Quantitative biology of cancer metabolism
We are a research group interested in uncovering quantitative principles of metabolism, with a particular focus on economics, thermodynamics, and control of metabolic networks. We are currently a part of the School of Life Sciences at Southern University of Science and Technology (SUSTech), Shenzhen, China.
Our research focuses on uncovering economic, thermodynamic, and control principles underlying metabolic networks. Living cells operate under stringent constraints of energy, material availability, and enzyme resources, requiring metabolic networks to allocate limited resources efficiently to sustain growth, maintenance, and adaptation. We aim to understand how such constraints give rise to universal design principles governing structure, dynamics, and regulation of metabolic networks.
We develop theoretical and computational frameworks to study metabolism as an optimization problem, where metabolic fluxes, enzyme investment, and metabolite concentrations are jointly optimized under thermodynamic and biophysical constraints. By formulating metabolism in terms of multi-objective and Pareto optimality principles, our work reveals how trade-offs between efficiency, robustness, and cost shape pathway organization, reaction thermodynamics, enzyme usage, and proteome composition across biological systems.
To connect theory with data, we integrate metabolic network modeling, control and optimization theory, and machine learning approaches. We develop data-driven models to infer thermodynamic properties of metabolic reactions and use them to test theoretical predictions at genome scale. Our research spans multiple biological contexts, including microbial systems, human metabolism, nutrition, and cancer, with the overarching goal of making metabolism interpretable and predictable.
Ziwei Dai received her Bachelor's degree in applied mathematics from Peking University in 2010, and PhD in physical chemistry from Peking University in 2016. She completed the dissertation on computational modeling of biological networks under the mentorship of Dr. Luhua Lai, and then moved to Duke University to join Dr. Jason Locasale's lab for postdoctoral training. See Ziwei's Curriculum Vitae for further information.
Trained as a mathematician, Ziwei has long been interested in unraveling the design principles of living cells using theoretical and computational approaches. She started her research from the metabolic reactions underlying human inflammatory responses, a pathway named arachidonic acid metabolism. Although she has spent four desperate and frustrating years on this project without any noteworthy publications, she learned, from all these failures, that the integration with data-driven approaches is of extreme importance in computational modeling of biological processes. In the meantime, she read lots of literature on the metabolic reprogramming of cancer cells, which established her interest in cancer metabolism and directed her to her later work on the origin of the Warburg Effect and multi-objective optimality in cancer metabolism, two studies combining mathematical models of cancer metabolism and multi-omics data analysis.
During her postdoctoral training in Jason Locasale's lab at Duke University, she further applied bottom-up mathematical models and data-driven statistical approaches to study the dysregulation of glucose and methionine metabolism in cancer cells. Her findings demonstrate how nutritional inputs affect tumor outcome through modulating metabolism and epigenetics, dissect the complexity in metabolic configurations of single cells in the tumor microenvironment, and offer a reductionist's view on the regulation of metabolic fluxes.
Ziwei likes stargazing, hiking (trails with stairs strongly disfavored), reading, and playing video games. She lives with her cat Snowball.
Email:daizw@sustech.edu.cn
Phone: 0755-88018549 (Ziwei's office)