Computational biology and bioinformatics
Bioinformatics and Molecular Biology of Systems
Departamento de Bioquímica (UAM)
DESCRIPTION OF THE OFFER
We are developing and comparing methods to understand possible restrictions in the order of accumulation of driver mutations in cancer, using oncogenetic trees and related approaches, and evaluating them using explicit evolutionary models of tumor progression.
The basic workflow in this work involves implementing/extending simulations of evolutionary models of cancer progression and evaluating the performance of methods to reconstruct the restrictions, as well as understanding the consequences that different evolutionary models can have for different methods of inference and for the types and patterns of data we observe. This project is therefore completely computational and statistical.
Your work would involve analyzing data (mainly data simulated by us from known evolutionary models and fitness landscapes, but possibly also "real" data from the literature) and/or implementing and studying the consequences of different evolutionary models of tumor progression.
Diaz-Uriarte, R, Vasallo Vega, C. 2018. Every which way? On predicting
tumor evolution using cancer progression models. bioRxiv (<https://www.biorxiv.org/content/early/2018/07/30/371039>)
Diaz-Uriarte, R. 2018. Cancer Progression Models And Fitness Landscapes: A
Many-To-Many Relationship. Bioinformatics (<https://doi.org/10.1093/bioinformatics/btx663>)
Diaz-Uriarte, R. 2017. OncoSimulR: genetic simulation with arbitrary
epistasis and mutator genes in asexual populations. Bioinformatics.
OncoSimul package repository: https://github.com/rdiaz02/OncoSimul
Biomolecules & Cell D.