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Системная биология и персонализированная медицина

2019/2020
Учебный год
RUS
Обучение ведется на русском языке
7
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль

Преподаватель

Программа дисциплины

Аннотация

This course provides an introduction to systems biology with an emphasis on analysis of -omics data and applications in medicine. Accent is made on analysis of expression data, heavy usage of graphs and networks and other methods to enhance routine statistical analysis with biological knowledge. Special lecture is devoted to introduction to oncogenomics. This course is designed for students with strong backgrounds in either molecular biology or computer science, but not necessarily both. The open R+Bioconductor programming environment — which is widely employed for bioinformatics and computational biology — is used to illustrate the applications.
Цель освоения дисциплины

Цель освоения дисциплины

  • The goal of this course is to give students an overview of various modern methods of systems biology and analysis of omics data with an emphasis on application in medicine.
Планируемые результаты обучения

Планируемые результаты обучения

  • Describe the main principles and the classes of problems that system biology can be applied to.
  • Understand principles of modeling of biological processes at various levels
  • Identify the main types of biological network models used in systems biology.
  • Use publicly available resources that are relevant to a given biological application domain.
  • Generate biologically meaningful hypotheses based on the analysis of high-throughput omics data.
  • Conduct a high-level system-biological analysis of various types of omics data
  • Explain how systems approaches can be used for purposes of personalized medicine.
Содержание учебной дисциплины

Содержание учебной дисциплины

  • Definition and main problems of system biology.
    Properties of biological systems. Omics data and omics technologies, including proteomics and metabolomics. GWAS analysis. The main repositories of omics data. Common software tools system biology. Reproducibility problem.
  • Modeling of biological and biochemical systems.
    Classification of models. Simulation based on differential equations: the law of effective masses, Michaelis-Menten kinetics. Compartmental models and pharmacokinetics. Parameter problem. Boolean networks, analysis of metabolic fluxes, Petri nets. Agent modeling approach. Description of models: SBML, BioPAX, SBGN. Software tools: CellDesigner, COPASI.
  • System-biological analysis of transcriptome data.
    A simple overrepresentation test. GSEA method. Gene Ontology Database, MSigDB. Online resources for enrichment analysis (DAVID, WebGestalt, G: profiler). R packages: clusterProfiler, Enrichment-Browser. The peculiarities of Gene Ontology. Visualization of the enrichment results (Rev-iGO). GSEA modifications: GSVA, GAGE, STEPath algorithms. Adaptation for RNASeq data.
  • Problems arising in the analysis of omics data.
    Principal Componene Analysis and Independent Component Analysis to overcome the “curse of dimensionality”. Missing data. Outliers. Batch effects identification and removal (ComBat algorithm). Meta-analysis and integration of omics data (PARADIGM, iCluster, MixOmics, MINT, YuGene). Analysis of prioritized gene lists (RankProd). Visualizing omics data.
  • Biological networks and their properties.
    Typical network analysis problems. Signalling and metabolic pathways. Databases: KEGG, Reactome, PathwayCommons. Application of text-mining analysis to pathway creation. Software for pathway analysis. Cytoscape and its plugins (ClueGO, GeneMANIA, ReactomeFIViz).
  • The use of networks for the analysis of omics data.
    Accounting for topology in the analysis of overrepresentation: SPIA, DEAP algorithms. Search for gene regulators: iRegulon, SNEA, master regulators. Subnetwork identification: jActiveModules, BioNet. Patient subtyping and identification of driver subnetworks. Prioritizing a list of genes. Identification of complexes in proteomics.
  • Introduction to oncogenomics.
    Oncogenes and tumor suppressors. Cancer genome. TCGA resource. Identification of novel cancer-associated genes. Identification of molecular tumor subtypes. Individual selection of optimal therapy. Clinically significant mutations. Mutation filtering and prioritization. The overall scheme of analysis of cancer genomes.
  • Systems medicine.
    Predicting the risk of developing diseases according to omics data: RiskOGram, POGO. Metagenomic data and their use in medicine. Omics data and aging. Epigenetic clock. Microbiome analysis. iPOP.
Элементы контроля

Элементы контроля

  • неблокирующий Homework
  • неблокирующий Exam
Промежуточная аттестация

Промежуточная аттестация

  • Промежуточная аттестация (2 модуль)
    0.3 * Exam + 0.7 * Homework
Список литературы

Список литературы

Рекомендуемая основная литература

  • Personalized Medicine ; Персонализированная медицина. (2019). https://doi.org/10.15690/vramn74161-70-1073

Рекомендуемая дополнительная литература

  • B. Kobrinskii A., & Б. Кобринский А. (2017). Personalized medicine: genome, e-health and intelligent systems. Part 1. Genomics and monitoring of clinical data ; Персонализированная медицина: геном, электронное здравоохранение и интеллектуальные системы. Часть 1. Геномика и мониторинг клинических данных. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.5670366E
  • Personalized Medicine ; Персонализированная медицина. (2019). https://doi.org/10.4103/0970-1591.91438.