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 (email@example.com) and consists of graduate students, postdocs and faculty in the Applied Mathematics, Physics and Quantitative & Systems Biology graduate programs.
For Fall 2022, we will be meeting Wednesday from 10:00am-11:00am in ACS 362C . 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 not on the listserv but want to attend presentations, please email Prof. Erica Rutter (firstname.lastname@example.org) for access.
Fall 2022 Schedule
August 31: [flyer]
Mohammed Aburidi, Oscar Davalos, Ali Heydari, Jocelyn Ornelas Munoz
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.
Suzanne Sindi, UC Merced
Title: How to Write a Mathematical Biology Paper
Abstract: In this Professional Development series, Dr. Suzanne Sindi will discuss tips and techniques for writing Mathematical Biology papers. How much biology should you discuss? What kind of outline should your paper feature? How do you decide which information/results to include in your paper? How do you tell your story?
Dr. Justin Yeakel, UC Merced
Title: On the dynamics of mortality and the ephemeral nature of mammalian megafauna
Abstract: The vital rates constraining energy flow through consumer-resource interactions largely vary as a function of body size. These allometric relationships govern the dynamics of populations, and the energetic constraints induced by different sources of mortality influence small- to large-bodied species in different ways. Here we derive the timescales associated with four alternative sources of mortality for terrestrial mammals: starvation from resource limitation, mortality associated with aging, consumption by specialist to generalist predators, and mortality introduced by subsidized harvest. The incorporation of these allometric relationships into a minimal consumer-resource dynamic system illuminates central constraints that may contribute to the structure of mammalian communities. Our framework reveals that while starvation largely impacts smaller-bodied species, the allometry of senescence is expected to be more difficult to observe. In contrast, external predation and subsidized harvest primarily influence larger-bodied species. The inclusion of predation mortality reveals mass thresholds of mammalian herbivores at which dynamic instabilities limit the feasibility of megaherbivore populations. Moreover, we show how these thresholds vary with predator-prey mass ratios, a relationship that is little understood within terrestrial systems. Finally, we predict the harvest pressure required to induce mass-specific extinction, and compare these values to estimates from episodes of both paleontological and historical megafaunal exploitation. With co-authors: Taran Rallings and Chris Kempes.
September 21: NO MEETING
Dr. Cameron Browne, UL-Lafayette
Title: Eco-evolutionary dynamics in prey-predator networks applied to HIV immune escape
Abstract: Population dynamics and evolutionary genetics underly the structure of ecosystems. For example, during HIV infection, the virus escapes several immune response populations via resistance mutations, precipitating a dynamic network of interacting virus and immune variants. I will talk about recent work to link virus population genetics with dynamics theoretically and through data. We analyze a resource-prey-predator differential equation network model to characterize the emergence of distinct stable equilibria and persistence of different diverse collections of virus and immune populations. Using binary sequences to code viral strain resistance to immune responses, we prove that bifurcations are determined and simplified by a certain evolutionary genetics measure of nonlinearity in the map from viral sequences to fitness (reproductive rate) landscape. The results generalize to decipher stability, structure and invasion of ecological networks based on the linear algebra of prey binary sequences encoding predation and fitness trade-offs. Finally, numerical simulations and an interdisciplinary application to virus-immune data illustrate our eco-evolutionary modeling framework.
Dr. Jonathan Allen, LLNL
Title: Challenges and opportunities in data driven drug discovery
Abstract: Advances in chemical synthesis are expanding the number of molecules that can be easily made and tested for drug discovery. The exceptionally large ‘makeable’ chemical space means experimental data is collected for a tiny fraction of candidate molecules and this fundamentally limits the molecules found using an experimentally driven drug discovery process. Computational search of the virtual chemical space has the potential to find molecules that meet multiple design criteria and increase the chances of a drug candidate advancing to human clinical trials. This talk will introduce elements of the Accelerating Therapeutics for Opportunities in Medicine (ATOM) drug discovery framework including a generative statistical chemical model and an iterative drug design loop that selectively explores chemical space using data-driven small molecule property prediction models and physics-based scoring functions. Preliminary results show the promise of an iterative design loop, with opportunities for improvement in areas such as quantifying model prediction uncertainty and optimizing chemical search techniques. The aim of this work is to build an open computational framework accessible to the broader research community that can improve the efficiency of drug discovery on new disease targets.
October 12: NO SEMINAR
Dr. Lale Asik, Univeristy of the Incarnate Word
Dr. Kelsey Gasior, Univeristy of Ottawa
Mr. Jonathan Anzules, UC Merced
November 23: NO SEMINAR (Thanksgiving Break)
Dr. Yoav Ram, Tel Aviv University
Dr. Ann Wells, Jackson Laboratory
If you want to be added to our mailing list please email email@example.com.