Hello, I’m
Yunchen Xiao
Postdoctoral Research Associate
Blizard Institute, Queen Mary University of London
About Me
Yunchen was born in Shenyang, China, on 02/10/1996.
After finishing his primary school in Shenyang, he moved to Guangzhou when he was 12 and spent his junior high school there.
He then moved to Paris and got his IB diploma in Ecole Jeannine Manuel.
After spending 3 years in Paris, he chose Scotland as his next destination, he graduated from the University of St Andrews in 2018, with a first-class MMaths (Fast Track) degree. Then he completed his PhD under the supervision of Mark Chaplain and Len Thomas in July 2022, with a thesis titled "Application of likelihood-free inference methods on numerical models of cancer invasion". During his PhD, Yunchen developed several likelihood-free statistical algorithms related to Approximate Bayesian Computation and gradient matching, which serve the aim of estimating parameters within mathematical models of cancer invasion and metastasis.
Starting from Sept 2022, Yunchen joined Marino Lab at Blizard Institute, Queen Mary University of London as a Postdoctoral Research Associate. His research focuses on investigating and comparing glioblastoma initiating cells (GIC) and neural stem cells (NSC) from the perspective of bioinformatics to shed more light on personalized drug discovery.
​
The story of Yunchen continues...
Research Highlights
Mathematical modelling of pneumonic plague in 1413. (Funded summer internship, 2017)
​
Under the supervision of Dr. Tommaso Lorenzi, I modelled the pneumonic plague across Europe in 1348 (related to the famous "Black Death") with a modified SIR model.
​
​
​
​
​
​
​
​
​
​
​
​
​
​
​
Mathematical modelling of cell dynamics in Acute Myeloid Leukemia (Master dissertation, 2018)
​
Based on the paper "Mathematical modelling of Leukemogenesis and Cancer Stem Cells Dynamics" written by Dr. Thomas Stiehl and Professor Anna Marciniak-Czochra, and under the supervision of Dr. Tommaso Lorenzi, I modelled the cell dynamics (both hematopoietic cells and leukemic cells) in Acute Myeloid Leukemia (AML) as my master dissertation.
​
​
​
​
​
​
​
​
​
​
​
​
​
​
Cancer invasion & Approximating Bayesian Computation (2019 -
​
With the great help of my PhD supervisors, I developed two algorithms that can estimate the underlying parameter values within a Partial Differential Equation (PDE) model about cancer invasion, based on the in silico data generated from the model. One is related to ABC and the other one is based on gradient matching. Both schemes demonstrated satisfactory results on 1D density data. We consider this as a novel statistical approach to estimate parameter values within a complex numerical model based on observed data. The detailed results and analysis can be found in our paper published on Royal Society Open Science.
​
​
​
​
​
​
​
​
​
​
Now we are aiming to extend our study to 2D data observed in organotypic cultures, under the new Individual-Based Modelling(IBM) framework which allows us to track the movements of individual cells and reproduce the cancer invasion patterns on a quantitative basis.
​
​
Teaching
2018-2019
​
Semester 1:
​
MT1002 Mathematics (Tutorials + Maple computing sessions)
​
Semester 2:
​
MT2508 Statistical Inference (Tutorials + R computing sessions)
​
2019-2020
​
Semester 1:
​
MT1002 Mathematics (Tutorials)
​
Semester 2:
​
MT2508 Statistical Inference (Tutorials)
​
2020-2021
​
Semester 2:
​
MT2508 Statistical Inference (Tutorials)
Publication
1. Xiao Y, Thomas L, Chaplain MAJ. 2021 Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching. R. Soc. Open Sci. 8: 202237. https://doi.org/10.1098/rsos.202237
​
2. Millner TO, Panday P, Xiao Y, Boot JR, Nicholson J, Arpe Z, Stevens P, Rahman N, Zhang X, Mein C, Kitchen N, McEvoy AW, McKintosh E, McKenna G, Paraskevopoulos D, Lewis R, Badodi S, Marino S. 2024 The inflammatory micro-environment induced by targeted CNS radiotherapy is underpinned by disruption of DNA methylation. https://doi.org/10.1101/2024.03.04.581366 (preprint)