Speaker
Description
Metabolic reprogramming is a central hallmark of cancer, enabling tumor cells to sustain rapid proliferation and adapt to fluctuating nutrient availability and microenvironmental stress. In particular, the coupled dynamics of glucose consumption and lactate production provide insight into tumor metabolic phenotypes such as the Warburg effect and metabolic switching between glycolytic and oxidative states. Mathematical models have been widely used to study these processes; however, linking experimental metabolite measurements with mechanistic models of tumor metabolism remains challenging due to limited observability, measurement noise, and uncertainty in model parameters. In this work, we present a hybrid experimental–computational framework for identifying tumor metabolic dynamics by integrating multiplexed microfluidic biosensing with physics-informed neural networks (PINNs). We developed a microfluidic bead-based aptamer sensing platform capable of quantifying glucose and lactate concentrations in cancer cell culture media, enabling time-resolved monitoring of metabolic changes during cell proliferation. Experimental data are integrated with a differential-equation model of tumor glucose–lactate metabolism using PINNs, enabling inference of metabolic parameters and reconstruction of metabolic trajectories. Application to glioblastoma and prostate cancer cell lines reveals distinct metabolic behaviors consistent with glycolytic and hybrid phenotypes.
