Abstract : Emerging forms of experimental data in cellular and molecular biology have driven new modeling frameworks, many of which are stochastic due to noisy characteristics at these scales. This session features stochastic model construction and analysis of microbiological systems ranging in scale from the movement of genetic material (e.g., DNA) to multi-cell tissues, including mathematical advances motivated by the challenges to explain observed phenomena. Mathematical themes will include stochastic differential equations, Monte Carlo techniques for stochastic simulation, and stochastic PDEs. Several talks will also incorporate novel approaches to effectively representing and incorporating experimental data into the modeling process.
Abstract : Biological systems are traditionally studied as isolated processes (e.g. regulatory pathways, motor protein dynamics, transport of organelles, etc.). Although more recent approaches have been developed to study whole cell dynamics, integrating knowledge across biological levels remains largely unexplored. In experimental processes, we assume that the state of the system is unknown until we sample it. Many scales are necessary to quantify the dynamics of different processes. These may include a magnitude of measurements, multiple detection intensities, or variation in the magnitude of observations. The interconnection between scales, where events happening at one scale are directly influencing events occurring at other scales, can be accomplished using mathematical tools for integration to connect and predict complex biological outcomes. In this work we focus on building inference methods to study the complexity of the cytoskeleton from one scale to another.
[02757] Inferring RNA Dynamic Rates from Spatial Stochastic Snapshots
Format : Talk at Waseda University
Author(s) :
Christopher Edward Miles (University of California, Irvine)
Abstract : There are unresolved mysteries about the dynamics of RNA splicing, an important molecular process in the genetic machinery. These mysteries remain because the obtainable data for this process are not time series, but rather static spatial images of cells with stochastic particles. From a modeling perspective, this creates a challenge of finding the right mathematical description that respects the stochasticity of individual particles but remains computationally tractable. I'll share our approach to constructing a spatial Cox process with intensity governed by a reaction-diffusion PDE. We can do inference on this process with experimental images by employing variational Bayesian inference. Several outstanding issues remain about how to combine classical and modern statistical/data-science approaches with more exotic mechanistic models in biology. This work is in collaboration with the Ding lab of Biomedical Engineering at UCI.
[02952] Centrosome Movement and Clustering During Mitosis
Format : Online Talk on Zoom
Author(s) :
Sarah Olson (Worcester Polytechnic Institute)
Amity Manning (Worcester Polytechnic Institute)
Abstract : While much work has been done to understand the roles of the key molecular components of the mitotic spindle
during cell division, identifying the consequences of force perturbations in the spindle remains a challenge. In
particular, cells with extra centrosomes may undergo a bipolar or multipolar division. We combine experimental
approaches with computational modeling to define a role for cortical dynein in centrosome clustering, allowing for a
bipolar division in cells with extra centrosomes.
[01821] Stochastic model of nuclear size control in S. pombe
Format : Online Talk on Zoom
Author(s) :
Xuesong Bai (Brandeis University)
Thomas Fai (Brandeis University)
Abstract : The size of the nucleus scales robustly with cell size so that the nuclear-to-cell size—the N/C ratio—is maintained during growth in many cell types. To address the fundamental question of how cells maintain the size of their organelles despite the constant turnover of proteins and biomolecules, we consider a model based on osmotic force balance predicts a stable nuclear-to-cell size ratio, in good agreement with experiments on the fission yeast Schizosaccharomyces pombe. We model the synthesis of macromolecules during growth using chemical kinetics and demonstrate how the N/C ratio is maintained in homeostasis. We compare the variance in the N/C ratio predicted by the model to that observed experimentally.
[05399] Stochastic models of DNA methylation, plasticity and drug resistance
Format : Online Talk on Zoom
Author(s) :
Jasmine Foo (University of Minnesota)
Abstract : In this talk I will discuss some recent work on modeling stochastic intracellular DNA methylation processes and examine their consequences on population dynamics within a growing tumor.
[04460] Stochastic modeling of ovarian aging
Format : Online Talk on Zoom
Author(s) :
Sean Lawley (University of Utah)
Abstract : Why are women born with up to a million primordial follicles when only a few hundred will ever ovulate a mature egg? What physiological mechanisms trigger menopause? Can the age of natural menopause be predicted? Can menopause be delayed? In this talk, we will describe recent stochastic models of ovarian aging which are aimed at answering these questions.
[02211] Optimal curvature and directional sensing in long-range cell-cell communication
Format : Talk at Waseda University
Author(s) :
Jun Allard (University of California Irvine)
Sohyeon Park (University of California Irvine)
Dae Seok Eom (University of California Irvine)
Hyunjoong Kim (University of Pennsylvania)
Abstract : Cells in tissue can communicate long-range via diffusive signals. In addition, another class of cell-cell communication is by long, thin cellular protrusions that are $\sim 100$ microns in length, i.e., many cell-lengths, and $\sim 100$ nanometers in width, i.e., below traditional microscope resolution. These protrusions have been recently discovered in many organisms, including nanotubes humans and airinemes in zebrafish. But, before establishing communication, these protrusions must find their target cell. Here we demonstrate airinemes in zebrafish are consistent with a finite persistent random walk model. We study this model by stochastic simulation, and by numerically solving the survival probability equation using Strang splitting. The probability of contacting the target cell is maximized for a balance between ballistic search and diffusive highly curved, random search. We find that the curvature of airinemes in zebrafish, extracted from live cell microscopy, is approximately the same value as the optimum in the simple persistent random walk model. We also explore the ability of the target cell to infer direction of the airineme’s source, finding the experimentally observed parameters to be at a Pareto optimum balancing directional sensing with contact initiation.
[04467] Centrosome asymmetry in the early C. elegans embryo
Format : Talk at Waseda University
Author(s) :
Adriana Dawes (Ohio State University)
Shayne Plourde (The Ohio State University)
David Ignacio (The Ohio State University)
Andrew Cohen (The Ohio State University)
Abstract : Centrosome positioning, which determines where a cell divides, is mediated by microtubules, biopolymers nucleated at the centrosomes, and the motor protein dynein. Using stochastic and continuum models along with a measure of centrosome movement, we identify key proteins involved in regulating centrosome movement in early embryos of the nematode worm C. elegans, and demonstrate the parallel role of cell geometry in proper positioning of the centrosomes.
[04462] Modeling and tracking random motion in micrometer-scale living systems
Format : Talk at Waseda University
Author(s) :
Jay Mack Newby (University of Alberta)
Abstract : We study stochastic motion of objects in micrometer-scale living systems: tracer particles in living cells, pathogens in mucus, and single cells foraging for food. We use stochastic models and state space models to track objects through time and infer properties of objects and their surroundings. For example, we can calculate the distribution of first passage times for a pathogen to cross a mucus barrier, or we can spatially resolve the fluid properties of the cytoplasm in a living cell. Recently developed computational tools, particularly in the area of Markov Chain Monte Carlo, are creating new opportunities to improve multiple object tracking. The primary remaining challenge, called the data association problem, involves mapping measurement data (e.g., positions of objects in a video) to objects through time. I will discuss new developments in the field and ongoing efforts in my lab to implement them. I will motivate these techniques with specific examples that include tracking salmonella in GI mucus, genetically expressed proteins in the cell cytoplasm, active transport of nuclei in multinucleate fungal cells, and raphid diatoms in seawater surface interfaces.
[04071] Stochastic effects in molecular motor teams under detachment and reattachment
Format : Talk at Waseda University
Author(s) :
Peter Kramer (Rensselaer Polytechnic Institute)
Joseph Klobusicky (The University of Scranton)
John Fricks (Arizona State University)
Abstract : We revisit two paradigms of cooperative action by kinesin molecular motors involving a coupling of the detachment and reattachment processes with the stochastic spatial dynamics. First, for two dissimilar types of kinesin transporting a common cargo, we provide approximate analytical characterizations for how incorporating slack in the tether model affects the cooperative dynamics. Secondly, we extend consideration of gliding assays to a situation where microtubules are crosslinked while being crowdsurfed by immobilized kinesin.
