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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 (ssindi@ucmerced.edu) and consists of graduate students, postdocs and faculty in the Applied Mathematics, Physics and Quantitative & Systems Biology graduate programs. 

For Spring 2021, we will be meeting Wednesday from 9:00-10:00am 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 (erutter2@ucmerced.edu) for access.

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

If you want to be added to our mailing list please email ssindi@ucmerced.edu.