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Previous Semester of Math Bio Meetings

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:
NO SPEAKER (JMM)

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.

 

Fall 2021 Schedule

September 8:  
Shayna Bennett, UC Merced Applied Math Graduate Student 
Title: A New Tool to Fight Invasive Species
Abstract:  Each year in the United States, invasive species cause over $120 billion in damages to natural resources. These species are difficult to detect until they are fully established and can no longer be easily removed. Containment is the most practical solution for dealing with invasive species, but requires understanding how landscape features such as rivers, roads, and changes in elevation impact spread. Since 1951, partial differential equations have been used to model the spread of invasive species and recent work has explored how rivers and roads act as barriers preventing spread. However, some invasive species are transported quickly in the presence of these network landscape features. We have developed a new mathematical model and numerical method to couple fast diffusion on a network with the Fisher-KPP equation in the surrounding landscape to better understand how rivers and roads change the spreading rate and spread pattern of a general invasive species. We present our findings for a single and three edge network over a span of 60 years. Using known metrics for spread of invasive species including radial and range distance, we have demonstrated that our results over short and intermediate times in finite domains match asymptotic results from theoretical work on coupling fast diffusion on a line with Fisher-KPP in the upper half plane. We also demonstrate that the location of the initial population density plays an important role in the spreading pattern observed in the landscape. Finally, we will discuss our numerical method, which allows the finite differences to be used in domains with complex shapes.
 

September 15:[flyer]
Ali Heydari, Morgan Lavenstein-Bendall, Jocelyn Ornelas Munoz, Akshay Paropkari
Title: Internship Panel
Abstract: Are you interested in applying for an internship? Come meet our panelists of graduate students who will be discussing their experiences in their internships this past summer. Ask your questions about finding internship opportunities, how to apply to internships, what the inverview process is like, and what day-to-day internship is like. 
 

September 29:
Jordan Collignon, Natalie Meacham
Title: Summer Research Showcase
Abstract: Come hear about what graduate students in Mathematical Biology researched over the summer. 
 

October 6:
Lihong Zhao
Title: Assessing Re-Opening Strategies for Mitigating COVID-19 Transmission Dynamics on A College Campus
Abstract: Nearly every higher-education institute rapidly transitioned all courses from face-to-face instruction to online instruction in March 2020, shortly after the World Health Organization declared the 2019 novel coronavirus outbreak (COVID-19) as a pandemic. COVID-19 is still an ongoing public health emergency of international focus. Mathematical modeling can be used to analyze and predict the spread of COVID-19 as well as evaluate the effectiveness of disease mitigation strategies, which will help educational institution leaders with decisions of whether to reopen schools. In Summer 2020, we used a model for structured bubble-like institutions to evaluate Fall 2020 reopening strategies (e.g., class-size caps, mask-use, and housing) for University of California (UC) Merced. In this model individuals within the community have complex structured interactions defined by their roles but, rather than a bubble, the boundaries between the environment are porous and certain types of individuals intermix freely within a larger surrounding community. We seek to answer whether undertaking strong disease mitigation measures on campus alone would prevent COVID-19 to enter the UC Merced campus population, or both the campus and surrounding community should adhere to strict social distancing. Our model was also used to study the spread of COVID-19 on UC Merced campus under a Fall 2021 return with different vaccination rates among campus populations.
 

October 13:
Akshay Paropkari
Title: Predicting novel transcription factor-target gene interactions in the Candida albicans biofilm network
Abstract: Biofilms are surface-adhered communities of microbial cells that can serve as reservoirs of infection. Candida albicans is a common human fungal pathogen, capable of forming biofilms on biotic and abiotic surfaces. Transcription factors (TFs), defined as sequence specific DNA binding proteins, are important players in regulating transcription during complex developmental processes, such as biofilm formation. The transcriptional network controlling biofilm formation in C. albicans, consisting of six “master” regulators, Bcr1, Brg1, Efg1, Ndt80, Rob1, and Tec1, and 1,007 downstream “target” genes, has been previously elucidated for a mature C. albicans biofilm. However, the roles of these TFs in controlling target gene expression at different stages of biofilm development have yet to be determined.

In this study, we use a supervised support vector machine (SVM) classifier and a validated set of TF binding sites (TFBSs), to predict novel TF-target gene interactions temporally over the course of C. albicans biofilm formation. First, target sequences were created using previously identified transcription factor binding site (TFBS) consensus sequences that represent potential binding sites. The number of TFBS consensus sequences for each TF depended on both the number of validated sites as well as the fidelity of the motifs and ranged from a few hundred (for Tec1) to over a million (for Rob1). Second, a feature matrix was built to capture the DNA shape and sequence qualities of each candidate TFBS motif. Next, a positive/true set of potential TFBSs were predicted using a trained SVM classifier based on the feature matrix. The sequence similarity score was the top contributing feature to classify novel TFBSs. Finally, active TF-target gene interactions were identified by correlating TF binding activity with the time-series gene expression data of target genes. Interestingly, Ndt80 and Efg1 are predicted to control the greatest number of target genes at any given stage of biofilm development. Overall, by coupling TFBS sequence and DNA shape information, here we predict novel TFBSs, TF-target gene interactions, and ultimately, entire gene regulatory networks controlling each stage of C. albicans biofilm development.
 

October 20: 
Suzanne Sindi
Title: A Tutorial and Intorduction to Markhov Chain Monte Carlo Methods (MCMC) Part I
 

October 27:
Suzanne Sindi
Title: A Tutorial and Intorduction to Markhov Chain Monte Carlo Methods (MCMC) Part II
 

November 3:
Mikahl Banwarth-Kuhn
Title: TBD
Abstract: TBD
 

November 10:
Tomas Rube
Title: TBD
Abstract: TBD
 

November 17: There will be no Math Bio Seminar on Wednesday, November 17.
Why: We stand in solidarity with our Unit-18 Lecturer colleagues who are planning to strike on November 17th and 18th. To learn more about the strike, what they are bargaining for, and how you can support your colleagues, please visit: https://www.weteachuc.org/

 

Dec 1:
Nessy Tania, Principal Quantitative Systems Pharmacologist, Early Clinical Division, Pfizer Worldwide Research, Development, and Medical

Title: Shaping Your Own Career as an Applied Mathematician
Abstract: In this talk, I will share some of my personal journey as a math biologist and applied mathematician who had pursued a tenure-track position in academia and is now working as a research scientist in the biopharma industry. I will discuss similarities and differences, rewards and challenges that I have encountered in both positions. On a more practical aspect, I will discuss how current trainees can prepare for a career in industry (specifically biopharma) and how to seek those opportunities. I will also describe the emerging field of Quantitative Systems Pharmacology (QSP): its deep root in mathematical biology and how it is currently shaping the drug development process. Finally, I will share some of my own ongoing work as a QSP modeler who is supporting the Rare Disease Research Unit at Pfizer. As a key takeaway, I hope to share that there are multiple paths to success and a rewarding and stimulating career in applied mathematics.

Dec 8:
Mikahl Banwarth-Kuhn

Title: Selected 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. Work in mathematical biology is typically a collaboration between a mathematician and a biologist. First, biologists pose questions that are difficult to answer in the lab or describe a set of interesting experimental results. Then, mathematicians use a variety of tools to develop a model to simulate the biological system in question or analyze the biological data collected. In most cases, the mathematical analyses performed provide new biological insight that can be tested using experiments. In this talk, I will review some personal adventures in mathematical biology selected from my work in prion disease, stem cells in plant, and blood vessel development.

 

Spring 2021 Schedule

February 3:  
Dr. Emily Kubicek, Disney 
Title: Data Science at Disney
Abstract:  "I didn't realize Disney had data scientists!" Data in entertainment is no new thing. What is new, and what is resulting in a large wave of new positions in this field, are the tools that can be used in conjunction with this data. Digital streaming services, cloud capabilities, and high level machine learning techniques are but a few of the reasons entertainment companies are seeing the value, or better yet, the revenue, in leveraging data intelligently. As digital platforms begin to grow in popularity, so does the need for qualified individuals to not only manage, but leverage this wealth of data. From theme parks to streaming, tech and entertainment can come together to create personalized, optimized, and efficient experiences for clients and consumers. I'm here to share with you all of the cool things about working in data science at Disney, but also want to show that choosing to pursue data science is really a choice that opens up doors to wherever you want to go.

 

February 10:
Dr. Tai-Danae Bradley,  Language & Google [flyer]
Title:  A Mathematical Perspective on Language
Abstract: From a mathematical perspective, natural language exhibits rich structure. It is both algebraic and statistical, in the basic sense that words concatenate to form longer expressions, and the frequencies of those expressions contribute to their meaning. Similar mathematical structure is found in quantum many body systems, which prompts the use of basic tools from quantum probability theory to investigate this structure in language. In recent work, my colleague and I show that these ideas find a nice home in category theory, a modern branch of pure mathematics. I’ll share a high-level overview of these ideas, working from the ground up.

 

February 17:  RESCHEDULED to March 17
Dr. Luna Luisa Sanchez Reyes, UC Merced [flyer]
Title:  Being kind to your future self: Leveraging Open Science principles and practices for your career path.
Abstract: One of the major principles of the scientific method is reproducibility, with which a scientific result can be asserted as a scientific fact and generally recognized as scientific knowledge only when it can be replicated independently several times by following the same methodology. We currently face a scientific reproducibility crisis. While initially unveiled in the psychological sciences, we continue to see unfortunate examples of published and peer-reviewed scientific results that are not reproducible across the natural, medical, and social sciences. Because scientific advancements rely on the basis of previous discoveries, when a scientific result is not replicable, it compromises any further scientific discoveries and understanding based on it, weakening scientific discoveries as a whole. The reproducibility crisis has deeply impacted science applications such as science policy and conservation practices, as well as public trust on science, data and experts. The Open Science movement aims to establish principles, protocols and initiatives to make the scientific process transparent, accessible, and reproducible, and has gained strength and visibility across the scientific community to face the challenges brought up by the reproducibility crisis. There is a growing number of Open Science practices with varying degrees of difficulty in the learning process. Is it worthy to climb the learning curve for all of these new tools? Should you focus on only a few? In this talk I will address relevant Open Science principles and practices to show how shifting to an open model of doing research can help science in general and make your career path smoother and stronger.

 

February 24:
Dr. Keisha Cook, Tulane [flyer]
Title: Single Particle Tracking with applications to lysosome transport
Abstract: Live cell imaging and single particle tracking techniques have become increasingly popular amongst the mathematical biology community. We study endocytosis, the cellular internalization and transport of bioparticles. This transport is carried out in membrane-bound vesicles through the use of motor proteins. Lysosomes, known for endocytosis, phagocytic destruction, and autophagy, move about the cell along microtubules. Single particle tracking methods utilize stochastic models to simulate intracellular transport and give rise to rigorous analysis of the resulting properties, specifically related to transitioning between inactive to active states. This confidence in the stochastic modeling of particle tracking is useful not only for particle-containing lysosomes, but also broad questions of cellular transport studied with single particle tracking

 

March 3:
Dr. Audrey Qiuyan Fu, University of Idaho [flyer]
Title: Imputation and causal network inference in genomics
Abstract: Genomic data can be complex, large, noisy & sparse. Here I will discuss two problems we have worked on. The first deals with the highly sparse data from single-cell experiments of gene expression. These data contain a large number of zeros (>80%); many of these are missing values rather than no expression. Underlying these data are complex regulatory relationships among genes, as well as potentially many cell types with different gene expression profiles. We took a deep learning approach and designed imputation methods based on autoencoders. We generated synthetic data using real single-cell data to evaluate the performance, although the theoretical properties of autoencoders for imputation are yet to be understood. The second problem deals with causal network inference: Can we learn about the complex regulatory relationships among genes directly from genomic data? Genetic variation makes this inference possible (under certain assumptions), as it provides randomization among the individuals -- this is known as the principle of Mendelian randomization in genetic epidemiology. We extended the interpretation of this principle to capture diverse causal relationships. We also developed an algorithm for learning causal networks based on the PC algorithm, a classical algorithm in computer science for inferring directed acyclic graphs

 

March 10 (Special Time 4pm):
Dr. Sulimon Sattari Hokkaido University, Molecule & Nonlinear Life Sciences Laboratory [flyer]
Title: Do leader cells drive collective behavior in Dictyostelium Discoideum amoeba colonies?
Abstract: Dictyostelium Discoideum (DD) are a fascinating model organism. When nutrients are plentiful, the DD cells act as autonomous individuals foraging their local vicinity. After nutrients become depleted, they begin to starve and must aggregate so that they can spawn in a new area. At the onset of starvation, a few (<0.1%) cells begin communicating with others by emitting a spike in the chemoattractant protein cyclic-AMP. Nearby cells within a few hundred microns sense the chemical gradient and respond by moving toward it and emitting a cyclic-AMP spike of their own. Cyclic-AMP activity increases over time, and eventually a spiral wave emerges that can travel at the centimeter scale, attracting hundreds of thousands of cells to an aggregation center. How DD cells  go from autonomous individuals to a collective entity remains an open question for more than 60 years. Recently, trans-scale imaging has allowed the ability to sense the cyclic-AMP activity at both cell and colony levels. Using both the images as well as toy simulation models, this research aims to clarify whether the activity at the colony level is in fact initiated by a few cells, which may be deemed "leader" or "pacemaker" cells. We begin by studying the meaning of leadership in collective behavior and use information-theoretic techniques to classify leaders and followers based on trajectory data, as well as to infer the domain of interaction of leader cells. 

 

March 17: 
Dr. Luna Luisa Sanchez Reyes, UC Merced [flyer]
Title:  Being kind to your future self: Leveraging Open Science principles and practices for your career path.
Abstract: One of the major principles of the scientific method is reproducibility, with which a scientific result can be asserted as a scientific fact and generally recognized as scientific knowledge only when it can be replicated independently several times by following the same methodology. We currently face a scientific reproducibility crisis. While initially unveiled in the psychological sciences, we continue to see unfortunate examples of published and peer-reviewed scientific results that are not reproducible across the natural, medical, and social sciences. Because scientific advancements rely on the basis of previous discoveries, when a scientific result is not replicable, it compromises any further scientific discoveries and understanding based on it, weakening scientific discoveries as a whole. The reproducibility crisis has deeply impacted science applications such as science policy and conservation practices, as well as public trust on science, data and experts. The Open Science movement aims to establish principles, protocols and initiatives to make the scientific process transparent, accessible, and reproducible, and has gained strength and visibility across the scientific community to face the challenges brought up by the reproducibility crisis. There is a growing number of Open Science practices with varying degrees of difficulty in the learning process. Is it worthy to climb the learning curve for all of these new tools? Should you focus on only a few? In this talk I will address relevant Open Science principles and practices to show how shifting to an open model of doing research can help science in general and make your career path smoother and stronger.

 

March 24: SPRING BREAK

 

March 31: 
Dr. Elana Fertig, Johns Hopkins [flyer]
Title: Enter the matrix - modeling tumor cell and immune cell interactions at the single cell resolution
Abstract: Tumors employ complex, multi-scale cellular and molecular interactions that evolve over the course of therapeutic response. The changes in these pathways enables tumors to overcome therapeutic regimens, and ultimately acquire resistance. New molecular profiling technologies, including notably single cell technologies, provide an unprecedented opportunity to characterize these molecular relationships. However, interpreting the specific cellular and molecular pathways in therapeutic response requires complementary computational analysis methods. We developed an unsupervised learning method, CoGAPS, that employs Bayesian non-negative matrix factorization to disentangle distinct biological processes from high-throughput molecular data. Notably, this algorithm discovers dynamic compensatory signaling in acquired therapeutic resistance from time course bulk RNA-seq data and novel NK cell activation in anti-CTLA4 response from post-treatment scRNA-seq data. To further demonstrate that the inferred pathways are biological rather than computational artifacts, we developed a complementary transfer learning method to relate learned patterns between datasets. We demonstrate that this approach identifies robust molecular processes between model systems and human tumors and enables multi-platform data integration to delineate the drivers of therapeutic response and resistance.

 

April 7: 
Dr. Fabian Jan Schwarzendahl, Postdoctoral Fellow, Institut für Theoretische Physik II: Weiche Materie Heinrich-Heine-Universität Düsseldorf [flyer]
Title: Defects and mixing in growing active nematics
Abstract: Recent works have shown that packings of cells, both eukaryotic cellular tissues and growing or swarming bacterial colonies, are well-described by a hydrodynamic model of active nematic liquid crystals.  A key property of active nematic dynamics is a chaotic self-mixing driven by motile topological defects.  For bacterial colonies, chaotic mixing could destroy genetic  spatial  structure  by  which  different  mutants  tend  to  segregate, with  important  implications  for  the  population’s  evolution.    Here,  we  study the mixing properties of an agent-based model for a growing colony of non-motile bacteria with emergent active nematic behavior.  By studying  the  defects’  mean-square  displacement,  we  find  that  their  motility, and the population’s active self-mixing, play only a minor role for active nematics where activity is driven by growth.  We compare spatial distance with distance in phylogenetic ancestry for nearby cells as a function of cell  aspect ratio, in order to distinguish effects of active nematic order from effects  of  growth  alone,  and  we  also  compare  to  a  steady-state  population with death balancing growth.  We find that, as compared with small aspect ratio cells, the active nematic bacteria exhibit only a slightly enhanced active mixing.

 

April 14:
Dr. Jill Galagher, Moffitt Cancer Center [flyer]
Title: Using tumor dynamics to characterize and treat metastatic cancer
Abstract: Despite the fact that heterogeneity is a major driver of treatment failure in advanced cancer, treatment strategies often ignore tumor evolutionary dynamics. Adaptive therapy is an evolutionary treatment strategy that exploits competition amongst heterogenous cells and is shown to be effective in pre- clinical models and clinical trials. The aim is to maintain a constant tumor burden by giving a lower dose to a shrinking tumor that selects for resistant cells and a higher dose to a growing tumor that selects for sensitive cells. Additionally, a single cycle of adaptive therapy could be used as a tool to probe tumor dynamics. This may be especially useful for clinical decision-making in the metastatic setting when multiple metastatic lesions contribute to systemic measures of burden but may not be observable through imaging.
Using an off-lattice agent-based model we investigate how spatial competition and heterogeneity affect treatment response. For different tumor compositions we compare outcomes using a continuous application given at the maximum tolerated dose with the intention to cure and an adaptive strategy that incorporates dose-modulation and treatment vacations to sustain control. Drug-sensitive tumors are cured with continuous treatment, but even a few resistant cells will cause eventual recurrence. With the right scheduling algorithm, we can maintain a steady tumor burden with adaptive therapy, as long as there are sufficient sensitive cells to suppress resistant cell outgrowth. Adaptive therapy can also control multiple metastatic lesions, and the dynamics from the first cycle can help characterize several features of the metastatic system. Tumor size, drug sensitivity, and cell turnover affect rates of response and regrowth, while changes in individual metastases gives insight on heterogeneity amongst metastases and can guide treatment. Generally, systems with more intertumor heterogeneity had better success with continuous therapy, while systems with more intratumor heterogeneity responded better to adaptive therapy. Critically, for smarter treatment strategies the underlying heterogeneity and evolutionary response of tumors should be exploited rather than ignored.

 

April 21:
Mr. Paul Lemarre [flyer]
Title: Prions come in all shapes and sizes
Abstract: Following the discovery that prions are self-replicating assemblies of proteins, mathematical models were developed in parallel with experimental methods in order to conceptualize this phenomenon. After four decades of research, much insight has been gained into protein misfolding processes and the neurodegenerative diseases which they cause. However, the complexity of these systems remains undiminished and the classical models of protein aggregation are now showing their limits. In particular, the observed spectrum of objects generated during the propagation of prions is not accounted for in any model, whereas it keeps expanding under the development of experimental tools. During this PhD project, we seek to identify the weaknesses of classical models of prion propagation in light of recent biological evidence. We suggest modified and improved models, by including different processes, by adding more levels of organization and more diversity to protein aggregates. We detail two subprojects during this talk. The first one investigates the diversity of small PrP oligomers formed in vitro, and proposes a kinetic model that fully embraces the concept of structural diversity. The second project we focus on deals with the propagation of the [PSI+] prion inside growing yeast colonies. The problem at hand is intrinsically multi-scale, and we propose a novel modeling framework for it, impulsive differential equations.

 

April 28: NO SEMINAR

 

May 5:
Dr. Bercem Dutagaci, UC Merced

 

 

Fall 2020 Schedule

August 26:
Thomas de Mondesir
Title: Automated image analysis of prion proteins with deep learning.
Abstract

Sept 2:
Organizational Meeting

Sept 9:
Lihong Zhao, RTG Postdoc in Mathematical Biology
Title: Mathematical Modeling As An Interdisciplinary Tool

Sept 16:
Suzanne Sindi, Shilpa Khatri, Erica Rutter, Lihong Zhao, and Fabian Santiago
Title: COVID-19 Modeling on UC Merced's Campus

Sept 23:
Akshay/Ali/Alex
Title: Summer Internship Experiences and How to Obtain Them

Sept 30:
John Hotchkiss, Ravi Goyal, Mathematica Consulting
Title: Mathematicians for public good: Solving problems in government and public policy

Oct 7: 
Thomas Rube
Title: Measuring Sequence-Specific Biopolymer Interactions using Biophysically Informed Machine Learning
Abstract: Sequence-specific protein-ligand interactions are critical for numerous cellular processes, including transcriptional regulation, RNA-processing, post-translational modifications, and immune recognition. In recent years, high-throughput methods that combine affinity selection of randomized ligand libraries with DNA sequencing have revolutionized our ability to quantify such sequence recognition. In this seminar, I will discuss computational challenges in interpreting such data, why mathematical modeling is critical, and introduce a general modeling framework that learns accurate and biophysically interpretable models of sequence recognition using these data. This framework employs multi-task learning to jointly analyze complementary datasets, and I will discuss how this can be used to decrease the generalization error, identify readout of modified DNA bases, and make quantitative measurements of interaction strengths and enzyme kinetics. 

Oct 14
Mikhal Banwarth-Kuhn and Jordan Collignon
Title: Quantifying the biophysical impact of budding division in yeast

Oct 21
Joana Masel
Title: TBA

Dec 2
Amandeep Kaur
Title: Mathematical Modeling and Optimization Uncovers the Regulation of Factor Xa by TFPI
Abstract: Blood coagulation is a complex network of biochemical reactions involving positive and negative feedback. Positive feedback initiates the formation of blood clot and negative feedback stops its growth. Because both over-clotting and under-clotting result in serious, and sometimes deadly consequences, it is important to understand the regulation of coagulation by inhibitors. In this study, we investigate a specific coagulation inhibitor, tissue factor pathway inhibitor (TFPI), for which the mechanism of action is not fully understood. Previous mathematical models of TFPI have fit kinetic parameters to a single experimental time course but these models fail when applied to data from multiple experimental time courses simultaneously. We use mathematical modeling, optimization, and forward uncertainty propagation method to uncover the precise mechanism of action and to determine kinetic rates by considering multiple experimental data sets simultaneously. We found that there exist multiple parameter sets that may describe the data. Assuming uncertainty in the initial condition may help in giving a better fit. Our scheme is consistent with the previous experimental data and describe the biological phenomena better than the schemes presented in the past.

 

Spring 2020 Schedule

January 29:
Tomas Rube 
Title: Quantifying sequence readout by transcription factors through principled analysis of high-throughput SELEX data

February 5:
Erica Rutter (Department of Applied Mathematics Assistant Professor)
Title: A Tutorial on Parameter Estimation, Sensitivity, and Identifiability

February 12:
Amandeep Kaur (Applied Mathematics Graduate Student)
Title: A Household Model of German Cockroach Infestations and their Effects on Symptoms of Atopic Asthma

February 19:
Dan Beller (Department of Physics Assistant Professor)
Title: Genetic lineage trees in populations undergoing range expansion

Febraury 26:
Akshay Paropkari (Quantitative and Systems Biology Graduate Student)
Title: Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (Journal Club)
Article is located here

March 4:
Jose Zamora (Materials and Biomaterials Science and Engineering Graduate Student)
TItle: Stochastic Spatial and Temporal Population-based Model for the Co-emergence of Vascular Patterns

March 11:
CANCELED

March 18:
CANCELED

March 25:
Spring Break (No Talk)

April 1:
CANCELED (Postponed to April 22)

April 8:
Melissa Spence (Applied Mathematics Graduate Student)
Title: Genomic Signal Processing for Structural Variant Detection in Related Individuals

April 15:
Derek Sollberger (Applied Mathematics Continuing Lecturer)
Title: R/Bioconductor Workshop for Genomic Data Analysis

April 22:
Ali Heydari (Applied Mathematics Graduate Student)
Title: Generating Realistic Single-Cell RNA-seq Data

April 29:
NO SPEAKER

May 6:
NO SPEAKER

 

Fall 2019 Schedule

September 4:
Ali Heydari (Applied Mathematics Graduate Student); Akshay Paropkari (Quantitative and Systems Biology Graduate Student).
Title: What I did on my summer internship and what you can do to get ready for yours!

Stpember 11:
Suzanne Sindi (Professor, Department of Applied Mathematics)
Title: Mathematical Modeling of Prion Aggregate Dynamics within a Growing Yeast Colony

Stepember 18:
Emily Jane McTavish (Professor, Department of Life and Environmental Sciences)
Title: Estimating Distributions of Divergence Times on Phylogenies Using a Dynamic Program Algorithm

September 25:
Alex Quijano (Applied Mathematics Graduate Student)
Title: The Dynamic Mode Decomposition and its Application to Diachronic Linguistic Datasets

October 2:
Ruihao Li (Quantitative and Systems Biology Graduate Student)
Title: Genetic Regulatory Network Reverse Engineering Using Attractor-based Evolutionary Algorithm

October 9:
Ajay Gopinathan (Professor, Department of Physics)
Title: Modeling Flocks of Cancer Cells

October 16:
Akshay Paropkari (Quantitative and Systems Biology Graduate Student)

October 23:
Kinjal Dasbiswas (Professor, Department of Physics
Title: Physical modeling of cell structural order by mechanical forces

October 30:
Jordan Collignon (Applied Mathematics Graduate Student)

November 6:
David Ardell (Professor, Department of Molecular and Cellular Biology)
Title: Decoding the Cellular Language of tRNA-protein Interactions

November 13:
Tyrome Sweet (Quantitative and Systems Biology Graduate Student)

November 20:
Melissa Spence (Applied Mathematics Graduate Student)

December 4:
Jinsu Kim (Postdoctoral Scholar, University of California, Irvine)

December 11:
Erica Rutter (Professor, Department of Applied Mathematics)

 

Spring 2019

January 23: Organizational Meeting

January 30: Akshay Paropkari, QSB Graduate Student

February 6: Suzanne Sindi, Applied Mathematics Faculty

February 11: ***SPECIAL TIME AND DATE***

Speaker: Prof Kathryn Hess

Location: COB2 390

Time: 2:30 - 3:30pm

Title:   Topological insights in neuroscience

Abstract:  Topology is the mathematics of shape, ideally suited to studying questions of connectivity and of the emergence of global structure from local constraints.  In this talk I will sketch a variety of fruitful applications of topology to neuroscience, such as to the identifications of biomarkers for outcome in early psychosis and to the classification of neuron morphologies, carried out by my lab over the past few years.

February 20: (2:30 - 3:30pm) Jonathan Anzules, QSB Graduate Student

 

February 22: No Meeting!

 

March 6:  Thaddeus Seher, QSB Graduate Student

 

March 13: Ali Heydari and Paul Lemarre will re-cap workshops they did at UCI

 

March 20: Tyrome Sweet, QSB Graduate Student

 

March 27: No Meeting! (Spring Break)

 

April 3:  Michael Stobb, Applied Mathematics Graduate Student

 

April 17: Jordan Collignon, Applied Mathematics Graduate Student

 

April 24: Fabian Santiago, Applied Mathematics Graduate Student

 

May 1: Melissa Spence and Ali Heydari, Applied Mathematics Graduate Students will present their ICGE project.

 

May 8: TBA

 
Fall 2018
 
August 22: Organizational Meeting
 
August 29:
Michael Stobb, Applied Mathematics Graduate Student
Topic: A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow
 
September 5:
Matea Alvarado, Applied Mathematics PhD Student at UC Merced
 
September 12:
Eric Roberts, Applied Mathematics PhD Student at UC Merced
 
September 19:
Paul Lemarre, Mathematics PhD Student at the University of Lyon
 
September 26: Clarence Le, Quantitative and Systems Biology PhD Student at UC Merced
 
October 3:
Ayme Tomson, Cognitive and Information Sciences PhD Student at UC Merced
 
October 10:
No Speaker! Writing Hour!
 
October 17:
Mikahl Banwarth-Kuhn, Mathematics PhD Student at UC Riverside
 
October 24:
Fabian Santiago, Applied Mathematics PhD Student at UC Merced
 
October 31:
Shayna Bennett, Applied Mathematics PhD Student at UC Merced
 
November 7:
No Meeting
 
November 14:
Prof Justin Yeakel, Life and Environmental Systems Department, UC Merced
 
November 21: No Meeting! (Thanksgiving!)
 
November 28:
Jordan Collingon, Applied Mathematics PhD Student at UC Merced
 
December 5:
Melissa Spence, Applied Mathematics PhD Student at UC Merced
Ali Heydari, Applied Mathematics PhD Student at UC Merced

 

 

 

Fall 2017

August 23, 2017: Organizational Meeting

August 30, 2017:
Paul Lemarre, Visiting Master's Student
Title: Mathematical Investigation Into Prion Strains Coexistence and Co-stability

September 6, 2017:
Michael Stobb, Applied Mathematics Phd Candidate
Title: Uncertaintity Quantification in Biochemical Systems
 

September 13, 2017:
Mario Banuelos, Applied Mathematics Phd Candidate
Title: Genomic Variant Detection Through Generations

 

September 20, 2017:
Suzanne S. Sindi, Assistant Professor
Title: Prion Dynamics in Dividing Yeast Populations

September 27, 2017:
Fabian Santiago, Applied Mathematics PhD Student
Title: Developing Mathematical Models to Cope with Antibiotic Resistance

October 4, 2017:
No Meeting

October 11, 2017:
Jason K. Dark, Postdoctoral Researcher at UC Irvine
Title: Stochastic Modeling of Protein Aggregation

October 18, 2017:
Matea Alvarado, Applied Mathematics PhD Student

October 25, 2017:
Shayna Bennett, Applied Mathematics PhD Student

November 1, 2017:
Shilpa Khatri, Assistant Professor

November 8, 2017:
Jordan Collignon, Applied Mathematics PhD Student

November 15, 2017:
Alex Quijano, Applied Mathematics PhD Student
Title: Mathematical Models of the Evolution of Intelligent Systems

Spring 2017
 
January 25: Jason Dark
 
February 1: Michael Stobb
 
February 8: Michael Stobb (Cont)
 
February 15: Shilpa Khatri
February 22: Suzanne S. Sindi
 
March 1: Michael De Giorgio, Assistant Professor, Penn State (Speaker hosted by Emilia Huerta-Sanchez, MCB)
 
March 8: Emilia Huetra-Sanchez
 
March 15: Emilia Huerta-Sanchez (Cont)
 
March 22: Matea Alvarado
 
March 29: Spring Break (no meeting)
 
April 5: Fabian Santiago
 
April 12: Fabian Santiago (Cont)
 
April 19: Alex Quijano
 
April 26: Mario Banuelos
 
May 3: Mario Banuelos (Cont)
 
Fall 2016
 
September 7: Jason Davis
 
September 14:
 
September 21: Mario Banuelos
 
Sepbeber 28:
 
October 5: Michael Stobb
 
October 12:
 
October 19:
 
October 26:
 
November 2: Matea Alvarado
 
November 9: Suzanne Sindi
 
November 16: Alex Quijano
 
November 30: Fabian Santiago
 
December 7: Shilpa Khatri
 
Previous Semesters
 
Spring 2016
 
February 25, 2016: Lindsay Waldrop, Post Doc
 
February 1, 2016: Fabian Santiago, Grad Student
 
February 8, 2016: Michael Kelley, Grad Student
 
February 15, 2016: President's Day (No Meeting)
 
February 22, 2016: Suzanne Sindi, Asst Prof
 
February 29, 2016: Mario Banuelos, Grad Student
 
March 7, 2016: Michael Stobb, Grad Student
 
March 14, 2016: Jason Davis, Grad Student
 
March 21, 2016: Sprint Break (No Meeting)
 
March 28, 2016: Karin Leiderman, Asst Prof
 
April 4, 2016: Shilpa Khatri, Asst Prof
 
April 11, 2016: Nicholas Danes, Grad Student
 
April 18, 2016: Matea Alvarado, Grad Student
 
April 25, 2016: Alex Quijano, Grad Student
 
May 2, 2016: Ngan Nguyen, Post-Doc
 
Group Meeting is Held on Thursdays 2:00-3:00 in COB SAR
 
Fall 2015
 
September 3, 2015
Karin Leiderman, Asst. Prof.
Synergy between Tissue Factor and Activated FXIa
 
September 10, 2015
Suzanne Sindi, Asst. Prof
 
September 17, 2015
Lindsay Waldrop, Post Doc
 
September 24, 2015
Jason Davis, Grad Student
 
October 1, 2015
Shilpa Khatri, Asst. Prof.
 
October 8, 2015
Michael Stobb, Grad Student
 
October 15, 2015
Nicholas Danes, Grad Student
 
October 22, 2015
Mario Banuelos, Grad Student
 
October 29, 2015
 
 
November 5, 2015
 
November 12, 2015
 
November 19, 2015
 
December 3, 2015
 
 
 
Group Meeting is Held on Tuesdays 2:00-3:00 in COB SAR
 
SPRING 2015
 
January 27, 2015
Suzanne Sindi, Assistant Professor
Mathematical Modeling of Prion Aggregation 
 
February 3, 2015
Karin Leiderman, Assistant Professor
 
February 10, 2015
Terese Thompson, Graduate Student
 
February 17, 2015
Marc Griesemer, Visiting Assistant Professor
 
February 24, 2015
Mario Banuelos, Graduate Student
 
March 3, 2015
Shilpa Khatri, Assistant Professor
 
March 10, 2015
SIAM CSE Practice Talks and Posters
 
March 17, 2015
No Meeting
 
March 24, 2015
Spring Break
 
March 31, 2015
Nick Danes, Graduate Student
 
April 7, 2015
Michael Stobb, Graduate Student
 
April 14, 2015
Jason Davis, Graduate Student
 
April 21, 2015
Elizabeth Owens, Graduate Student
 
April 28, 2015
Hoang-Ngan Nguyen, Postdoc
 
May 5, 2015
Marc Griesemer, Postdoc and Visiting Assistant Professor
 
 
 
 
 
FALL 2014
 
September 9th, 2014
Marc Griesemer, Visiting Assistant Professor
"BiP Clustering Facilitates Protein Folding in the Endoplasmic Reticulum."
 
September 16th, 2014
Nick Danes, Graduate Student
"Factor Xa Production and its Dependence on the Competition of Factors VII/VIIa for Tissue Factor."
 
September 23rd, 2014
Jason Davis, Graduate Student
"Modeling Chaperone-Assisted Fragmentation in Prion Aggregation." CANCELLED
 
September 30th, 2014
Hoang-Ngan Nguyen, Postdoc
Paper presentation: "The dynamics of sperm detachment from epithelium in a coupled fluid-biochemical model of hyperactivated motility" - Simons et al., JTB 2014
 
October 7th, 2014
Terese Thompson, Graduate Student
When zombies attack!
 
October 14th, 2014
Jason Davis, Graduate Student
"Modeling Chaperone-Assisted Fragmentation in Prion Aggregation." (Rescheduled from Sept 23)
 
October 21st, 2014
Michael Stobb, Graduate Student
Modeling Teeth Primordia Using Turing Instabilities
 
October 28th, 2014
Elizabeth Owens, Graduate Student
The relationship between genetic and geographic distance
 
November 4th, 2014
Shilpa Khatri, Assistant Professor
Biological Invasions
 
November 18th, 2014
Suzanne Sindi, Assistant Professor
Mathematical Modeling of Prion Aggregation
 
December 2nd, 2014
Mario Banuelos, Graduate Student
Classifying p53 Mutant Data