Description
We are dedicated to applying computational tools on single-cell and other omic-based sequencing data to answer questions regarding the biology of female reproductive organs. Our main aim is to model different biological processes (such as regeneration, niche interactions, or tumor formation) to contribute to an understanding of the uterus under healthy and pathological conditions.
The team builds project-tailored pipelines and uses machine learning algorithms to solve questions such as the description of new cell populations and cell states, identification of changes in cell abundances, determination of differentiation trajectories and progenitor populations, characterization of cell-to-cell molecular networks, the discovery of disease biomarkers for early onset prediction, screening of new drug targets, or potential repurposing of pre-existing drugs.
Description
We are dedicated to applying computational tools on single-cell and other omic-based sequencing data to answer questions regarding the biology of female reproductive organs. Our main aim is to model different biological processes (such as regeneration, niche interactions, or tumor formation) to contribute to an understanding of the uterus under healthy and pathological conditions.
The team builds project-tailored pipelines and uses machine learning algorithms to solve questions such as the description of new cell populations and cell states, identification of changes in cell abundances, determination of differentiation trajectories and progenitor populations, characterization of cell-to-cell molecular networks, the discovery of disease biomarkers for early onset prediction, screening of new drug targets, or potential repurposing of pre-existing drugs.
Team members
Petr Volkov is the Director of BioIT at the Carlos Simon Foundation, where he leads a computational biology team that supports the bioinformatics, biostatistics, and data science work within the foundation.
A computer scientist by training, Petr received his doctorate in bioinformatics from Lund University’s Medical Faculty in 2016, where he studied the function of epigenetics in Type 2 Diabetes. After completing his doctorate, he served as Head of the LUDC Bioinformatics Unit from 2017-2019 and as Director of Bioinformatics at AstraZeneca, where he led a computational biology team in drug discovery.
Petr’s primary research interests lie in high-performance computing, epigenetics, omics technologies, mathematical statistics, and machine learning. He has achieved an H-index of 27, directly or indirectly contributing bioinformatics and biostatistical analysis to more than 36 research papers.
Passionate about optimizing bioinformatics and data science pipelines, Petr aims to enable the creativity of individual scientists and bioinformaticians to push the boundaries of molecular biology, genetics, and medical science. He also enjoys establishing and developing high-performance computational biology teams.
ResearchID: A-3971-2014
Diego Amoros, Ph.D.
Senior Bioinformatician
Sören R Stahlschmidt, Ph.D.
Senior Computational Biologist
François Serra
Senior Bioinformatician
Yolanda Castello
Bioinformatician
Jaime Llera
Bioinformatician