# Mathematical Biology

## Self-organization and pattern selection in run-and-tumble processes

I will report on a simple model for collective self-organization in colonies of myxobacteria. Mechanisms include only running, to the left or to the right at fixed speed, and tumbling, with a rate depending on head-on collisions. We show that variations in the tumbling rate only can lead to the observed qualitatively different behaviors: equidistribution, rippling, and formation of aggregates. In a second part, I will discuss in somewhat more detail questions pertaining to the selection of wavenumbers in the case where ripples are formed, in particular in connection with recent progress on the marginal stability conjecture for front invasion.

## A simple stochastic model for cell population dynamics in colonic crypts

The questions of how healthy colonic crypts maintain their size under the rapid cell turnover in intestinal epithelium, and how homeostasis is disrupted by driver mutations, are central to understanding colorectal tumorigenesis. We propose a three-type stochastic branching process, which accounts for stem, transit-amplifying (TA) and fully differentiated (FD) cells, to model the dynamics of cell populations residing in colonic crypts. Our model is simple in its formulation, allowing us to estimate all but one of the model parameters from the literature. Fitting the single remaining parameter, we find that model results agree well with data from healthy human colonic crypts, capturing the considerable variance in population sizes observed experimentally. Importantly, our model predicts a steady-state population in healthy colonic crypts for relevant parameter values. We show that APC and KRAS mutations, the most significant early alterations leading to colorectal cancer, result in increased steady-state populations in mutated crypts, in agreement with experimental results. Finally, our model predicts a simple condition for unbounded growth of cells in a crypt, corresponding to colorectal malignancy. This is predicted to occur when the division rate of TA cells exceeds their differentiation rate, with implications for therapeutic cancer prevention strategies.

## Mathematical Biomedicine: Examples

Mathematical biomedicine is an area of research where questions that arise in medicine are addressed by mathematical methods. Each such question needs first to be represented by a network with nodes that includes the biological entities that will be used to address the medical question. This network is then converted into a dynamical system for these entities, with parameters that need to be computed, or estimated. Simulations of the model are first used to validate the model, and then to address the specific question. I will give some examples, mostly from my recent work, including cancer drug resistance, side effects and metastasis, autoimmune diseases, and chronic and diabetic wounds, where the dynamical systems are PDEs. In each example, I will write explicitly the biological network, but will not the details of the corresponding PDE system.

## A journey in the use of mathematical models to gain insight into ecological and sociological phenomena

While mathematical models have classically been used in the study of physics and engineering, recently, they have become important tools in other fields such as biology, ecology, and sociology. In this talk I will discuss the use of partial differential equations and dynamical systems to shed light onto social and ecological phenomena. In the first part of this talk, we will focus on an Ecological application. For an efficient wildlife management plan, it is important that we understand (1) why animals move as they do and (2) what movement strategies are robust. I will discuss how reaction-advection-diffusion models can help us shed light into these two issues. The second part of the talk will focus on social applications. I will present a few models in the study of gentrification, urban crime, and protesting activity and discuss how theoretical and numerical analysis have provided intuition into these different social phenomena. Moreover, I will also point out the many benefits of utilizing a mathematical framework when data is not available.

## Perceptual Learning in Olfaction: Flexibility, Stability, and Cortical Control

The ability to learn and remember is an essential property of the brain that is not limited to high-level processing. In fact, the perception of olfactory stimuli in rodents is strongly shaped by learning processes in the olfactory bulb, the very first brain area to process olfactory information. We developed computational models for the two structural plasticity mechanisms at work. The models capture key aspects of a host of experimental observations and show how the separate plasticity time scales allow perceptual learning to be fast and flexible, but nevertheless produce long-lasting memories. The modeling gives strong evidence for the formation of odor-specific neuronal subnetworks and indicates how their formation is likely under top-down control.

## What conifer trees can show us about how organs are positioned in developing organisms

One of the central questions in developmental biology is how organs form in the correct positions in order to create a functional mature organism. Plant leaves offer an easily observable example of organ positioning, with species-specific motifs for leaf arrangement (phyllotaxis). These patterns arise through a combination of chemical pattern formation, mechanical stresses and growth. Mathematical modelling in each of these areas (and their combinations) contributes to quantitative understanding of developmental mechanisms and morphogenesis in general. Conifer trees are some of the most characteristic plants of BC. They also display a type of ring patterning of their embryonic leaves (cotyledons), which I believe offers a unique route to understanding plant phyllotaxis in general. I will discuss how early work at UBC on similar patterning in algae led to application of reaction-diffusion models in conifer development. This framework has guided experiments at BCIT and recently led to a model that accounts for the natural variability in conifer cotyledon number. The model involves the kinetics of a highly conserved gene regulation module and therefore sheds light on the chemical pattern formation control of phyllotaxis across plants. Conifer patterning also demonstrates scaling of position to organism size, an active area of research in animal development: the model provides some mechanistic insight into how this can occur via chemical kinetics.

## Turing Patterns on Growing Domains

Turing patterns have been suggested as an explanation for morphogenesis in a variety of organisms. Despite the fact that morphogenesis occurs during growth, most studies of Turing patterns have been conducted on static domains. We present experimental and computational studies of Turing patterns in a chemical reaction-diffusion system on growing two-dimensional domains. We also investigate the effect of inert obstacles on pattern evolution. We find that the rate of domain growth significantly affects both how the patterns are laid down and their ultimate morphology.

## Existence, Stability and Slow Dynamics of Spikes in a 1D Minimal Keller–Segel Model with Logistic Growth

We analyze the existence, linear stability, and slow dynamics of localized 1D spike patterns for a Keller-Segel model of chemotaxis that includes the effect of logistic growth of the cellular population. Our analysis of localized patterns for this two-component reaction-diffusion (RD) model is based, not on the usual limit of a large chemotactic drift coefficient, but instead on the singular limit of an asymptotically small diffusivity of the chemoattractant concentration field. In the limit, steady-state and quasi-equilibrium 1D multi-spike patterns are constructed asymptotically. To determine the linear stability of steady-state N-spike patterns, we analyze the spectral properties associated with both the “large” O(1) and the “small” o(1) eigenvalues associated with the linearization of the Keller-Segel model. By analyzing a nonlocal eigenvalue problem characterizing the large eigenvalues, it is shown that N-spike equilibria can be destabilized by a zero-eigenvalue crossing leading to a competition instability if the cellular diffusion rate exceeds a threshold, or from a Hopf bifurcation if a relaxation time constant is too large. In addition, a matrix eigenvalue problem that governs the stability properties of an N-spike steady-state with respect to the small eigenvalues is derived. From an analysis of this matrix problem, an explicit range of cellular diffusion rate where the N-spike steady-state is stable to the small eigenvalues is identified. Finally, for quasi-equilibrium spike patterns that are stable on an O(1) time-scale, we derive a differential algebraic system (DAE) governing the slow dynamics of a collection of localized spikes. Unexpectedly, our analysis of the KS model with logistic growth in the small chemical diffusion rate regime is rather closely related to the analysis of spike patterns for the Gierer-Meinhardt RD system.

This paper is a joint work with Professor Michael J. Ward and Juncheng Wei.

## Opinion Dynamics and Spreading Processes on Networks

People interact with each other in social and communication networks, which affect the processes that occur on them. In this talk, I will give an introduction to dynamical proceses on networks. I will focus my discussion on opinion dynamics, and I will also discuss coupled opinion and disease dynamics on networks. Time-permitting, I may also briefly discuss a model of COVID-19 that centers on disabled people and their caregivers.

## Localized Patterns in Population Models with the Large Biased Movement and Strong Allee Effect

The strong Allee effect plays an important role on the evolution of population in ecological systems. One important concept is the Allee threshold that determines the persistence or extinction of the population in a long time. In general, a small initial population size is harmful to the survival of a species since when the initial data is below the Allee threshold, the population tends to extinction, rather than persistence. Another interesting feature of population evolution is that a species whose movement strategy follows a conditional dispersal strategy is more likely to persist. To study the interaction between Allee effect and the biased movement strategy, we mainly consider the pattern formation and local dynamics for a class of single species population models that is subject to the strong Allee effect. We first rigorously show the existence of multiple localized solutions when the directed movement is strong enough. Next, the spectrum analysis of the associated linear eigenvalue problem is established and used to investigate the stability properties of these interior spikes. This analysis proves that there exist not only unstable but also linear stable steady states. Finally, we extend results of the single equation to coupled systems for two interacting species, each with different advective terms, and competing for the same resources. We also construct several non-constant steady states and analyze their stability.

This is a work in progress talk by a local graduate student.