Computational biology and bioinformatics

We work on developing and comparing methods to understand restrictions in the order of accumulation of driver mutations in cancer, using oncogenetic trees and related approaches, and how they can be used to predict tumor evolution. We evaluate these methods using explicit evolutionary models of tumor progression, and we have also started focusing on how they can be used to design adaptive therapies, as well as how interpretation is affected by frequency dependent fitness.

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.

References:

Diaz-Colunga, J, Diaz-Uriarte, R. 2020. Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?. bioRxiv preprint <https://www.biorxiv.org/content/10.1101/2020.12.16.423099v2>.

Diaz-Uriarte, R, Vasallo, C. 2019. Every which way? On predicting tumor evolution using cancer progression models. PLoS Comp Biol (<https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007246>)

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.  <https://academic.oup.com/bioinformatics/article/2982052/OncoSimulR> (OncoSimul package repository: https://github.com/rdiaz02/OncoSimul)