MS06 - CDEV-1
Barbie Tootle Room (#3156) in The Ohio Union

Computational models for developmental and cell biology: A celebration of the works of Prof. Ching-Shan Chou

Thursday, July 20 at 10:30am

SMB2023 SMB2023 Follow Thursday during the "MS06" time block.
Room assignment: Barbie Tootle Room (#3156) in The Ohio Union.
Note: this minisymposia has multiple sessions. The other session is MS07-CDEV-1 (click here).

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Wing-Cheong Lo, Weitao Chen, Wenrui Hao, Leili Shahriyari


This session is organized in honor of the late Professor Ching-Shan Chou, who had performed very valuable research works on computational models for developmental and cell biology. Cell biology aims to study the structure, function, and development of cells. Since the muli-scale cell systems usually include complex regulation controls, computational modeling becomes an essential tool in predicting cell and tissue development under multilevel regulations. This mini-symposium will highlight recent computational approaches applied in cell and developmental biology. The research topics will include single-cell polarity, tissue pattern formation, and colony formation.

Han-Wei Shen

The Ohio State University (Computer Science and Engineering)
"Neural Network Assisted Visual Analysis of yeast simulation data"
In the field of simulation sciences, a popular and effective strategy to address the challenges of high computational and storage costs is to create a simpler statistical/mathematical surrogate, mimicking the original expensive simulation mode. The surrogate is then utilized to perform detailed analysis tasks instead of the expensive simulation model. In this talk, I will describe collaborative research with Prof. Chou in which we designed an interactive visual analysis framework, backed by a neural network-based surrogate model, to assist in analyzing and visualizing a complex yeast cell polarization simulation model. The model simulates the concentration of important protein molecules along the membrane of a yeast cell (single-cell microorganism) during its mating process. The simulation model comprises 35 uncalibrated input parameters and generates a 400-dimensional output. we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.

Yutong Sha & Qing Nie

University of California, Irvine (Department of Mathematics)
"Reconstructing transition dynamics from static single-cell genomic data"
Recently, single-cell transcriptomics has provided a powerful approach to investigate cellular properties in unprecedented resolution. However, given a small number of temporal snapshots of single-cell transcriptomics, how to connect them to obtain their collective dynamical information remains an unexplored area. One major challenge to connecting temporal snapshots is that cells measured at one temporal point may divide at the next temporal point, leading to growth and differentiation in the system. It’s increasingly clear that without incorporating cellular growth dynamics, the inferred dynamics often becomes incomplete and less accurate. To fill these gaps, we present a novel method to reconstruct the growth and dynamic trajectory simultaneously as well as the underlying gene regulatory networks. A deep learning-based dynamic unbalanced optimal transport is developed to infer interpretable dynamics from high-dimensional datasets.

Weitao Chen

University of California, Riverside (Department of Mathematics)
"A Mechanochemical Coupled Model to Understand Budding Behavior in Aging Yeast – An extension of Prof. Ching-Shan Chou’s work"
Cell polarization, in which a uniform distribution of substances becomes asymmetric due to internal or external stimuli, is a fundamental process underlying cell mobility and cell division. Budding yeast provides a good system to study how biochemical signals and mechanical properties coordinate with each other to achieve stable cell polarization and give rise to certain morphological change in a single cell. Recent experimental data suggests yeast budding develops into two trajectories with different bud shapes as mother cells become old. We first developed a 2D model to simulate biochemical signals on a shape-changing cell and investigated strategies for robust yeast mating. Then we extended and coupled this biochemical signaling model with a 3D subcellular element model to take into account cell mechanics, which was applied to investigate how the interaction between biochemical signals and mechanical properties affects the cell polarization and budding initiation. This 3D mechanochemical model was also applied to predict mechanisms underlying different bud shape formation due to cellular aging.

Xinfeng Liu

University of South Carolina (Mathematics)
"Data-driven mathematical modeling, computation and experimental investigation of dynamical heterogeneity in breast cancer"
Solid tumors are heterogeneous in composition. Cancer stem cells (CSCs) are a highly tumorigenic cell type found in developmentally diverse tumors that are believed to be resistant to standard chemotherapeutic drugs and responsible for tumor recurrence. Thus understanding the tumor growth kinetics is critical for development of novel strategies for cancer treatment. For this talk, I shall introduce mathematical modeling to study Her2 signaling for the dynamical interaction between cancer stem cells (CSCs) and non-stem cancer cells, and our findings reveal that two negative feedback loops are critical in controlling the balance between the population of CSCs and that of non-stem cancer cells. Furthermore, the model with negative feedback suggests that over-expression of the oncogene HER2 leads to an increase of CSCs by regulating the division mode or proliferation rate of CSCs.
Additional authors: Hexin Chen, Department of Biology, University of South Carolina

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