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Mathematical Biology SMART Team/Directed Study Group

Starting in Fall 2019, the mathematical biology group is officially a mathematical biology SMART Team as part of the NSF Funded NSF Funded Data-Intensive Research And Computing (DIRAC) Research Training Group (RTG).

We meet weekly to discuss reearch involving the application of mathematical tools (such as mathematical modeling, computational simulation) to the study of biological systems. Our topics cover a range of biological processes including, but not limited to, protein aggregation, population genetics & structural variation, and dynamics of marine animals.

Our group is led by Professor Suzanne Sindi ( and consists of graduate students, postdocs and faculty in the Applied Mathematics, Physics and Quantitative & Systems Biology graduate programs. 

For Spring 2022, we will be meeting Wednesday from 11:00am-12:00am via Zoom . We will post our updated schedule on this website once it is complete. You can follow our activities at our Twitter Account.

If you are on the listserv or part of the slack channel, you will receive the zoom link. If you are not on the listserv but want to attend presentations, please email Prof. Erica Rutter ( for access.

Spring 2022 Schedule

January 19: [flyer]
Dr. Mikahl Banwarth-Kuhn, UC Merced
Title: Adventures in Mathematical Biology
Abstract: Mathematical Biology refers to the application of mathematics, statistics, computer science and other quantitative methods to answer questions in biology. In this talk, I will first share a little about my journey to becoming a mathematical biologist. Then, I will introduce the methods I use in my research by leading the audience through the development of a model we will use to answer questions about tumor growth. Lastly, I will talk specifically about my current research areas including prion disease, stem cells in plants, blood vessel development, and social justice.

January 26: [flyer]
Dr. Alex Quijano, Reed College
Title: Exploring Collocations using Data Science: The Complicated Story of Words and Society
Abstract: The phrase “Data Science” is an example of a collocation, a sequence of words of some length with a frequency greater than chance. A collocation is a combination of two or more words that are typically placed together to establish a certain meaning. For example, “Data Science” is not just two random words put together but it means an interdisciplinary field of study in which scientists use scientific techniques, approaches, methods, and algorithms to extract valuable insights and information from structured and unstructured data. In this talk, I will briefly introduce my journey of becoming a data scientist, and my experience of applying statistical and mathematical methods to understand natural language evolution. This talk specifically focuses on using statistical methods to detect collocations within bodies of text. Using simple statistical measures, I will guide you on exploring different combinations of words - their contextual meanings and evolution. Moreover, I will explain how we can use this technique to inform us about our society and how it relates to our most challenging problems in social justice initiatives.

February 2: [flyer]
Dr. Daniel Cruz, Georgia Tech
Title: Agent-based modeling of emergent patterning within stem cell colonies
Abstract: The differentiation of stem cell colonies into specified tissue types is possible through local and long-distance intercellular communication; however, it is unclear which mechanisms take priority in context-specific situations. Here we consider human induced pluripotent stem cells (hiPSCs) whose therapeutic potential arises from their ability to differentiate into all germ layers: endoderm, mesoderm, and ectoderm. Informed by experimental data, we develop a collection of Boolean network models for the FGF/ERK pathway which serves as a means for intercellular communication. The purpose of these models is (i) to study the role of FGF signaling in the context of hiPSC differentiation and (ii) to inform a type of multi-scale model called an agent-based model (ABM) that incorporates additional biological details like cell location. We also extend an existing mathematical framework which formalizes ABMs to estimate long-term model behavior with respect to time. In this way, we aim to both the local dynamics of intercellular communication and the emergent behaviors of our ABM to ascertain which mechanisms determine cell fate in this context.

February 9: [flyer] Special time: 4:00 pm instead of 11am!
 Dr. Ling Xue, Professor, Harbin Engineering University
Title: Evaluating strategies for tuberculosis to achieve the goals of WHO in China
Abstract: Although great progress has been made in the prevention and mitigation of TB in the past 20 years, China is still the third largest contributor to the global burden of new TB cases, accounting for 833,000 new cases in 2019. Improved mitigation strategies, such as vaccines, diagnostics, and treatment, are needed to meet goals of WHO. Given the huge variability in the prevalence of TB across age groups in China, the vaccination, diagnostic techniques and treatment for different age groups may have different effects. Moreover, the data of TB cases show significant seasonal fluctuations in China. In this talk, I present a non-autonomous differential equation model with age structure and seasonal transmission rate. Our results show that vaccinating susceptible individuals whose ages are over 65 and between 20 and 24 is much more effective in reducing prevalence of TB. Although the improved strategies will significantly reduce the incidence rate of TB, it is challenging to achieve the goal of WHO by 2050.

February 16:
NO SPEAKER (Time conflicts with SIAM Student Chapter Talk)

February 23:
Suzanne Sindi, Professor, UC Merced
Title: Tutorial on Parameter Identifiability 

March 2: [flyer]
Dr. Lihong Zhao
Title: Modeling Immunity to Malaria with anAge-Structured PDE Framework
Abstract: Malaria is one of the deadliest infectious diseases globally, causing hundreds of thousands of deaths each year. It disproportionately affects young children, with two-thirds of fatalities occurring in under-fives. Individuals acquire protection from disease through repeated exposure, and this immunity plays a crucial role in the dynamics of malaria spread. We develop a novel age-structured PDE model of malaria specifically tracking acquisition and loss of immunity across the population. Using our analytical calculation of the basic reproduction number (R0), we study the role of vaccination and immunity feedback on severe disease and malaria incidence. Using demographic and immunological data, we parameterized our model to simulate realistic scenarios. Thus, via a combination of analytic and numerical investigations, our work sheds light on the role of acquired immunity in malaria dynamics and the impact on vaccination strategies in the presence of demographic effects.

This is a joint work with Lauren Childs, Christina Edholm, Denis Patterson, Joan Ponce, Olivia Prosper, and Zhuolin Qu.

March 9: [flyer]
Dr. Antoni Luque Santolaria, Professor, SDSU
Title: Bridging the biophysics and evolution of viruses
Abstract: Viruses are the most abundant biological entity on Earth and play a pivotal role in regulating the evolution of organisms and the planet's biogeochemistry. Most viruses protect their genome in icosahedral shells made of multiple copies of the same protein. Viral icosahedral shells span two orders of magnitude in size and thousands of different architectures. Yet, the physical mechanisms that have selected such diverse viral structures are unknown. Here, I will share my lab's most recent contributions to this fundamental problem. First, I will introduce the generalized quasi-equivalence theory of icosahedral architectures as a framework to investigate systematically viral architectures and their protein components. Second, I will show how the physical relationship between the protein shell and genome of viruses has opened the door to characterize uncultured viruses, predict the existence of unknown viruses, and engineer new viruses from the environment. Finally, I will discuss a novel physical mechanism that may hold the key to how viruses explore different viral architectures

March 16:[flyer]
Dr. Alex Capaldi, Associate Professor, Valparaiso University
Title: Could Dogs Have Been Self-Domesticated via Natural Selection?: A Simulation Story
Abstract: Wolves are among the earliest known animals to be domesticated. However, the mechanism by which gray wolves were domesticated into modern dogs is still unknown. The prevailing domestication hypothesis is that humans selectively bred the gray wolves that were more docile. However, there is a more recent hypothesis which states that wolves which were less hostile towards humans would essentially domesticate themselves by naturally selecting for such wolves because of the availability of food near human settlements. Simulating the process would help demonstrate whether it was possible dogs were domesticated simply via natural selection. Therefore, we present an agent-based model of evolution of a single trait, a measure of human tolerance, in wolves to test the plausibility of the natural selection process. We use fecundity and mortality rates from the literature and use Hartigan's Dip Test for Unimodality to measure if and when divergence of populations occurred. We conclude that our model indicates the natural selection hypothesis is plausible within realistic time constraints

March 23:
NO SPEAKER  (Spring Break)

March 30: [flyer]
Dr. Laura Strube, Postdoc, Virginia Tech
Title: Role of Repeat Infection in the Dynamics of a Simple Model of Waning and Boosting Immunity
Abstract: Some infectious diseases produce lifelong immunity while others only produce temporary immunity. In the case of short-lived immunity, the level of protection wanes over time and may be boosted upon re-exposure, via infection or vaccination. Previous work developed a simple model capturing waning and boosting immunity, known as the Susceptible-Infectious-Recovered-Waned-Susceptible (SIRWS) model, which exhibits rich dynamical behavior including supercritical and subcritical Hopf bifurcations among other structures. Here, we extend the bifurcation analyses of the SIRWS model to examine the influence of all parameters on these bifurcation structures. We show that the bistable region, involving both a stable fixed point and a stable limit cycle, exists only for a small region of biologically realistic parameter space. Furthermore, we contrast the SIRWS model with a modified version, where immune boosting may involve the occurrence of a secondary infection. Analysis of this extended model shows that oscillations and bistability, as found in the SIRWS model, depend on strong assumptions about infectivity and recovery rate from secondary infection. Understanding the dynamics of models of waning and boosting immunity is important for accurately assessing epidemiological data.

April 6:

April 13:[flyer]
Dr. Amanda Laubmeier, Assistant Professor, Texas Tech
Title: Incorporating temperature-dependence in biological control by generalist insect predators
Abstract: In agricultural ecosystems, one source of biological control comes from natural insect predators. However, many insects are generalist consumers, which form dense feeding networks with high levels of intraguild predation. These intraguild interactions make it difficult to determine the efficiency of insect predator communities. Additionally, since insects are ectotherms whose behavior is strongly regulated by temperature, predator efficiency is further complicated by environmental effects. In preliminary work, we investigated how temperature impacted predator efficiency through a system of ordinary differential equations. We incorporated observations of species abundance and feeding interactions from ten agricultural fields, to build realistic systems. Using an optimization approach to maximize the expected level of pest control, we determined the “best” balance of species in the predator community. Now, we expand the temperature-dependence in this model, by incorporating terms for the effect of temperature on hunting activity and sheltering. We repeat our prior optimization to determine how this extension changes the optimal predator community. We also explore increased temperature values and variability, to assess how climate change might affect expected biological control by natural insect communities.

April 20:
Mr. Ali Heydari, Graduate Student in Applied Math
Title: N-ACT: An Interpretable Deep Learning Model for Automatic Marker Gene and Cell Type Identification
Abstract: Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of the cellular composition within complex tissues and organisms. A major limitation in most single-cell RNA sequencing (scRNAseq) analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and subjective. Given the growth in number of sequenced cells, supervised methods--specially Deep Learning (DL) models--have been developed for automatic cell type identification (ACTI), achieving high accuracy and providing scalability. However, all existing DL frameworks for ACTI lack interpretability and are used as ``black-box” models. We present N-ACT (Neural-Attention for Cell Type identification): the first interpretable deep neural network for ACTI that utilizes a novel attentive mechanism for detecting landmark genes used to identify cell-types. On all tested datasets, our results demonstrate that N-ACT accurately identifies landmark genes and cell types in an unsupervised (or semi-supervised) manner, while performing comparable to the current state-of-the-art ACTI on traditional supervised classification tasks.

April 27:
NO SPEAKER (Let's take a break before finals!)

May 4:
Dr. Samantha Erwin, Pacific Northwest National Laboratory
Title: Mathematical model of the effects of antibiotics on antimicrobial susceptibility of enteric bacteria
Abstract: Antibiotics administered systemically can cause emergence and dissemination of antimicrobial resistance among enteric bacteria. In order to develop rational, research-based recommendations for food animal veterinarians we need to understand how to maximize antibiotic efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dose regimens of enrofloxacin (a single high dose or three low doses) in cattle on enteric bacteria. For that purpose, we developed an ordinary differential equation model for the antibiotic concentrations in plasma and colon and bacteria populations in the colon. The model was fitted to individual animal data of antibiotic concentrations in the plasma and colon obtained using an ultrafiltration probe. E. coli counts in feces and the minimum inhibitory concentrations were measured over the week after receiving the antibiotic doses. Our model predicts that increasing antimicrobial susceptibility of the resistant bacteria, fitness costs of resistant bacteria, and increased removal of antibiotics from the colon all lead to improved clearance of resistant bacteria in the low dose study. Moreover, our model suggests that the long-term amount of resistant bacteria is highly dependent on the initial amount of resistant bacteria pre-antibiotic treatment

May 11:
Mr. Masato Terasaki, Graduate Student in Applied Math
Title: Mathematical Modeling of Brain Cancer Growth
Abstract: Glioblastoma multiforme (GBM) is one of the fastest-growing brain tumors and it has very low survival rates. Mathematical modeling can be used to predict the growth and treatment of brain cancer. However, one of the difficulties lies in the ability to estimate patient-specific parameters in the mathematical model from magnetic resonance imaging (MRI) data. We constructed a numerical solver to simulate tumor growth over a realistic 3D brain geometry derived from segmented-MRI. Then, using information about the size of the different glioma sub-regions, we are developing a method that estimates the patient-specific model parameters to inform the forward simulation. Our goal is to predict the overall survival of a patient from a single pre-operative scan.

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