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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 2020, 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 ( for access.

Fall 2020 Schedule

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

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:
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.

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