MS04 - ONCO-01

Data-informed mathematical modeling in cancer and development

Tuesday, July 15 at 3:50pm

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Changhan He (University of California, Irvine), Chengyue Wu, University of Texas MD Anderson Cancer Center

Description:

Understanding and representing the complex dynamics of cancer progression and developmental processes is critical for advancing our knowledge of tumor growth, tissue organization, and treatment response. Data-informed mathematical modeling, integrating frameworks such as differential equations, image-processing techniques, and advanced computational methods, provides powerful tools to capture these intricate biological, chemical, and physical interactions. This mini-symposium aims to unite experts and researchers working on innovative mathematical models and computational approaches, leveraging clinical and experimental data for validation. By bridging mathematical modeling with real-world data, we seek to deepen our understanding of both cancer and developmental biology, fostering collaboration to address key challenges in these interconnected fields.

Lifeng Han

Tulane University

"Calibrate a phenotype-structured population model with cell viability data to study drug resistance in cancer treatment"

We fit a phenotype-structured population mode to cell viability data on a drug used for ovarian cancer, olaparib. This approach reveals the effects of a drug on fitness landscape and the evolution of a population of cancer cells structured with a spectrum of drug resistance. We will show that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. We will discuss the potential use of this model to design drug treatment regimens to improve cancer treatment.

Wenjun Zhao

Wake Forest University

"Dynamical GRN inference via optimal transport"

In this talk, we present a framework for inferring gene regulatory networks from time-stamped single-cell gene expression data. Our algorithm first reconstructs the correspondence between single cells across time points through optimal transport, and then infers the underlying dynamical model governing gene expression dynamics, which can be interpreted in terms of differential equations. We will also discuss extensions of this framework to infer context-specific regulatory mechanisms.

Qixuan Wang

University of California, Riverside

"Hair follicle cell fate regulations and the effect on the follicle growth control"

In tissues, proper regulations of cell fate decisions are important in maintaining the shape and functions of the tissue. In this talk, I will present our recent research in modeling cell fate regulation mechanisms, using mammalian hair follicles as a model system. Hair follicles are mini skin organs, and they are highly dynamic in the way that they can undergo cyclic growth during the lifespan of the organism. To maintain tissue homeostasis and functions of a hair follicle, the follicle transient amplifying epithelial cells need to make correct decisions among cell division, differentiation and apoptosis, instructed by various signals produced by the follicle itself as well as by the surrounding skin environment. We develop a probabilistic Boolean model based on both literature and published single-cell RNA sequencing data. Using both computational simulations and attractor analysis, we investigate how hair follicle epithelial cells respond to TGF-beta, BMP and TNF, so to make the correct cell fate decision, and how signals cooperatively regulate hair follicle growth dynamics. Next, we develop a hybrid multiscale model on the bottom part of a HF, and use it to investigate how signaling dynamics, cellular kinetics and movement, and gene regulation coordinate to regulate the HF cell fate decisions in the tissue.

Axel Almet

University of California, Irvine

"Systems modeling of cellular senescence using single-cell transcriptomics"

Cellular senescence is a stress-induced cell state characterised by irreversible cell cycle arrest and an enhanced secretome where senescent cells secrete an array of inflammatory signals and remodeling factors. An accumulation of senescent cells across biological age is hypothesised to contribute to aging-related declines in tissue. However, there are contexts where senescent cells may be transiently beneficial. To study the impact of cellular senescence requires a systems approach that analyzes both its intrinsic features, driven by changes in gene transcription dynamics, and its extrinsic impacts on the tissue environment driven by cell-cell communication. Single-cell transcriptomics, which profile molecular states with broad gene coverage and cell type coverage, provide an exciting opportunity to generate further insight into the multi-dimensional features of cellular senescence. In this talk, we present our recent work on using single-cell transcriptomics to dissect the multi-dimensional features of cellular senescence. First, we fit stochastic models of gene transcription to ground truth single-cell RNA-sequencing datasets of cellular senescence to dissect how cellular senescence drives intrinsic changes in gene transcription kinetics. We then shift our analysis to the perspective of cell-cell communication, identifying senescence-driven communication modules that can reveal novel senescent cell states with distinct communication patterns and, in turn, show how these communication patterns are associated with distinct transcriptional features. Overall, these analyses reveal new ways that we can integrate sequencing data and mathematical modeling to better understand cellular senescence.

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Annual Meeting for the Society for Mathematical Biology, 2025.