philippkhaitovich

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Philipp Khaitovich

Professor
Center for Data-Intensive Biomedicine and Biotechnology

Philipp Khaitovich, born in Moscow, Russia in 1973.

Philipp completed undergraduate studies in molecular biology at Moscow State University in 1995 and PhD in biochemistry at the University of Illinois at Chicago in 1999.

From 2000 till 2006 Philipp worked as a postdoctoral researcher at the Max Planck Institute for Evolutionary Anthropology at the department of Evolutionary Genetics headed by Prof. Svante Pääbo.

In September 2006, Philipp took a faculty position at the Institute for Computational Biology jointly established by the Chinese Academy of Sciences and the Max Planck Society in Shanghai China. In 2012 Philipp was promoted to an institute director position.

From 2012, Philipp is a fellow member of Max Planck Society of Germany, as well as adjunct Professor at ShanghaiTech University in Shanghai, China.

Philipp joined Skoltech as a Full Professor in April 2014.

As humans, we are always interested to know how exactly our species came to exist. Human evolution has resulted in a species that possesses an apparently unique set of phenotypic capabilities. Newly evolved human-specific features may also represent vulnerable points in otherwise well buffered functional networks, resulting in uniquely human disease susceptibilities.

In our laboratory, we search for molecular features specific to humans, through integrative analysis of genetic, transcriptomic and metabolomic data measured in modern and archaic humans, as well as closely related mammalian species: chimpanzees, macaques and mice. Following this approach, we have identified several molecular mechanisms that potentially underlie the evolution of the human phenotype features including exceptional cognitive abilities, as well as outstanding longevity.

  • the Friendship medal (China)
ningfu
Ning Fu
Research Scientist
ekaterinakhrameeva
Ekaterina Khrameeva
Research Scientist
pavelmazin
Pavel Mazin
Junior Research Scientist
Bioinformatics Lab Course 1

Number of ECTS credits: 6
Course Classification: Science, Technology, and Engineering

Course description:
The course will introduce students to the hands-on practical analysis of novel biological “omics” data with a specific focus on the state-of-the-art analysis of the genome, epigenome and transcriptome. The course will integrate various types of omics data generated by new generation sequencing technologies and will include the following parts: genome assembly, genotyping and GWAS analyses in the genome section; histone modifications, DNA methylation and 3D chromatin organization in the epigenetics section; transcriptome assembly, splicing analysis, RNA editing, differential transcription, non-coding RNA analysis and functional characterization in transcriptome section. In addition the course will include introductions into biostatistics and use of R-environment and online analysis tools.
The course will include practical data analysis work conducted by student in front of computer, but also introductory lectures into principles of data analysis and basic elements of statistical analysis of large-scale biological data.
At the end of the course, students would be expected to accomplish an independent data analysis project on a model dataset including several heterogeneous types of biological “omics” data.

Prerequisites:
Knowledge of basic statistics and basic molecular biology. The course is recommended for students specialized in large-scale biological data analysis (bioinformatics / computational biology / theoretical biology). The course is recommended to be taken in addition to the theoretical bioinformatics and biostatistics courses.

Bioinformatics Lab Course 2

Number of ECTS credits: 6
Course Classification: Science, Technology, and Engineering

Course description:
The course will introduce students to the hands-on practical analysis of novel biological “omics” data with a specific focus on the state-of-the-art analysis of the proteome, metabolome and lipidome. The course will integrate various types of omics data generated by mass spectrometry based approaches, as well as axillary data new generation sequencing technologies.
The course will include the following parts: general principles of proteomics analysis, semi-quantitative and quantitative proteomics, posttranslational modifications analysis and proteome database assembly in the proteomics section; general principles of metabolomics/lipidomics analysis, metabolite/lipid detection, quantification and annotation, metabolome/lipidome pathway analysis, systems-level analysis in the metabolomics and lipidomics sections.
The course will include practical data analysis work conducted by student in front of computer, but also introductory lectures into principles of mass spectrometry based data analysis, proteome, metabolome and lipidome organization, as well as current tools available for data analysis in these fields.
At the end of the course, students would be expected to accomplish an independent data analysis project on a model dataset including several heterogeneous types of biological “omics” data.

Prerequisites:
Bionformatics Lab Course 1.
Knowledge of basic statistics and basic molecular biology. The course is recommended for students specialized in large-scale biological data analysis (bioinformatics / computational biology / theoretical biology). The course is recommended to be taken in addition to the theoretical bioinformatics and biostatistics courses.

RNA Biology

Number of ECTS credits: 3
Course Classification: Science, Technology, and Engineering

Course description:
The course will introduce students to the current understanding of the RNA types, their biological functions, their interplay and connection to other types of biological molecules, as well as introduce novel laboratory and analytical methods to investigate them.
Recent advances in new generation sequencing technology resulted in substantial broadening of the RNA universe, as well as resulted in discovery of novel RNA functions and entirely novel RNA types. This course is aimed to introduce students to the current state of knowledge with regard to RNA biology, with particular focus on human, and its place in the framework of basic biological processes.

Prerequisites:
Knowledge of basic molecular biology. The course is recommended for students specialized in molecular biology, biomedicine and transcriptome data analysis. The course is recommended to be taken in addition to the bioinformatics lab 1 course.

 

ФИО: Хайтович Филипп Ефимович

Занимаемая должность (должности): Профессор

Преподаваемые дисциплины: –

Ученая степень: Ph.D., Университет Иллинойса в Чикаго, США, 1999

Ученое звание (при наличии): нет

Наименование направления подготовки и/или специальности: Биохимия

Данные о повышении квалификации и/или профессиональной переподготовке (при наличии): нет

Общий стаж работы: 15 лет

Стаж работы по специальности: 15 лет