Organized by: Wing-Cheong Lo, Weitao Chen, Wenrui Hao, Leili Shahriyari
Note: this minisymposia has multiple sessions. The other session is MS07-CDEV-1.

• 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.

Organized by: Wanda Strychalski, Alexander Hoover
Note: this minisymposia has multiple sessions. The other session is MS07-CDEV-2.

• Wanda Strychalski Case Western Reserve University (Mathematics, Applied Mathematics, and Statistics)
  "Quantifying the role of fluid mechanics during confined cell migration"
Cell migration is critical for many vital processes, such as embryogenesis and tissue repair, as well as harmful processes, such as cancer cell metastasis. In experiments, cells have been shown to exhibit different migration strategies based on the properties of their external environment. Here, we leverage modeling and computational tools to reveal the step-by-step cycle of locomotion for cells in confined environments that use blebs as leading-edge protrusions. We present two models of a blebbing cell migrating in a confined microchannel to quantify the role of hydrodynamics on confined cell migration. One model consist of a cell modeled by an elastic membrane, poro-(visco)elastic cortex, membrane-cortex adhesion, and the fluid cytoplasm. The fluid-free model consists of a force balance that includes the cell membrane, cortex, membrane-cortex adhesion, and viscous drag with outside environment. The channel walls are modeled as rigid structures. The fluid model is formulated using the method of regularized Stokeslets. Results show that cells can effectively migrate only if the cortical turnover is included by modeling the cortex as a poro-viscoelastic structure. We also show that blebbing generates a favorable intracellular pressure gradient that aids migration in the fluid model.
• Jared Barber Indiana University-Purdue University Indianapolis (Mathematical Sciences)
  "A 2D model to assess stresses on flexible osteocytes and the influence of elastic properties"
Osteocyte are cells residing deep in bone that are widely believed to play a key role in regulating bone growth by sensing and responding to forces as we use our bodies daily. Despite several experimental and theoretical studies providing strong support for this paradigm, there are still several uncertainties surrounding the process by which the cells turn those forces into usable biochemical signals. For instance, studies suggest that to initiate any appreciable response from osteocytes, the average stresses we typically experience on a macroscale level must multiplied at least tenfold as they make their way towards the microscale level. In addition, there are several parts of the osteocyte that have been theorized to play a role in the mechanotransduction process. To help understand how forces may be magnified in and near the osteocyte region and which parts of the cell are more likely to be the location of subcellular mechanosensors, we have produced a two-dimensional model of a flexible osteocyte. The cell is represented by a network of interconnected viscoelastic elements (damped springs) immersed in interstitial flow that is, in turn, encased in rigid bone matrix material. We utilize a lattice-Boltzmann method combined with the immersed boundary method to produce simulations that allow us to explore the force distributions experienced by such cells. We share our results including pictures of where forces seem to centralize in such systems as well as how the elastic properties of different parts of the cell affect force localization in both steady state and oscillatory regimes.
• Thomas Fai Brandeis University (Mathematics)
  "Lubricated Immersed Boundary Method with Application to Fiber Bundles"
Fluid-mediated near contact of elastic structures is a recurring theme in biofluids. The thin fluid layers that arise in applications such as the flow of red blood through blood vessels are difficult to resolve by standard computational fluid dynamics methods based on uniform fluid grids. A key assumption of the lubricated immersed boundary method, which incorporates a subgrid model to resolve thin fluid layers between immersed boundaries, is that the average velocity of nearby boundaries can be accurately computed from under-resolved simulations to bridge between different spatial scales. Here, we present a one-dimensional numerical analysis to assess this assumption and quantify the performance of the average velocity as a multiscale quantity. We explain how this analysis leads to more accurate formulations of the method and present examples from two-dimensional simulations, including applications to filament bundles.
• Luoding Zhu Indiana University - Purdue Univiersity Indianapolis (Mathematics)
  "Computational modeling of stress/strain amplification of an osteocyte process interacting with a viscous flow in a 3D canaliculus"
Computational modeling of stress/strain amplification of an osteocyte process interacting with a viscous flow in a 3D canaliculus Jared Barber (1), Maxim Mukhin (2), Vanessa Maybruck (3), Luoding Zhu (4) 1. Indiana University – Purdue University Indianapolis, USA; 2. Vanderbilt University, USA; 3. University of Colorado Boulder, USA; 4. Indiana University – Purdue University Indianapolis, USA. Osteocytes are bone cells stationed in fluid-filled cavities (lacunae) within hard bone matrix. Each osteocyte is equipped with numerous finger-like structures (processes) radiating outwards through cylindrical openings (canaliculi). Osteocytes are responsible for mechanosensation in the body; however, the tissue level stress and strain needs to be amplified at least 10 times in order for osteocytes to respond on the cellular level. The mechanism for such magnification is not yet fully understood. Previous studies suggest that the processes are primary sites for mechanosensation thanks to the existence of tethering elements attaching the process membrane to the canalicular wall. However, other studies suggest that potential contributing factors may also include the canalicular wall geometry and pericellular matrix. In our work, computational modelling, based on the lattice Boltzmann immersed boundary framework, is designed and used to assess possible effects of canalicular wall roughness in stress/strain amplification and the underlying mechanism. Our results indicate that canalicular wall roughness contributes substantially to stress and strain amplification and the underlying reason is the increased resistance to flow induced by wall roughness. Acknowledgements This work was supported by grants DMS-1951531 and DMS-1852146 from NSF USA and SoS Near-Miss Grant from IUPUI.

Organized by: Christopher Heggerud, Tyler Meadows
Note: this minisymposia has multiple sessions. The other session is MS07-ECOP-1.

• Alan Hastings University of California - Davis (Environmental Science and Policy)
  "Transient dynamics: the key to ecological understanding"
Much of classical ecological theory is focused on the long -term behavior of ecological models yet the time scales of ecological dynamics are such that a focus on asymptotic behavior is likely misguided. Ecological conclusions change in important ways when focusing on appropriate time scales. I will begin with some of my much older work that suggests the importance of transients and some of the challenges. I will then focus on more recent work, most of which has been done with a wonderful group of colleagues from a working group that began at NIMBioS. We have given a rough classification of features that produce transients similar to approaches for understanding dynamical systems, examined implications for management, and examined transients in systems where stochasticity is important. I will also consider related issues that arise when taking into consideration changing external conditions (global change) and the implications for ecological prediction.
• Rebecca Tyson University of British Columbia, Okanogan
  "Mutualism at the leading edge: Insights into the eco-evolutionary dynamics of host-symbiont communities during range expansion"
The evolution of mutualism between hosts and symbiont communities plays and essential role in maintaining ecosystem function and thus should have a profound effect during range expansion. In particular, the presence of mutualistic symbionts at the leading edge should enhance the propagation of the host and the overall symbiont community. Here we develop a theoretical framework that captures the eco-evolutionary dynamics of resource exchange between host symbionts and their dispersal in space. We provide quantitative insights into how the evolution of resource exchange may shape community strucure during range expasion. Parasitic symbionts receive the same amount of resources from the host as mutualistic symbionts, but at lower cost. This selective advantage is strengthened with resource availability (i.e., with host density), promoting mutualism at the range edges, where host density is low, and parasitism in the core of the range, where host desnity is higher. Host growth depends on the overall benefit provided by the symbiotic community, and is maximal at the expansion edges, where symbionts are more mutualistic. The expansion of host-symbiont communities is pulled by the hosts, but pushed by the symbionts. The spatial selection also influences the speed of spread. In particular, hosts with low dependence on their symbionts, or host-symbiont communities with high symbiont density at their core (e.g., resulting from more mutualistic hosts) or at their leading edge (e.g., resulting from symbiont inoculation) enhance the speed of spread into new territories.
• Susmita Sadhu Georgia College & State University (Department of Mathematics)
  "Methods for analyzing long transient dynamics in a three-dimensional predator-prey model featuring two timescales"
The leading role of long transient dynamics in ecological timescales can be very important in explaining regime shifts. However, analytical techniques for studying long transients in relevant timescales in three or higher-dimensional ecological models is still at its infancy. In this talk, I will consider a three-dimensional predator-prey model featuring two-timescales that studies the interaction between two species of predators competing for their common prey with explicit interference competition. I will consider two different scenarios in a parameter regime near {emph{singular Hopf bifurcation}} of the coexistence equilibrium point. In one case, the system exhibits bistability between a periodic attractor and a boundary equilibrium state, with long transients characterized by rapid small-amplitude oscillations and slow variation in amplitudes, while in the other, the system exhibits chaotic {emph{mixed-mode oscillations}}, featuring concatenation of small and large-amplitude oscillations, as long transients before approaching a stable limit cycle. To analyze the transients, the system is reduced to a suitable normal form near the singular Hopf point. Exploiting the separation of timescales and the underlying geometry of the normal form, the transient dynamics are analyzed. The analyses are then used to devise methods for identifying early warning signals of a large population transition leading to an outbreak or resulting in an extinction of one of the species.
• Tyler Meadows Queen's University (Mathematics and Statistics)
  "Evolution of persister cells"
Most β-lactam antibiotics, such as penicillin, function by disrupting membrane formation during mitosis. So-called persister cells survive antibiotic treatment by entering a semi-dormant state. These cells can be used to found a new culture of microorganisms that is equally susceptible to the antibiotics as the original culture. We investigate a model for the competition between two species of bacteria with different affinities for the persister type on both the population scale and the evolutionary scale.

Organized by: Omar Saucedo, Bren Case, Lauren Childs
Note: this minisymposia has multiple sessions. The other session is MS07-MEPI-1.

• Widodo Samyono Jarvis Christian University (Mathematics and Sciences)
  "Parameters Identifiability for selecting the best model using differential equations optimization"
Solving mathematical model based on differential equations (DEs) to match with the data by changing the initial guesses for identifying the parameters interactively by using trial and error methods is needed lots of efforts and time. Additionally, the model could be ill-posed and non-linear in term of the parameters, so specialized computational techniques are needed. To solve these problems, the parameter identification problems were set up as differential equations optimization, where the objective function is the misfit statistical measures between the data and the model solutions, and the constraint is the DE. The optimization can be formulated as constrained and unconstrained optimization. Some specialized numerical methods are presented. The models are the classical mathematical models for cancer cell growths.
• Marisa Eisenberg University of Michigan, Ann Arbor (Epidemiology and Complex Systems)
  "Identifiability and infectious disease interventions: exploring when uncertainty matters"
Identifiability, estimability, and parameter reduction methods provide tools to understand the interactions between parameters, model structure, and outputs—and how these interactions determine what inferences and predictions are possible for a given system. In particular, issues of identifiability and uncertainty can affect whether it is possible to select an optimal intervention—an important question for applied infectious disease modeling. In this talk, we will explore how identifiability can be used in practice to help inform epidemiological decision-making, and when intervention strategies are or are not robust to uncertainty in the model parameters and structure.
• All Participants
  "Open Forum"
The last 30 minutes of this session will include an open forum for discussion with speakers and participants.

Organized by: Joshua Caleb Macdonald, Hayriye Gulbudak
Note: this minisymposia has multiple sessions. The other session is MS07-MEPI-2.

• Anna Jolles Oregon State University (Carlson College of Veterinary Medicine and Department of Integrative Biology)
  "Mechanisms of persistence of highly transmissible foot-and-mouth viruses in their maintenance host, African buffalo (Syncerus caffer)"
Extremely contagious pathogens are a global biosecurity threat because of their high burden of morbidity and mortality, as well as their capacity for fast-moving epidemics that are difficult to quell. Understanding the mechanisms enabling persistence of highly transmissible pathogens in host populations is thus a central problem in disease ecology. Through a combination of experimental and theoretical approaches, we investigated how highly contagious foot-and-mouth disease viruses persist in the African buffalo, which serves as their wildlife reservoir. We found that viral persistence through transmission among acutely infected hosts alone is unlikely. Working with three viral strains (SAT1,2,3), we found that different strains appear to utilize distinct mechanisms to ensure their long-term persistence in their maintenance host: The inclusion of occasional transmission from persistently infected carriers reliably rescues the most infectious viral strain (SAT1) from fade-out. We observed that antibody titers against FMD viruses are surprisingly dynamic in buffalo; and show that frequent drops in antibody protection can allow persistence of the least transmissible strain we studied (SAT3). The persistence of SAT2 remains somewhat enigmatic - additional mechanisms such as antigenic shift, or spillover among host populations may be required for its persistence.
• Simon Gubbins The Pirbright Institute (Transmission Biology)
  "Cross-scale dynamics of foot-and-mouth disease virus: from within hosts to between farms"
Foot-and-mouth disease virus (FMDV) infects cloven-hoofed livestock and wildlife species. It causes foot-and-mouth disease (FMD), which has substantial economic impacts for endemic countries and for disease-free countries when epidemics occur in them. Because of its importance FMDV has been studied at a range of scales from within a host to continental scale. This provides an opportunity to develop data-driven multi-scale models for FMDV and to examine how process at one scale affect process at another. In this presentation we will discuss a mathematical and statistical framework for linking models for FMDV at different scales (within-host, between-host and between-farm) to investigate how the dynamics at one scale influences dynamics at another. For example, we can use the models to show how within-host parameters (e.g. viral growth and clearance rates) influence between-host transmission (reproduction numbers) and how within-farm transmission (e.g. via direct contact or a contaminated environment) affects between-farm transmission. The models are parameterised using Bayesian methods applied to a combination of data from transmission experiments, within-farm outbreaks and regional epidemics. This allows us to test model assumptions and to incorporate parameter uncertainty at one scale in predictions at another.
• Jan Medlock Oregon State University (Biomedical Sciences)
  "The Persistence of Foot-and-Mouth Disease Virus in African Buffalo"
Foot-and-mouth disease virus (FMDV) is a very important trade-restricting livestock disease. In sub-Saharan Africa, buffalo act as reservoir for FMDV, challenging global eradication and local economies. However, little is known about the dynamics of FMDV in African buffalo. We conducted FMDV infection experiments to quantify epidemiologic parameters of FMDV transmission in buffalo, and a 3-year cohort study to document birth timing, and duration of maternal protection from FMDV infection. We used Bayesian inference to estimate parameters, and constructed a rigorous quantitative framework that explicitly incorporates individual variation in birth rates, waning of maternal antibodies, and epidemiological parameters into predictions about disease persistence from an individual-based stochastic model. We used our model to show that FMDV's high transmission rate, short infectious period, and long-term immunity, when combined with the buffalo’s seasonal variation in births, fails to explain the persistence of FMDV from year to year. We showed that an alternative hypothesis, based on infection experiments, that FMDV forms some long-term carriers after acute infection does explain the persistence for one of the three circulating serotypes in southern Africa. I will also discuss work-in-progress on hypotheses that may explain the persistence of the two remaining serotypes.
• Cameron Browne University of Louisiana at Lafayette (Mathematics)
  "Environmental adaptation and seasonality in cholera eco-evolutionary dynamics"
Cholera epidemics are largely driven by direct transmission from person to person or indirectly through environment, although Vibrio cholerae is also capable of growth and long-term survival in aquatic ecosystems. In this talk, I will discuss recent mathematical modeling work showing how fluctuations and strain evolution in the environment impacted the cholera outbreak in Haiti beginning in 2010. First, we calibrate a stochastic multi-strain mixed-transmission dynamic model of V. cholerae to phylogenetic, case and seasonal rainfall data from Haiti. Along with fitting the clinical incidence, we connect genetic diversity and a coalescence process in model simulations to the effective population size computed from serially sampled cholera genomes. The results suggest that environmental replication actively contributes to genetic diversification and environmental adaptation, which can impact the success of different control measures. Mathematical analysis of the underlying deterministic model is challenging, however competitive exclusion is proved in the absence of environmental replication and seasonality. Assuming only partial cross-immunity in this case does induce coexistence of two strains (called serotypes) and serotype cycling with seasonal forcing, which may explain switching of serotype dominance observed in Haiti.

Organized by: Daniel A. Cruz,Margherita Maria Ferrari

• Lina Fajardo Gomez University of South Florida (Mathematics)
  "Homology for Directed Graphs with Applications to DNA Recombination"
We propose custom made cell complexes, in particular prodsimplicial complexes, in order to analyze data consisting of directed graphs. These are constructed by attaching cells that are products of simplices and are suited to study data of acyclic directed graphs, called here consistently directed graphs. We investigate possible values of the first and second Betti numbers and the types of cycles that generate nontrivial homology. We apply these tools to graphs associated with DNA recombination processes in certain species of ciliates and we study the effects of changes in the directed graphs on the homology.
• Puttipong Pongtanapaisan Arizona State University (Mathematics and statistics)
  "On the scarcity of split links spanning a lattice tube"
Frisch, Wasserman, and Delbrück conjectured that as the length of a polymer chain tends to infinity, the probability of knotting approaches 1. Similar questions regarding the entanglement complexity of multiple closed curves can also be addressed with lattice models. In this talk, I will show that all but exponentially few sufficiently large spanning pairs of self-avoiding polygons in the (2x1)-tube are linked.
• Caitlin Lienkaemper Boston University (Department of Mathematics and Statistics)
  "Combinatorial coexpression in mosquito olfaction"
Across species, the olfactory system follows a stereotyped organization: each olfactory receptor neuron expresses a single type of olfactory receptor, and responses of olfactory sensory neurons which express the same receptor are pooled before they are sent to higher regions of the brain. Mosquitoes have recently been shown to violate this organization: olfactory sensory neurons coexpress multiple receptor types, thus mixing information about activation of different receptors from the start. We describe the properties of this pattern of receptor expression as a combinatorial code, consider from an information-theoretic perspective how coexpression helps mosquitoes perform as olfactory-guided predators, and investigate how the pattern of coexpression must be tuned to the pattern of stimulus statistics.
• Radmila Sazdanovic NC State University (Mathematics)
  "Categorified chromosome aberration model"
Graphs are ubiquitous in mathematics and widely used to model physical phenomena in science and engineering. One such model was developed by Sachs and collaborators to analyze exchange type chromosome aberrations. In this talk we introduce a 'categorification' of this model where chromosomes (and their aberrant versions) are modeled as objects and the aberrations causing exchange processes as morphisms in a thin-surface cobordism category. This new framework allows for interpreting and analyzing patterns of chromosome aberrations observed in radiation experiments.
• Margherita Maria Ferrari University of Manitoba (Department of Mathematics)
  "Graph theory for DNA self-assembly"
The chemical and physical properties of DNA strands contain a high degree of information which allows DNA to serve as building material for assembling nanostructures. These complexes have a wide range of applications, including drug delivery and molecular scaffolding. In this talk, we focus on assembling graph-like structures using branched junction DNA molecules which are star-shaped molecules that join together through adhesion sites at the end of their arms. We describe a combinatorial representation of these molecules and consider the problem of optimally building a target graph under different laboratory settings. We show how this question give rises to new graph invariants and how these invariants can be studied through edge-colorings and graph decompositions.

Organized by: Heber Rocha, John Metzcar, Paul Macklin

• Alexander Browning University of Oxford (Mathematical Institute)
  "Drawing biological insight from non-identifiabile models of tumour growth using simple surrogates"
Models are now routine in the interpretation of biological data, however are often limited by parameter non-identifiabilities. Indeed, simple goodness-of-fit metrics including the likelihood and residual error give only limited information about where a model does and doesn’t fit. In the talk, we demonstrate a new framework for the study non-identifiability of complex models of tumour growth using simple surrogate models, that lie in between a model of interest and the data. For example, the traditional one-dimensional line of constant goodness-of-fit, studied in traditional likelihood-based identifiability analysis, might lie at the intersection of two higher dimensional surfaces, each representing features in the data (in this case, the maximum size and initial growth rate of the tumour). One can move along this intersection in parameter space and achieve only a minimal change to the model predictions; hence, parameter non-identifiability. Overall, we demonstrate a novel technique for gaining insight from the complex biological models that are often essential for the elucidation of important biological processes.
• Jeanette Johnson Johns Hopkins University (Immunology)
  "Integrating Omics Data and Agent-Based Models for Comprehensive Digital Biology"
Agent-based modeling for biological systems currently suffers from limited ability to systematically integrate experimental data into model parameters. To facilitate the wider use of ABMs in these contexts, we sought ways to address this issue. Here we present two models built in the PhysiCell agent based modeling framework using results from high-throughput analyses. We show a method for generation of model agents directly from standard 10x Visium spatial transcriptomics data into an initial state of the ABM, preserving spatial relationships between cells when translating data to model. In future work we hope to build a general purpose “digital tissue” pipeline, capable of constructing spatially-resolved ABMs in 2d and 3d. We then show a model built upon more qualitative observations from a pancreatic cancer single-cell atlas and a separate set of clinical trial results from a matched biological context. The model simulates hypotheses of tumor progression among a group of epithelial and immune cells under three therapy conditions, exploring the tumor-immune logic of the system and attempting to understand what could have led to the results of the clinical trial. These integrations are proof-of-concept for more general integration of omics with agent-based models, and will empower life science investigators to make use of ABMs, which are ultimately very powerful hypothesis building and visualization tools, in their research.
• Adam MacLean University of Southern California (Department of Quantitative and Computational Biology)
  "Learning gene regulatory networks that control cell state transitions from multi-modal single-cell genomics"
Single-cell genomics offer unprecedented resolution with which to study cell fate decision-making in cancer. We present new tools to infer gene regulatory networks (GRNs) controling cell fate decisions and model their multiscale dynamics. We introduce popInfer, single-cell multi-modal GRN inference via regularized regression, and demonstrate its potential for network discovery Through application to hematopoiesis, we discover new gene interactions regulating early fate decisions during stem cell differentiation that are profoundly affected by diet and age.
• Matthew Simpson Queensland University of Technology (School of Mathematical Sciences)
  "A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling"
In vitro tumour spheroids have been used to study avascular tumour growth and drug design for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing spheroid, giving rise to the notion of a four-dimensional (4D) tumour spheroid. We develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data compares well with new experimental data using a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is difficult to measure these data experimentally.

Organized by: Cheng Ly, Janet Best, Pamela Pyzza, Yangyang Wang
Note: this minisymposia has multiple sessions. The other session is MS07-NEUR-1.

• Krasimira Tsaneva-Atanasova University of Exeter (Mathematics and Statistics)
  "Mathematical modelling of GnRH pulse generator frequency modulation through the interaction between kisspeptin and GABA-glutamate in the posterodorsal medial amygdala"
Gonadotrophin releasing hormone (GnRH) pulsatile activity and the initiation of functional gonadotrophin secretion controlling reproductive competence are primarily driven by kisspeptin neurons located in the arcuate nucleus of the hypothalamus. Nevertheless, kisspeptin present in other brain regions, exerts a significant modulating effect on the hypothalamic kisspeptin population. In particular, a population of kisspeptin and its receptors has been found in the posterodorsal medial amygdala (MePD), where it acts as an upstream regulator of γ-aminobutyric acid (GABA) and glutamate sub-populations of neurons. We propose a coarse-grained network model that captures the cooperative and competitive dynamics between these sub-populations. We employ bifurcation analysis to study the effect of network connectivity strengths and the role of the afferent input from kisspeptin. This allows us to characterise the dynamical changes in the MePD output for different levels of kisspeptin. Our mathematical model, supported by experimental findings demonstrate that the effective modulation of the GnRH pulse generator by amygdala kisspeptin neurons is dependent on the functional neurotransmission of both GABA and glutamate.
• Andrea K. Barreiro Southern Methodist University (Mathematics)
  "Fluid dynamics as a driver of retronasal olfaction"
Flavor perception is a fundamental governing factor of feeding behaviors and associated diseases such as obesity. Smells that enter the nose retronasally, i.e. from the back of the nasal cavity, play an essential role in flavor perception. Previous studies have demonstrated that orthonasal olfaction (nasally inhaled smells) and retronasal olfaction involve distinctly different brain activation, even for identical odors. Differences are evident at the glomerular layer in the olfactory bulb (Gautam et al. 2012, Sanganahalli et al. 2020) and can even be identified in the synaptic inputs to the bulb (Furudono et al. 2013). Why does the bulb receive different input based on the direction of the air flow? We hypothesize that this difference originates from fluid mechanical forces at the periphery: olfactory receptor neurons respond to mechanical, as well as chemical stimuli (Grosmaitre et al, 2007, Iwata et al, 2017). To investigate this, we use computational fluid dynamics to simulate and analyze shear stress patterns during natural inhalation and sniffing. We will show preliminary results demonstrating that shear stress forces differ for orthonasal vs. retronasal air flow; i.e. inspiration vs. exhalation, in a model of the nasal cavity, and connect these findings to our earlier work on directional selectivity in neural network models of the olfactory bulb (Craft et al. 2021).
• Madeline Edwards University of Pittsburgh (Department of Neuroscience)
  "Exploring the Roles of Interneuron Subtypes in Network Dynamics"
Neuronal responses to sensory stimuli can be strongly modulated by animal's brain state. Three distinct subtypes of inhibitory interneurons, parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) expressing cells, have been identified as key players of flexibly modulating network activity. The three interneuron populations have specialized local microcircuit motifs and are targeted differentially by top-down inputs from higher-order cortical areas and neuromodulators. Optogenetic stimulation of different interneuron cell types demonstrates different impacts on neuronal population responses, such as firing rate and network synchrony. In this work, we systematically study the function of each interneuron cell type at controlling network dynamics in a spatially ordered spiking neuron network. We model top down and neuromodulatory inputs as static current applied to each neuron population. We find that the network transitions through three distinct network states, from subcircuit to weak synchrony to strong synchrony state, as we activate the excitatory or SOM population or inactivate the PV or VIP population. Further, we investigate how network responses to modulatory inputs depend on the connectivity of the SOM cells. This work provides a foundational understanding for the modulation of network activity with respect to four unique populations and testable predictions for future experiments.
• Andrea Welsh University of Pittsburgh (Department of Mathematics)
  "Modeling Mouse Colon Non-propulsion Dynamics"
Colon motility, the spontaneous self-generated movement and motion of the colon muscle and its cells, is produced by activity in different types of cells such as myenteric neurons of the enteric nervous system (ENS), neurons of the autonomic nervous system (ANS) and interstitial cells of Cajal (ICC). Two colon motor patterns measured experimentally are motor complexes (MC) often associated with the propulsion of fecal contents, and ripple contractions which are involved in mixing and absorption. It has been observed that the MCs can occur without fecal matter present, but it is poorly understood how these spontaneous CMs occur. How ICC and neurons of the ENS and ANS interact to initiate and influence colon motility is still not completely understood. This makes it difficult to develop new therapies to restore function in pathological conditions. This talk will discuss the data-driven modeling of the ICCs and neurons that also capture the spontaneous global dynamics that are observed in the colon and give insight into how these dynamical features may occur.

Organized by: Yangjin Kim, Magdalena Stolarska
Note: this minisymposia has multiple sessions. The other session is MS07-ONCO-1.

• Dumitru Trucu University of Dundee (Mathematics)
  "Multiscale Modelling Glioblastoma Progression within the Fibrous Brain Tissue"
Glioblastoma multiforme (GBM) is the most aggressive brain tumour, with patients having poor survival prospects despite recent surgery, radiotherapy and chemotherapy advancements. A central role in the development and spread of GBM within the brain is played by the collective cancer cell migration within the fibrous brain environment. This talk aims to explore this key invasion aspect through a novel non-local multiscale moving boundary modelling framework that takes into account the intrinsic link between overall macroscale tumour dynamics and both the microscale proteolytic activity at the invasive edge as well as the crucial bulk microdynamics of cancer cell-fibres interactions. T1 weighted and DTI scans are used as initial conditions for our model as well as to parametrize the diffusion tensor. Numerical results will illustrate clinically relevant GBM development patterns.
• Junho Lee Konkuk University (Mathematics / Seoul, Republic of Korea)
  "Role of senescent tumor cell in building a cytokine shield in tumor microenvironment: mathematical models"
Cell aging can promote or inhibit cancer progression. Here, it was shown that the proportion of senescent tumor cells (STCs) in colorectal cancer (CRC) supported cancer growth by inhibiting intratumoral infiltration of CD8+ T cells. It has been found that the expression of C-X-C motif chemokines ligand 12 (CXCL12) and colony stimulating factor 1 (CSF1) in senescent tumor cells is increased, and senescent tumor cells secrete high concentrations of CXCL12 to spread chemokine shields. This inhibits the infiltration of CD8+T cells into tumor by causing loss of CXCR4 in T cells and interfering with directional movement. In this study, we investigate the mutual interactions between the CD8+ T cells and the STCs that prevent T cell invasion by developing a mathematical model that involves taxis-reaction-diffusion equations for the critical components in the interaction. We apply the mathematical model to a Boyden invasion assay used in the experiments to demonstrate that the over-expressed CXCL12 can prevent T cell infiltration into tumor. Moreover, we consider tumor-immune dynamics by a hybrid approach, we investigate the fundamental mechanism of STC-mediated cytokine shield and the impact on the migration patterning of T cells. We show that the model can both reproduce the major experimental observation on T cell infiltration and make several important predictions to guide future experiments with the goal of the development of new anti-tumor strategies.
• Eunjung Kim Korea Institute of Science and Technology (Natural Product Informatics)
  "Acquired resistance shapes the treatment outcomes by modulating the distribution of resistance"
Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell-cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cells acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley's K-function, Sampled Ripley's K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. This model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy.

Organized by: Melissa Stadt
Note: this minisymposia has multiple sessions. The other session is MS07-OTHE-1.

• Lihong Zhao University of California, Merced (Department of Applied Mathematics)
  "Mathematical modeling of the menstrual cycle and hormonal contraception"
Developing a mechanistic understanding of the menstrual cycle is important to human health and wellness. Menstruation is driven by hormones. Hormonal imbalances can lead to issues with menstrual cycle that affect health, wellness, and quality of life. There is a high level of variability in hormone levels both between different individuals and from cycle to cycle within a single individual. In this talk, we will provide an overview of the current state-of-the-art in mathematical modeling of the menstrual cycle. We will show how we utilize a mechanistic mathematical model of the menstrual cycle to explore the qualitative effects of hormonal contraception on the menstrual cycle.
• Erica Graham Bryn Mawr College (Mathematics)
  "Functional Variations in the Ovulatory Cycle: Insights from Modeling"
A normally functioning ovulatory cycle results from a tightly regulated system of crosstalk between the brain and the ovaries. Failure to regulate reproductive hormones may cause ovarian dysfunction and sometimes infertility. For example, polycystic ovary syndrome (PCOS) is a relatively common cause of such dysfunction, often accompanied by irregular glucose metabolism. Here we examine mechanisms of disruption and characterize ovulatory phenotypes through a new endocrine model and discuss the impact of metabolic abnormalities on the female endocrine system.
• Carley V. Cook University at Buffalo (Department of Chemical and Biological Engineering)
  "Mathematical Modeling of Osteoporosis Due to Surgical Menopause"
Osteoporosis, characterized by decreased bone mass and structural deterioration, results from an imbalance in the bone tissue's metabolic processes. In the adult skeleton, bone is remodeled regularly due to dynamic interactions between several bone cell types: osteoclasts, osteoblasts, osteocytes, and their precursors. It is known that estrogens affect bone remodeling in both biological sexes. Specifically, postmenopausal bone loss results from estrogen deficiency in older women. Estrogen deficiency has a sudden onset when the ovaries are surgically removed, and osteoporosis risk is higher in these patients than for those experiencing natural menopause. We have developed a mathematical model for the bone cell dynamical responses to estrogen deficiency during the surgical menopausal transition using information about the key impacts observed in female mice and humans after ovary removal. We build upon an existing model for osteoporosis due to aging. Our new model considers the role of embedded osteocyte cells in regulating enhanced osteoclast formation, inducing enhanced bone resorption after surgical menopause. With two new adjustable parameters, the model fits clinical bone mineral density decreases. Other parts of the model results will be compared to various in vivo clinical and animal studies. The impacts of hormone replacement therapy on surgical menopause in silico scenarios will also be simulated and discussed.