Mohammed El-Kebir

Dr. Mohammed El-Kebir, University of Illinois at Urbana-Champaign, US.

"Algorithms for Cancer Phylogenetics"

Cancer is a genetic disease, where cell division, mutation and selection produce a heterogeneous tumor composed of multiple subpopulations of cells with different sets of mutations. During later stages of cancer progression, cancerous cells from the primary tumor migrate and seed metastases at distant anatomical sites. The cell division and mutation history of an individual tumor can be represented by a phylogenetic tree, which helps guide patient-specific treatments. In this talk, I will introduce combinatorial algorithms for reconstructing, comparing and interpreting tumor phylogenies from DNA sequencing data. Specifically, I will discuss the challenges that arise when analyzing bulk and single-cell DNA sequencing samples of tumors. I will also discuss how to summarize diverse solution space of tumor phylogenies. Finally, I will demonstrate how inferred tumor phylogenies can be used in downstream analyses to obtain additional insights about tumorigenesis.

Biography

El-Kebir received his PhD in Computer Science at VU University Amsterdam and Centrum Wiskunde & Informatica (2015) under the direction of Jaap Heringa and Gunnar Klau. He did postdoctoral training with Ben Raphael at Brown University and Princeton University (2014-2017). In 2018, he joined the University of Illinois at Urbana-Champaign as an Assistant Professor of Computer Science. El-Kebir has affiliate faculty appointments in Electrical and Computer Engineering, the Institute of Genomic Biology and the National Center for Supercomputing Applications. He received the National Science Foundation CISE Research Initiation Initiative (CRII) Award in 2019 and the CAREER Award in 2021.

El-Kebir's main research is in combinatorial optimization algorithms for problems in computational biology, with a particular focus on cancer genomics. Among his major contributions are advances in the theoretical foundations of cancer phylogenetics (e.g., hardness proofs for phylogeny estimation problems from mixture data), methods for the estimation of cancer phylogenies from sequencing data of tumors, and new mathematical models for studying cancer evolution and metastasis.

El-Kebir's current focus is on developing methods that enable the estimation of cancer phylogenies from single-cell sequencing data. A specific challenge he is addressing is the integration of data obtained from the same tumor using distinct single-cell technologies. Another focus is the development of comprehensive evolutionary models for somatic mutations that occur at varying genomic scales. More generally, El-Kebir is developing novel problem statements and corresponding methods to analyze omics data in novel ways, thereby improving scientific discovery.