotide polymorphism<\/td>\n<\/tr>\n |
| Vyalova N., Mikhalitskaya E., Paderina D., Pozhidaev I., Roschina O.<\/td> | Association of a polymorphic variant of IL6 gene with the severity of depressive symptoms<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | depression, gene polymorphism, interleukin 6, severity of the disease<\/td>\n<\/tr>\n |
| Barbitoff Y., Changalidis A., Glotov A.<\/td> | Assessing the performance of tools for functional annotation of GWAS results using simulated data<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | complex traits, genetic variant, GWAS, simulated data<\/td>\n<\/tr>\n |
| Nikonova E.O., Komech E.A., Barinova A.A., Samitova A.F., Belova V.A., Buyanova A.A., Repinskaya Z.A., Suchalko O.N., Korostin D.O., Lukyanov S., Zvyagin I.V.<\/td> | Discovery Of Somatic Mutations Associated With Clonally Expanded T cells<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d<\/td> | ankylosing spondylitis, autoimmune diseases, clonally expanded T cell, cytomegalovirus, somatic mutations, T cell regulation<\/td>\n<\/tr>\n |
| Kobzeva K., Dorofeeva A., Bushueva O.<\/td> | HSP90AA1 gene polymorphisms and the risk of ischemic stroke<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d,<\/td> | chaperones, HSP90AA1, ischemic stroke, SNP<\/td>\n<\/tr>\n |
| Vitalik Bezuglov, Victoria Shtratnikova, Alexander Suvorov, Russ Hauser and Oleg Sergeyev<\/td> | Association of pubertal trajectories with molecular markers and health outcomes in young men: prospective cohort study<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d,<\/td> | epigenetic program, hormones, microRNA, piRNA, pubertal progression, RNA-seq, semen quality, small RNA, sperm, tRNA-derived small RNA<\/td>\n<\/tr>\n |
| Lazareva T., Barbitoff Y.<\/td> | Systematic analysis of genetic variants with conflicting interpretations of pathogenicity \n<\/a><\/td> | 4. \u201cEvolutionary, population and medical genomics\/genetics of human\u201d,<\/td> | ClinVar, genetic variant, variant interpretation<\/td>\n<\/tr>\n |
| Glotova A., Nassonova E., Abakumov E., Smirnov A.<\/td> | Culture independent barcoding of naked lobose amoebae (Amoebozoa: Tubulinea, Discosea, Variosea) from freshwater and soil habitats<\/a><\/td> | 5. \u201cEvolutionary, population genomics\/genetics and molecular phylogeny\u201d<\/td> | Amoebozoa, biogeography, Discosea, metabarcoding, Tubulinea, Variosea<\/td>\n<\/tr>\n |
| Nassonova E., Glotova A.<\/td> | Metabarcoding study of the phylogenetic diversity of basal Holomycota (Opisthokonta) in the bottom sediments of Turovskoe Lake (Northwestern Russia)<\/a><\/td> | 5. \u201cEvolutionary, population genomics\/genetics and molecular phylogeny\u201d<\/td> | Aphelida, Holomycota, metabarcoding, Microsporidia, molecular phylogeny, Rozellida<\/td>\n<\/tr>\n |
| Azarova D.S., Zemlyanskaya E.V., Lavrekha V.V.<\/td> | DyCeModel: a tool for 1D simulation for plant hormones control of tissue patterning using shoot and root apical meristems of Arabidopsis thaliana as examples<\/a><\/td> | 6. \u201cGenetics\/genomics, bioinformatics and systems biology of plants\u201d<\/td> | computer modelling, developmental trajectory, genetic algorithm, input data, plant hormones<\/td>\n<\/tr>\n |
| Maria Duk, Gurkina M.V., Nekrasov A.Y.<\/td> | GWAS using the IIIVmrMLM model provides new genomic as-sociations with agronomic traits in Chickpea<\/a><\/td> | 6. \u201cGenetics\/genomics, bioinformatics and systems biology of plants\u201d<\/td> | chickpea, Cicer arientinum, favorable alleles, GWAS, IIIVmrMLM<\/td>\n<\/tr>\n |
| Ivanova E.A.<\/td> | Eco-genomic stress-resistance of plants as strategy and tactics: supramolecular-proteomic system of developmental biology<\/a><\/td> | 6. \u201cGenetics\/genomics, bioinformatics and systems biology of plants\u201d<\/td> | Interphase chromatin topology, Karyogenomics wheat, Proteomics, signaling systems, Supramolecular biochemistry<\/td>\n<\/tr>\n |
| Sannikova A.V., Sharipova M.R., Shakirov E.V., Valeeva L.R.<\/td> | Intra- and interspecific variations in the telomere length of bryophytes<\/a><\/td> | 6. \u201cGenetics\/genomics, bioinformatics and systems biology of plants\u201d<\/td> | Ceratodon purpureus, liverworts, Marchantia polymorpha, mosses, Physcomitrium patens, Sphagnum, telomeres<\/td>\n<\/tr>\n |
| Duk M., Gursky V., Samsonova M., Surkova S.<\/td> | Application of mathematical modeling to analyze the flowering activation motif during vernalization in legumes<\/a><\/td> | 6. \u201cGenetics\/genomics, bioinformatics and systems biology of plants\u201d<\/td> | dynamical model, feed-forward loop, flowering, gene networks, legumes, vernalization<\/td>\n<\/tr>\n |
| Orbant M.O., Babochkina T.I., Fedorova S.A.<\/td> | Methylsulfonylmethane (MSM) increases the expression (genetic) noise, but has no effect on the fertility and life span of D. melanogaster<\/a><\/td> | 7. \u201cGenetics\/genomics, bioinformatics and systems biology of animals\u201d<\/td> | DMSO2, expression noise, genetic noise, MSM, phenotype<\/td>\n<\/tr>\n |
| Zhukova M., Yakovlev K., Shidlovskii Y.<\/td> | The use of CRSIPR\/Cas9 scarless system to generate Drosophila melanogaster line with point mutations<\/a><\/td> | 7. \u201cGenetics\/genomics, bioinformatics and systems biology of animals\u201d<\/td> | CRSIPR\/Cas9, Drosophila, genetic engineering, site-directed mutagenesis<\/td>\n<\/tr>\n |
| Velichko A.A.*, Milovidov G. D., Deryusheva E.I., Shevelyova M.P., Nemashkalov, V. A.<\/td> | In silico search and analysis of mutant forms of increased thermostability of the industrially important enzyme savinase from Bacillus lentus by optimizing its surface charge-charge interactions<\/a><\/td> | 8. \u201cMicrobiology & Biotechnologies: computational and experimental approaches\u201d<\/td> | AlphaFold, electrostatic interactions, savinase, thermal stability, TKSA-MC<\/td>\n<\/tr>\n |
| Loginov V.I., Burdennyy A.M., Pronina I.V., Lukina S.S., Braga E.A.<\/td> | Modern assessment of ncRNAs as biomarkers of prognosis in clear cell renal cell carcinoma<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | ccRCC, clear cell renal cell carcinoma, lncRNA, miRNA, mRNA<\/td>\n<\/tr>\n |
| Burdennyy A.M., Loginov V.I., Pronina I.V., Braga E.A.<\/td> | Competing endogenous RNAs as non-small cell lung cancer markers<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | long non coding RNA, lung adenocarcinoma, squamous cell carcinoma, tumor markers<\/td>\n<\/tr>\n |
| Polyakova E.1, Beresten S., Belevtsev M.<\/td> | Assessment of the effect of reproductive pathology on the number of TREC\/KREC copies in the dry blood spot in premature newborns<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | premature newborns, real-time PCR, reproductive pathology, TREC\/KREC<\/td>\n<\/tr>\n |
| Lykov A., Gevorgiz R., Zheleznova S., Uvarov I., Poveshchenko O.<\/td> | Mesenchymal stem cell proliferation and NO production under cyanobacteria and microalgae condition<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | cyanobacteria, mesenchymal stem cells, microalgae, NO, proliferation<\/td>\n<\/tr>\n |
| Bykov Yu.V., Bykova A.Yu., Bekker R.A.<\/td> | On the role of genetic testing in finding the root cause of the treatment resistance of major depressive disorder (A Clinical Case)<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | adenosine deaminase, methylenetetrahydrofolate reductase, P450 cytochromes, treatment resistant depression<\/td>\n<\/tr>\n |
| Sinitskaya A., Kostyunin A., Asanov M., Poddubnyak A., Khutornaya M., Sinitsky M.<\/td> | Proteomic profiling of native heart valves affected by infective endocarditis<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | differentially expressed proteins, infective endocarditis, mass spectrometry, native heart valves, proteome<\/td>\n<\/tr>\n |
| Khvorykh G., Ageeva E.<\/td> | The genes and eQTLs to unravel the genetic underpinnings of skin wrinkling and sagging<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | genes, single nucleotide polymorphism, skin aging<\/td>\n<\/tr>\n |
| M.O. Shedenkova, D.I. Salikhova, A.A. Nekrasova, Z.V. Bakaeva, D.V. Goldshtein<\/td> | Extracellular vesicles of human glial progenitor cells exhibit neuroprotection by modulating the PI3K-Akt pathway in glutamate excitotoxicity<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | extracellular vesicles, glial progenitor cells, glutamate excitotoxicity, iPSCs, neuroprotection, proteome<\/td>\n<\/tr>\n |
| Asainov D., Kirillov B.<\/td> | Application of Interpretive Machine Learning methods to find transcriptomic biomarkers of Alzheimer's disease<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | Alzheimer\u2019s disease, biomarkers, machine learning, transcriptome<\/td>\n<\/tr>\n |
| Borkovskaya E., Vilinski-Mazur K., Kolomenskiy D., Kirillov B.<\/td> | Active machine learning for experimental planning for 3D printing of biocompatible scaffolds<\/a><\/td> | 9. \u201cBiomedicine, bioinformatics and systems computational biology\u201d<\/td> | 3D bioprinting, active learning, biocompatible scaffolds, machine learning<\/td>\n<\/tr>\n |
| ZalomaevaE.S., EgozovaE.S., MedvedevaA.V., ZhuravlevA.V., Nikitina \u0415.\u0410.<\/td> | The effect of changes limk1 gene expression in dopaminergic, serotonergic andfruitless neurons on learning and forgetting<\/a><\/td> | 10. \u201cCognitive science, neurogenetics, neuroinformatics and systems computational biology\u201d<\/td> | Drosophila melanogaster, forgetting, learning, limk1, memory<\/td>\n<\/tr>\n |
| Baltin S. M., Suspitsyna A. D., Shepelev N. M., Rubtsova M. P., Dontsova O. A<\/td> | Regulatory Functions of the Upstream Open Reading Frame in mRNA of the MCRS1 Human Gene<\/a><\/td> | 11. \u201cFundamental genetic\/cellular systems\/processes\u201d<\/td> | MCRS1, mRNA, small open reading frame, sORF, translation, uORF<\/td>\n<\/tr>\n |
| Petoukhov S.V.<\/td> | Binary-genomic numbers and symmetries of the universal rules of statistical organization of genomic DNAs<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | bioinformatics, dichotomies, fractal networks, genomic DNA, multi-level texts, probability rules, symmetries<\/td>\n<\/tr>\n |
| Buzanov G., Makeev V.<\/td> | Constructing an exclusive Gene Functional Annotation with the help of a Graph-Based MetaAnalysis<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | Enrichr database, functional annotation, gene clustering, gene functions, graph-based algorithm, OpenOrd<\/td>\n<\/tr>\n |
| Dedok V.<\/td> | Decentralized algorithm for nucleotide sequence alignment<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | parallel computing, sequence alignment, string similarity<\/td>\n<\/tr>\n |
| Golubyatnikov V.P. , Tatarinova E.A.<\/td> | Analytical and numerical modeling of a pluripotency gene network<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | equilibrium points, gene network models, positive and negative feedbacks, stability<\/td>\n<\/tr>\n |
| Bondareva A., Kirillov B.<\/td> | In silico modeling for biochemical processes of liver fibrosis based on physics-informed machine learning<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | PIML, liver fibrosis, mathematical modeling, direct biomarkers, machine learning, differential equations<\/td>\n<\/tr>\n |
| Seikin A., Kirillov B.<\/td> | Machine learning in predicting freezing of gait events using ultra-resource-constrained computational devices<\/a><\/td> | 12. \u201cMathematical problems of bioinformatics and systems computational biology\u201d<\/td> | movement disorders, neural networks, Parkinson\u2019s disease, tinyML<\/td>\n<\/tr>\n |
| Andreev Y. A., Pasyukova E. G.<\/td> | Impact of post-transcriptional regulation of expression of the gene encoding GSK3 protein kinase in the control of tissue homeostasis and survival in Drosophila melanogaster<\/a><\/td> | 13. \u201cGenetics and systems biology of aging\u201d<\/td> | adipose tissue, Drosophila melanogaster, GSK3, Homeostasis, survival<\/td>\n<\/tr>\n |
| Gorbunova A.A., Shaposhnikov M.V., Babak T.V., Koval L.A., Moskalev A.A.<\/td> | Effect of deletions of the CBS, CSE and MST hydrogen sulfide biosynthesis genes under restricted intake of sulfur-containing amino acids (methionine and cysteine) on the viability of Drosophila melanogaster<\/a><\/td> | 13. \u201cGenetics and systems biology of aging\u201d<\/td> | 3-mercaptopyruvate sulfurtransferase, cystathionine-\u03b2-synthase, cystathionine-\u03b3-lyase, cysteine, deletions, hydrogen sulfide, methionine<\/td>\n<\/tr>\n |
| Bykov Yu.V., Bykova A.Yu., Bekker R.A.<\/td> | Using the knowledge of system and molecular biology in the development of individualized program to slow down the Alzheimer Disease process (A Clinical Case Presentation)<\/a><\/td> | 13. \u201cGenetics and systems biology of aging\u201d<\/td> | Alzheimer Disease, cyclooxygenase type 2, glycogen synthase kinase 3-beta, histone deacetylase, sirtuin-1<\/td>\n<\/tr>\n |
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