Lab Members Bio
I have collected data from both a WT mouse species and an IL-2 KO species that develops several forms of autoimmune disease. This data will be used to estimate the parameters of a mathematical model that represents the homeostatic elements of the immune system. Our model is able to grasp the known dynamics of homeostasis and can be used to understand the etiology of autoimmune disease. Experimentally doing so is extremely costly and consumes an innumerable amount of man hours. By using biological data to estimate the parameters of our mathematical model we can simulate the homeostatic dynamics that that occurs in a growing immune system before autoimmune disease sets in. We then can test our hypotheses to identify the patterns of dysregulation that can lead to a predisposition of autoimmune disease. By elucidating the internal working dynamics of homeostatic expansion we can understand the multiple factors that are essential for stability. Dynamic models, such as the one proposed can used to perform experiments quickly and cost-effectively under a wide variety of conditions, and the results can be used to plan in vivo or in vitro experimentation.
I am starting my fourth year in the Applied Mathematics Ph.D. program at the University of California, Merced. My research focuses on the development of mathematical models and computational tools to study the dynamics of prion proteins using multiscale modeling techniques. To start, I am studying prion dynamics in a colony of saccharomyces cerevisiae, the baker’s yeast, and analyzing the colony structure as it relates to the distribution of prion proteins. Previous research has investigated the dynamics of the cell colony structure, but not much is known about intracellular dynamics within the colony and how these are connected to the information the colony contains. My research hopes to synthesize the intracellular and extracellular dynamics to better explain the processes behind prion aggregation in a growing cell colony. Eventually, I would like to extend this project toward how prion disease affects the human brain and use the results of my research to aid in the fight against this disease.
I am a third year Applied Mathematics PhD student in the Sindi Lab. My research lies at the intersection of deep learning, computer vision, and bioinformatics, but I am also very interested in natural language processing and machine learning in general. The goal of my research is to develop computational methods for better downstream analysis of Single-Cell RNA sequencing and development of personalized treatments. To achieve this, we have used a deep generative model to generate realistic synthetic single-cell data. Creating realistic in-silico data will help scientists with more robust analysis and more reproducible research. The next aspect of my research focuses on developing large-scale deep-learned transferable network for cell-type classification and clustering using large-scale public and generated data in a multi-task manner. Our pre-trained core will enable researchers to transfer the vast existing knowledge to their domain specific tasks, allowing for accurate predictions on datasets orders of magnitude smaller than our own. We will demonstrate the capabilities and performance of our model by fine-tuning our core on small domain-specific data for single-cell clustering and classification tasks.
I am a second year graduate student in the doctorate program at UC Merced. I am passionate about developing mathematical models, learning new techniques, and applying mathematical and statistical approaches to answer questions about biological phenomena. My research focuses on uncertainty quantification and kinetic parameter estimation to fit biological data to the model. Currently, I am looking closely at the underlying dynamics of the initial phase of blood coagulation. In this phase, the activation of factor X by the extrinsic pathway of coagulation occurs to form Xa, which is thought to be the initial step for clotting. My main goal is to determine kinetic parameters by fitting the data to the existing scheme that models the regulation of extrinsic pathway factor Xa formation by tissue factor pathway inhibitor. In particular, I am interested in looking at all the uncertainties in the parameter space and see how these uncertainties translates to the uncertainty in the model output. If the scheme I am working on does not fit effectively the data, then I will model a new scheme which both fits the experimentally determined data and explains the importance of TFPI in the initial phase of blood coagulation and the underlying dynamics.
My research interests are centered around mathematical modelling for biology. My ongoing PhD project aims at studying the propagation of protein misconformation in neurodegenerative diseases, or prion diseases. Mathematical tools are used to build models of the molecular processes and derive conclusions on their macroscopical impact. Some characteristics of prion propagation fail to be explained by previous mathematical modelling works, such as prion strain phenomena, interactions and coexistence, and species barrier. The first branch of my project focuses on these mammalian diseases, in collaboration with biologists.
The second branch relies on the study of yeast prions. These unicellular organisms can indeed host a variety of misconformed proteins that propagate in a prion-like manner. Yeast cells are a great toy model because they can be manipulated and engineered with great control. However, the specificities of this system (cell division, asymmetric division, influence of chaperones) call for unique modelling approaches. We aim at building a solid mathematical modelling framework for the study of yeast prions, with the intent to get the most out of experimental results and obtain valuable information regarding the underlying molecular processes. Given the similarities with prion diseases, this would be beneficial for both fields.
I am working on creating a quantitative model to understand the mechanism underlying biofilm formation in human fungal pathogen – Candida albicans. C. albicans is a normal resident of human microbiome and its ability to form biofilms is a major etiological factor for local and systemic infections. Recent evidence suggests differential gene expression associated with C. albicans biofilm formation. We are yet to fully understand the regulatory adaptations underlying its biofilm development. My work will inform us on what gene regulatory changes occurring during C. albicans biofilm formation.
I am currently a third-year Ph.D. student in Applied Mathematics at the University of California, Merced, and a Graduate Research Fellow with the National Science Foundation. I am broadly interested in data science, natural language processing, and any mathematical way I can contribute to equity. My current research projects include analyzing discourse, studying the spread of misinformation, and identifying covert transmission of hate speech on social media (specifically Twitter). When I’m not doing math, I like to watch crime shows and/or look at pictures of dachshunds on the internet.
Alex John Quijano
I am interested in an interdisciplinary research that includes applying mathematical techniques on language modeling, natural languages processing (NLP), machine learning, data mining, and data visualization. In particular, I am interested in research problems involving mathematical modeling and statistical analysis of natural languages. I seek to develop models that describes and explains how language - its word meanings and syntax - evolved in time using machine learning algorithms and evolutionary models. I use modern natural languages processing techniques and data science methods to extract information from available linguistic data from digitized books and online text data. The first project that I am currently working on is the use of the Google Ngram data to analyze eight languages and compared it to a neutral model of word frequency evolution. The multivariate time-series evolutionary dynamics of words are investigated using mathematical methods such as the Vector AutoRegression and the Dynamic Mode Decomposition. Second, I use NLP methods to uncover the discourse and evolution behind the certain hashtag social movements on Twitter. Lastly, I have experience in training and evaluating encoder-decoder with self-attention type models (also called Transformers) - such as BERT - for Question Answering and text classification tasks.
I am a PhD candidate in the Applied Mathematics Department. My research interests are in the application of statistical, machine learning, and mathematical modeling methods to answer questions in biology that relate to public health. The focus of my PhD work is on studying the rates and dynamics of protein misfolding, or prions, in Saccharomyces cerevisiae (yeast) cells. This prion phenotype can be inherited by daughter cells through a non-Mendelian form of inheritance. In humans and other mammals, prions diseases are associated with neurodegeneration and are fatal. Because prions are not harmful to yeast, this allows their use as biological model to gain insight into the biological mechanisms that govern prion replication and transmission.
My second research interest is in data driven investigations of bacterial antibiotic resistance. Antibiotic resistance is a global human health problem. In the U.S. more than 35,000 people die from antibiotic-resistant infections and around 3 million get an antibiotic-resistant infection every year (CDC). Our collaborators have partnered with Dignity Health Mercy Medical Center to study antibiotic resistance in clinical isolates. The goal of this work is to use a mathematical approach to evaluate new biological techniques used to study the evolution of bacterial antibiotic resistance, and to understand local and national trends in antibiotic resistance.
Developed a customized bioinformatic pipeline to detect and analyze prophage sequences in 10,000 S. aureus genomes, discovering thousands of putative prophages. This resource will facilitate the discovery of novel roles for prophage in the function and evolution of S. aureus and enable insights that will assist in the fight against multi-drug resistant infections.
I am a postdoctoral scholar in the Sindi Lab. My research interests are in using mathematical modeling and computational methods to answer questions in biology. My work combines multi-scale modeling, intracellular signaling, mechanobiology, scientific computing, uncertainty quantification and sensitivity analysis to develop computational tools that integrate biological data from experiments and produce predictive simulations that motivate the development of future experiments.
I am currently developing a cell-based model to investigate the spread of prion disease dynamics within an actively growing and dividing yeast colony. Prion proteins are most commonly associated with fatal progressive neurodegenerative diseases in humans and other mammals. Most of the current mathematical models developed for studying prion disease dynamics only focus on isolated prion aggregate dynamics. However, in a population of living cells, different cell behaviors such as growth, diffusion, and division are known to impact the abundances and concentrations of reactants and could have a large impact on protein aggregation or more specifically propagation of prion aggregates throughout the colony. Developing a cell-based model that incorporates both intracellular dynamics and cell behaviors affecting protein aggregation will provide a novel tool to test hypotheses about mechanisms that can explain unresolved experimental data and yield new strategies for treating protein misfolding diseases.
I am a DIRAC-RTG Postdoctoral Researcher in the Mathematical Biology SMaRT Team. I develop and analyze mathematical models of biological systems to explain observed dynamics and gain insight into underlying mechanisms. My research has focused on the application of mathematics to ecology and epidemiology. My work involves both analytical and numerical approaches, ordinary and partial differential equations, inverse problem methodology including parameter estimation and model selection criteria, sensitivity analysis, and techniques from mathematical population genetics and quantitative genetics. I am also interested in stochastic modeling approaches and spatio-temporal models.
I’ve worked on modeling population dynamics of Pomacea maculata (a recent invasive species), developed a bacterial-load structured model of the transmission dynamics of Mycobacterium marinum in aquatic animals with the key feature of variability in the structure variable, looked at the trait evolution in microbial communities, and I’ve working on disentangling the association of microbiome dynamics with Chlamydia infection. My work in these areas involves both analytical and numerical approaches. I believe for mathematical models to be useful, they must have a solid mathematical formulation with realistic biological understanding, and mathematical results should guide the refinement of empirical studies. I look forward to establishing new collaborations and being engaged in projects in the Sindi lab.