Dr Weini Huang
Lecturer in Mathematical Biology
Email: email@example.comTelephone: +44 (0)20 7882 2965Room Number: Mathematical Sciences Building, Room: MB-116
Weini Huang currently works on modelling cancer evolution and species interactions in biological systems. Before being a lecturer in mathematical biology in 2018, she worked as a postdoctoral researcher in the Barts Cancer Institute, QMUL for modelling cancer spatial heterogeneity and the evolution of tumour resistance. She obtained her PhD in 2012 in Evolutionary Theory in the Max Planck Institute of Evolutionary Biology in north Germany.
Currently, Weini supervises four PhD students in the topics of evolutionary theory with application in species coevolution and cancer dynamics. Weini is interested in understanding how diversity and population patterns are formed and maintained in nature/human cell populations through theoretical approaches as well as their connections with experimental/clinical observations. She collaborates with experimental evolution groups and cancer biologists/clinicians, such as the evolution of trade-offs in a bacteria-ciliate system and drug resistance in ovarian cancer, ecDNA copy number distribution, mtDNA dynamcis in healthy liver.
Differential Equations (2017-2018,2019-2020, 2020-2021)
Complex Systems (2018-2019)
(2020). Measuring single cell divisions in human tissues from multi-region sequencing data Nature Communications.
(2019). The feedback between selection and demography shapes genomic diversity during coevolution Science Advances.
(2019). Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data PLoS Computational Biology.
(2019). Population size changes and extinction risk of populations driven by mutant interactors. Phys Rev E.
(2018). Life History Trade-Offs in Tumors. Curr Pathobiol Rep.
(2017). Dynamical trade-offs arise from antagonistic coevolution and decrease intraspecific diversity. Nat Commun.
(2017). Robust RNA-based in situ mutation detection delineates colorectal cancer subclonal evolution. Nat Commun.
(2016). Competing metabolic strategies in a multilevel selection model Royal Society Open Science.
(2016). A resource-based game theoretical approach for the paradox of the plankton. PeerJ.
(2015). Stochastic game dynamics under demographic fluctuations Proceedings of the National Academy of Sciences.
(2015). Learning process in public goods games Physica A: Statistical Mechanics and its Applications.
Marius Moeller (2018.05 - Modeling genetic mutations in constant populations)
Magnus Haughey (2018.11 - Modeling spatial dynamics in cancer evolution)
Magnus' research interests include spatial simulations, stochastic modelling, statistical physics and analysis of large-scale genomic data. He is currently developing computational and mathematical methods to analyse spatially resolved tumour tissue samples, to understand the early-stage development of tumours and how dynamical properties of different tumour cells are reflected in their spatial arrangement. Before joining Queen Mary, Magnus earned his Master’s degree in theoretical physics from The University of Edinburgh where he worked on a stochastic model of chemotherapy response the brain tumours. During his undergraduate studies he also completed particle physics research projects at DESY, Hamburg, and Fermilab, Chicago.
Iftikhar Ahmed (2020.01- Modeling genetic diversity in hierarchical structured human tissues)
Iftikhar is from Pakistan and holds an MPhil degree in Mathematics from Abdul Wali Khan University, Mardan, Pakistan. His area of research was Fluid Dynamics and worked on a thesis titled “Some Exact Solutions of the Boundary Layer Equation and Electro-magnetic Body Force”. Studying the infectious disease models has developed his interest in Mathematical Biology. Now he is working on the deterministic model and stochastic simulation of tumor accumulation in hierarchically organized tissue structures.
Elisa Scanu (2020.09 - Modeling ecDNA dynamcis in human cells)
Before joining Queen Mary University, Elisa studied in “La Sapienza” University of Rome, Italy, where she graduated (both BSc and MSc) in Applied Mathematics with the highest marks, and she spent a semester in Queen Mary University as an Erasmus+ student. She is also working in the Biology Department of “Tuscia” University of Viterbo, Italy, as a contract teacher for a 1st year module in Mathematics.
Elisa’s PhD project is to use stochastic models based on simple mechanistic assumptions to explain observed ecDNA patterns in human cells, including healthy tissues and tumours. The project will be done in cooperation with Barts Cancer Institute at the School of Medicine and Dentistry in Queen Mary University of London.