APBioNET Talks: Dynamic modeling of chromosomal instability in somatic genomes


Chromosomal instability (CIN), a constantly high frequency of chromosome segregation errors during cell divisions, is a major form of genome instability and plays an import role in intra-tumour heterogeneity, metastasis, and therapy resistance. CIN often leads to structural or numerical chromosomal alterations, such as structural variants and copy number alterations. Linking these alterations detected from cancer genomics data with stochastic modelling and Bayesian inference provides a powerful approach to quantify CIN in an evolutionary context, which helps to better understand cancer evolution and inform cancer treatment. In this talk, I will share our work on modelling experimental and real data with this approach.

Speaker Profile:

“I am currently a Surrey Future Fellow, at Section of Systems Biology and Surrey Institute for People-Centred AI, University of Surrey.
Before joining Surrey, I was a Postdoc at Department of Cell and Developmental Biology University College London, where I worked on dynamical modelling of chromosomal instability (CIN) in cancer genomes. Previously, I was a Postdoctoral Fellow at Genome Institute of Singapore, where I mainly developed pipelines and methods to analyse tumour heterogeneity and clonal evolution in liver and lung cancer genomes. I completed my PhD in Computational Biology at School of Computing National University of Singapore, where I developed machine learning and phylogenetic methods for problems related to lateral gene transfer. I obtained my Master’s and Bachelor’s degree from Software Engineering Institute East China Normal University, where I led the development of platforms for high-throughput biological data analysis, including RNA-Seq and proteomic data.

My research is in the broad field of computational biology, which bridges software engineering, machine learning, algorithms, statistics, phylogenetics, population genetics, and omics. I am particularly interested in developing new computational methods and models to address important biological problems related to human health. My goal is to facilitate the mining of new knowledge from the accumulating huge amounts of data for the biological and biomedical community. I have developed several new methods and applied available methods to tackle basic questions arising in the study of species and cancer evolution. My current primary interests are evolutionary dynamics of cancer genomes, especially those driven by CIN, which are still less well studied than point mutations but critical in tumorigenesis and patient treatment.
” https://icelu.github.io