Mathematical modeling of emergent phenomena in cell colonies

Monday, June 14 at 11:30am (PDT)
Monday, June 14 at 07:30pm (BST)
Tuesday, June 15 03:30am (KST)

SMB2021 SMB2021 Follow Monday (Tuesday) during the "MS02" time block.
Note: this minisymposia has multiple sessions. The second session is MS01-CBBS (click here).

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Shawn D. Ryan (Cleveland State University, United States), Mykhailo Potomkin (UC Riverside, United States), Jia Gou (UC Riverside, United States)


Biological systems are complex, they typically consist of a vast number of interacting components, which may exhibit an emergent behavior. Though phenomena occur at the macroscale, it is the result of spontaneous cooperation between the components at the microscale. Examples are collective swimming of bacteria changing the rheology of a bacterial suspension, and steady cell motility originated from molecular motors moving inside a cell. How collective nonlinear interactions at the microscale result in observable behavior at the macroscale is a challenging question. The goal of this special session is to gather experts in mathematical models capable of describing emergent behavior in various biological contexts with the focus on microorganisms and their colonies.

Shawn D. Ryan

(Dept. of Mathematics and Statistics, Cleveland State University, United States)
"Role of hydrodynamic interactions in collective swimming of bacteria"
Chemotaxis of bacterial populations has been traditionally modeled using either individual-based models describing the motion of a single bacterium as a velocity jump process, or macroscopic PDE models that describe the evolution of the bacterial density. Hydrodynamic interaction has been shown to induce collective bacterial motion and self-organization resulting in larger mesoscale structures. In this talk, the role of hydrodynamic interactions in bacterial chemotaxis is investigated by extending a hybrid computational model that incorporates hydrodynamic interactions and adding components from a classical velocity jump model. It is shown that hydrodynamic interactions enhance the merging of the small aggregates into larger ones and lead to qualitatively different aggregate behavior than possible with pure chemotaxis models. Namely, differences in the shape, number, and dynamics of these emergent clusters.

Paul Kulesa

(Stowers Institute for Medical Research, United States)
"Coupling Invasion and Collective Migration of the Embryonic Neural Crest"
Several well-known models of collective cell migration, such as the Zebrafish lateral line or Drosophila border cells, feature tightly connected cells or cell-neighbor contacts through broad lamellipodial protrusions that together have led to cell adhesion and contact-inhibition of locomotion models. In contrast, neural crest cells travel in loosely connected, discrete streams and interact with each other through thin filopodial extensions. This has led to natural questions as to how neural crest cells invade through extracellular matrix and mesoderm, and communicate with each other over long distances to move collectively. Here, we set out to understand the molecular signals that drive collective neural crest cell migration using a combination of experimental perturbations, gene profiling, time-lapse imaging and computational modeling. We test the central hypothesis that lead neural crest cells express a distinct set of genes that are critical to invasion and the source of signals that communicate information to promote collective migration. By using a novel label free, unsorted single cell RNA sequencing method we derive the transcriptional states of migrating neural crest cells and the cellular landscape of the chick head, neck, and cardiac region. We identify a set of novel cell invasion genes common to the first four branchial arch streams and use time-lapse imaging and molecular perturbations to test their functional relevance. Cell behavioral and stream changes are compared to agent-based model simulations that incorporate the neural crest migratory domain and experimentally-derived measurements of tissue growth and chemotaxis. We conclude that local cell invasion signals and long-range communication between follower cells play a critical role in collective neural crest cell migration and may provide key insights to stem-cell based strategies that aim to repair birth defects to the face and neck and treatment of aggressive cancers.

Brian Camley

(Johns Hopkins University, United States)
"Collective cell migration on patterns with topological defects"
Sheets of eukaryotic cells migrate cooperatively in order to heal wounds or invade new locations - and these cell monolayers can be guided by ridges and patterns on their substrate. How do cells in a monolayer respond when given conflicting signals from their neighbors and the surface they are crawling on? We are motivated by recent experiments showing that fibroblasts crawling on target-shaped patterns can align to the pattern, but show increased cell density and decreased cell anisotropy near the center of the pattern [Endresen et al. Soft Matter 2021]. These induced topological defects within the liquid crystalline order of these cells are known to be important in both morphogenesis and cell death. We model our cells as self-propelled deformable ellipses that interact via a modified Gay-Berne potential. Consistent with experiment, cells are denser and more isotropic toward the center of the defect. This density change is driven by the combination of collective cell flow, the cell anisotropy, and the ability of the cells to deform their shapes. We also discuss how these factors alter the extent of coherent rotational motion in these systems.

Wouter-Jan Rappel

(UC San Diego, United States)
"Modeling the collective motion of amoebae"
Collective rotational motion is observed in a variety of experimental settings, including dense extracellular matrices and patterned substrates. Here we focus on the rotational vortex-like state observed when the social amoeboid Dictyostelium cells aggregate following starvation. We employ traction force microscopy to determine the force patterns during this aggregation process. We then develop a mathematical model that can provide insights into the mechanisms of this collective motion.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.