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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 Fall 2021, we will be meeting Wednesday from 9:00-10:00am via Zoom and in-person ACS 362. 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.

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
AbstractAre 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
AbstractCome 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:

October 27:

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

November 10:
Tomas Rube
Title: TBD
Abstract: TBD

November 17:

Dec 1:

Dec 8:


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