Ching-Shan Chou, Ph.D. - US grants

Cell polarity, the asymmetric organization of cellular components, is fundamental to life. Important cell functions such as differentiation and proliferation rely upon spatially and temporally accurate cell polarization and the induced cell morphologenesis. While polarization typically involves signal transduction through certain pathways, many recent experimental and theoretical studies revealed that, besides the feedback regulators, more machineries including substructure of the plasma membrane and intracellular vesicle trafficking are contributing to the architecture of cell polarity and cell morphogenesis. This project is concerned with a mathematical and computational investigation of the establishment and maintenance of cell polarity and the induced cell morphological change during yeast Saccharomyces cerevisiae mating process. The first part of this project is to study the role of microdomains on the cell membrane in cell polarization, tracking directional changes of the stimuli, and attenuating noise present in the signals. The second part will investigate potential mechanisms that maintain the polarized growth of yeast cells during mating, with models including a moving cell membrane. The roles of endocytosis and exocytosis in cell polarization will be studied with these models.


A major topic in cell biology is to understand how cells sense and react to a wide variety of stimuli, which convey information essential for their growth, development and functions. Cell polarization, which relies on an asymmetric organization of intracellular components, has been widely acknowledged to be a fundamental process underlying spatial sensing of stimuli. The work seeks to address two important questions in cell biology through mathematical modeling and computation: 1) how is the cell polarity established and maintained? 2) what controls the emergence and dynamics of cell morphogenesis during signal sensing? Our models are based on the known experimental observations of polarized morphological change during yeast mating process, and they incorporate important components in the signaling network. We will investigate how the machineries such as substructure of the cell membrane and vesicle trafficking, participate in generating and retaining cell polarity, and how those machineries are incorporated in cell morphogenesis during yeast mating process. Quantitative studies of cell polarization and cell morphogenesis, based on the systems biology methodology which integrates mathematical modeling with experiments, will improve our understanding on the behavior of cell in response to the environment or its physiological setting. The developed framework and models could be applied to other systems, such as T-cell and neutrophils, which also undergo polarization and morphological changes in response to external signals. The research project is interdisciplinary and will enhance interdisciplinary training at the interface between mathematics and biology for the students associated with the project.

Cell-to-cell communication is fundamental to cell and system functions. Proper communication between cells relies upon precise control and coordination of extracellular and intracellular signaling, as well as the induced cellular responses such as cell morphogenesis. This project is to study cell-to-cell communication through yeast mating system using mathematical modeling and will be conducted in close collaboration with experimentalists. The project will develop the models and the computational framework for a multi-cell environment that includes complex extracellular and intracellular signaling networks and the cell morphological changes. Numerical methods will be developed to meet the computational modeling challenges.

Cells interact with their environment or with other cells through sensing and making responses such as cell movement and cell shape change. These processes require precise coordination of intricate cellular networks and the resulting responses. This project will use mathematical modeling and computation to examine the interaction between cells and the underlying networks. The models and computational framework developed in this research can be applied to other systems, such as neural and immune systems, in which cell-to-cell communication is central to their function. Therefore, this project can aid medical research. The research in this project will be integrated with educational activities in computational and mathematical methods, with the aim of training undergraduate and graduate students for interdisciplinary research in mathematical biology.

As the High Performance Computing (HPC) field moves towards even more powerful (Exascale) systems, it faces two key challenges: resilience and power efficiency. Increasing computing power while not significantly increasing the power budget potentially involves new architectural designs, such as the ones with low power and/or low margins. Though future systems are expected to experience an increase in the number of faults (because of increasing number of cores and decreasing feature sizes), energy efficient designs will likely suffer even more errors due to tighter margins and other design compromises. Of particular concern are the errors that escape hardware detection: such errors are said to cause Silent Data Corruption (SDC). Maintaining correctness of a numerical simulation in the presence of SDCs is a very challenging problem. The intellectual merit of this project is in combining ideas from numerical methods, programming model design and architecture design to address the correctness of numerical simulations. The broader significance and importance of the project includes impact on scientific and high performance computing, system software for parallel computing, and architectural designs and research. This project will also make several contributions towards education, human resource development, and increasing diversity, with activities like teaching parallel computing (and programming) to diverse audience, mentoring of doctoral students, including those from underrepresented groups, and an interdisciplinary training program in Mathematical Biology for undergraduates.

Technically, the project addresses the challenge of developing and executing scientific applications with energy efficient low-power/margin architectures that experience occasional faults, while maintaining programmer productivity and accuracy of results. This project develops a synergistic research program combining advances in HPC programming models, runtime systems, Near Threshold Voltage (NTV) architectures, and numerical methods (algorithms). Specifically, the project involves close collaboration between researchers from three areas: (a) parallel programming models, applications, and runtime systems, (b) architecture, and (c) finite difference and finite volume numerical models.

Project Summary    Aging  is  a  complex  process  that  involves  numerous  physiological  and  morphological  changes. Although aging in multicellular organisms is inevitably more complex, the basic  mechanisms of cellular aging appear to be conserved across organisms ranging from the  single-­celled  yeast  to  mammals.  Cell  asymmetry  and  polarity  are  critical  for  cell  proliferation  and  development,  and  the  loss  of  cell  polarity  and  asymmetry  has  been  implicated  in  cellular  aging.  However,  the  causes  of  cellular  aging  remain  poorly  understood. Cdc42, an evolutionally conserved Rho GTPase, is one of the key regulators  of  cell  polarity  in  diverse  species  including  yeast  and  humans.  While  the  mechanism  underlying  Cdc42  polarization  has  been  extensively  investigated  for  its  roles  in  the  establishment of cell polarity, the functional significance of the Cdc42 signaling in aging  has  not  been  addressed.  Here,  we  will  use  our  expertise  in  the  small  GTPase  field  to  understand the spatial and temporal regulation of Cdc42 signaling during aging process.  In this proposal, we will test an unexplored concept that the intrinsic genetic program of  cell polarity and morphogenesis is linked to control of cellular lifespan using the tractable  budding  yeast  as  a  model.  Specifically,  we  will  explore  how  changes  of  positive  and  negative  regulation  of  Cdc42  during  repeated  cell  divisions  limit  replicative  lifespan  by  combining methods in genetics and live-­cell imaging in a microfluidic device. We will also  use  mathematical  modeling  to  predict  underlying  mechanisms  leading  to  loss  of  Cdc42  polarization  and  thus  limiting  the  cell  division.  The  outcomes  of  this  work  will  lay  the  foundation  for  identifying  a  mechanism  underlying  cellular  aging  that  will  be  also  applicable  to  other  eukaryotes.  Knowledge  gained  from  this  study  will  ultimately  be  translatable  to  identifying  candidate  genes  and  processes  in  humans  that  are  similarly  affected by aging.         

The goal of this project, jointly funded by the Division of Mathematical Sciences Mathematical Biology Program and Division of Molecular and Cellular Biosciences Cellular Dynamics and Function Program, is to obtain a more detailed understanding of how cells "polarize" to generate different shapes. Specifically, how do cells rearrange themselves to go from a symmetric object like a round egg cell into an asymmetric object like a nerve cell? Experiments will be performed on budding yeast which can switch from a sphere to an asymmetric projection shape by localizing components to one end, a process termed cell polarization. Examples of polarization include immune cell activation, tumor cell metastasis, and yeast infections in which yeast cells invade human tissue by polarized cell growth. Fungal infections are increasing in prevalence in the U.S. Insights can help researchers discover treatments to halt the invasive growth of yeast infections. The research will investigate the spatial distribution of cellular proteins during polarization using a combination of mathematical modeling and microscopy imaging. Experimental data can be compared to computer simulation data to confirm the models and adjust the model parameters. An important methodological advance will be replacing the model, which can take significant time to run, with a simpler polynomial surrogate model which can be calculated very quickly resulting in a dramatic computational speed-up. Through this process one obtains models that can reproduce and predict the behavior of polarizing cells. In addition, the research will be integrated with outreach activities training graduate, undergraduate, and high school students on how to perform quantitative microscopy experiments and simulate mathematical models.

Cell polarity and morphology define the form and function of individual and groups of cells. A systems biology approach will delve into this subject at a more quantitative level moving beyond arrow diagrams to characterize the spatial dynamics of cell polarity. The investigation will take advantage of the experimental tractability of budding yeast to characterize two classic cell polarity morphologies: the bud and the mating projection. More specifically, the investigation will use microscopy to visualize the spatial dynamics of polarization proteins and process the images into quantitative data. In parallel, a collection of mathematical models of budding and mating projection growth will be constructed based on biological hypotheses. Methodologically, one of the grand challenges of systems biology is to estimate the models/parameters using large datasets. Bayesian inference will be used to select the best models and estimate the parameters based on the experimental data. A central concept in this proposal will be applying techniques from uncertainty quantification (UQ) that replace model evaluations in the Monte Carlo method with a surrogate polynomial function, resulting in a dramatic speed-up of the uncertainty analysis. The combined result will be a systematic investigation of cell polarity leading to model predictions that will be tested by experiments converting one cellular morphology into the other. In addition, the proposed study of improved surrogate model calculation will further accelerate the uncertainty analysis of complex models.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.