About
Click here for full CV.
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Click here to find me on ResearchGate!
Click here to find my Google Scholar profile.
Education
PhD Bioinformatics and Computational Biology - UNC Chapel Hill 2021
MS Applied Mathematics - UNC Chapel Hill 2019
Certificate in Data Science - UNC Chapel Hill
Certificate in Cardiovascular Science - UNC Chapel Hill
BS Mathematics with distinction, BA Biology - Duke University 2015
PhD Bioinformatics and Computational Biology - UNC Chapel Hill 2021
MS Applied Mathematics - UNC Chapel Hill 2019
Certificate in Data Science - UNC Chapel Hill
Certificate in Cardiovascular Science - UNC Chapel Hill
BS Mathematics with distinction, BA Biology - Duke University 2015
Research Interests
Broadly: modeling biological systems
Specifically: cancer, immunotherapy, drug development and delivery strategies
Broadly: modeling biological systems
Specifically: cancer, immunotherapy, drug development and delivery strategies
More Detailed Bio
I am currently a postdoctoral fellow in the Biomedical Engineering department at the University of Virginia. In Dr. Sepideh Dolatshahi's Systems Immunology lab, I am studying mechanisms of intercellular communication in the melanoma tumor microenvironment associated with failure to respond to immune checkpoint blockade. My work involves data-driven network inference and mechanistic models to investigate intracellular regulatory processes and intercellular communication.
I completed my PhD in Bioinformatics and Computational Biology at the University of North Carolina at Chapel Hill, working under Greg Forest and Sam Lai to study pharmacokinetic models for drug delivery and adverse responses. Previously, I studied mechanistic models for cancer growth and response to treatment under Rick Durrett at Duke University. Prior to attending Duke, I worked at the Sartorius-Stedim Biotech Center at UNC Pembroke studying the growth patterns of Photorhabdus luminescens bacteria under Len Holmes and Guo Wei.
I enjoy the unique perspective that comes from studying systems at the intersection of math and biology, allowing ideas from one discipline to cross over into the other and advance knowledge in ways that otherwise wouldn't be possible.
I am currently a postdoctoral fellow in the Biomedical Engineering department at the University of Virginia. In Dr. Sepideh Dolatshahi's Systems Immunology lab, I am studying mechanisms of intercellular communication in the melanoma tumor microenvironment associated with failure to respond to immune checkpoint blockade. My work involves data-driven network inference and mechanistic models to investigate intracellular regulatory processes and intercellular communication.
I completed my PhD in Bioinformatics and Computational Biology at the University of North Carolina at Chapel Hill, working under Greg Forest and Sam Lai to study pharmacokinetic models for drug delivery and adverse responses. Previously, I studied mechanistic models for cancer growth and response to treatment under Rick Durrett at Duke University. Prior to attending Duke, I worked at the Sartorius-Stedim Biotech Center at UNC Pembroke studying the growth patterns of Photorhabdus luminescens bacteria under Len Holmes and Guo Wei.
I enjoy the unique perspective that comes from studying systems at the intersection of math and biology, allowing ideas from one discipline to cross over into the other and advance knowledge in ways that otherwise wouldn't be possible.
Publications
- Cecily Wolfe, Yayi Feng, David Chen, Edwin Purcell, Anne Talkington, Sepideh Dolatshahi, Heman Shakeri (2022), “GeoTyper: Automated Pipeline from Raw scRNA-Seq Data to Cell Type Identification,” Proceedings of the 2022 Systems and Information Engineering Design Symposium, 2022 June, pp. 223-228. DOI 10.1109/SIEDS55548.2022.9799321.
- Anne M. Talkington, Reema B. Davis, Nicholas C. Datto, Emma R. Goodwin, Laura A. Miller, and Kathleen M. Caron (2022), “Dermal lymphatic capillaries do not obey Murray’s Law,” Frontiers in Cardiovascular Medicine, Vol. 9. DOI 10.3389/fcvm.2022.840305.
- Anne M. Talkington, Morgan D. McSweeney, Timothy Wessler, Marielle K. Rath, Zibo Li, Tao Zhang, Hong Yuan, Jonathan E. Frank, M. Gregory Forest, Yanguang Cao, Samuel K. Lai (2022), “A PBPK model recapitulates early kinetics of anti-PEG antibody-mediated clearance of PEG-liposomes,” Journal of Controlled Release, Vol. 343, pp. 518-527. DOI 10.1016/j.jconrel.2022.01.022.
- Anne M. Talkington, Timothy Wessler, Samuel K. Lai, Yanguang Cao, M. Gregory Forest (2021), “Experimental data and PBPK modeling quantify antibody interference in PEGylated drug carrier delivery,” Bulletin of Mathematical Biology, Vol. 83. DOI 10.1007/s11538-021-00950-z. Early Career Feature, Society for Mathematical Biology Newsletter, Spring 2022
- Anne M. Talkington, Morgan D. McSweeney, Tao Zhang, Zibo Li, Andrew C. Nyborg, Brian LaMoreaux, Eric W. Livingston, Jonathan E. Frank, Hong Yuan, Samuel K. Lai (2021), “High molecular weight polyethylene glycol restores prolonged circulation of pegloticase in mice with anti-PEG antibodies,” Journal of Controlled Release, Vol. 338, pp. 804-812. DOI 10.1016/j.jconrel.2021.08.05.
- Michael Senter, Dylan Ray, Christopher Strickland, Steven Thomas, Anne Talkington, Laura Miller, Nicholas Battista (2020), “A Semi-Automated Finite Difference Mesh Creation Method for Use with IB2d,” Bioinspiration and Biomimetics, Vol. 16 (1). DOI 10.1088/1748-3190/ababb0.
- Anne Talkington, Claudia Dantoin, Rick Durrett (2018), “Ordinary differential equation models for adoptive immunotherapy,” Bulletin of Mathematical Biology. DOI 10.1007/s11538-017-0263-8. Top 10 Most Downloaded Articles in Bulletin of Mathematical Biology (2018)
- Anne Talkington (2017), “Undergraduate research highlight: Modeling movement behavior among interacting species,” Women in Mathematical Biology. Association for Women in Mathematics Series, Vol.8. DOI 10.1007/978-3-319-60304-9_12.
- Anne Talkington, Rick Durrett (2015), “Estimating tumor growth rates in vivo,” Bulletin of Mathematical Biology. DOI 10.1007/s11538-015-0110-8.
- “More than Research: A Day in the Life of a Biomathematician” (Association for Women in Mathematics Bulletin, 2013)
- Anne M. Talkington, Floyd L. Inman III, and Leonard D. Holmes (2013), “A novel method for determining microbial kinetics,” Journal of Life Sciences, Vol.7, No.8, pp.787-790.
- Anne Talkington, Floyd Inman III, Leonard D. Holmes, and Guo Wei (2013), “An extension to a logistic model for microbial kinetics,” Advances in Systems Science and Applications, Vol.13, No.1, pp.80-99.
My Projects
Intercellular Communication in the Melanoma Tumor Microenvironment
My current postdoctoral work investigates communication strategies between tumor and immune cells, as well as between types of immune cells, in the tumor microenvironment (TME). Taking a network inference approach, I am investigating communication mechanisms and pathways that arise following immune checkpoint blockade in patients responsive and non-responsive to the therapy. The overall goal of this work is to identify potential mechanisms of resistance, and suggest an alternate or complementary treatment strategy with improved efficacy.
My current postdoctoral work investigates communication strategies between tumor and immune cells, as well as between types of immune cells, in the tumor microenvironment (TME). Taking a network inference approach, I am investigating communication mechanisms and pathways that arise following immune checkpoint blockade in patients responsive and non-responsive to the therapy. The overall goal of this work is to identify potential mechanisms of resistance, and suggest an alternate or complementary treatment strategy with improved efficacy.
PBPK Models for Delivery of PEGylated Therapeutics
My thesis work involved studying antibody responses against drugs coated with polyethylene glycol, or PEG. Working at the interface between applied mathematics and pharmacology, we developed and optimized a pharmacokinetic model of the biodistribution of PEGylated drugs. This model recapitulated accelerated blood clearance due to anti-PEG antibodies, which can lead to loss of efficacy, and highlighted the mechanisms most likely to be responsible for this clearance process. Our work additionally tested a potential solution to mitigate an adverse response to PEG, with promising results in a mouse model. Ultimately, this work holds potential for improving drug safety and efficacy.
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My thesis work involved studying antibody responses against drugs coated with polyethylene glycol, or PEG. Working at the interface between applied mathematics and pharmacology, we developed and optimized a pharmacokinetic model of the biodistribution of PEGylated drugs. This model recapitulated accelerated blood clearance due to anti-PEG antibodies, which can lead to loss of efficacy, and highlighted the mechanisms most likely to be responsible for this clearance process. Our work additionally tested a potential solution to mitigate an adverse response to PEG, with promising results in a mouse model. Ultimately, this work holds potential for improving drug safety and efficacy.
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Lymphatic Development
I have studied optimal branching patterns for lymphatic development, and the implications of this branching structure for lymph transport. This project considered a computational fluid dynamics approach to model flow through dermal lymphatic capillary networks, with the goal of understanding how the system typically functions vs. abnormalities that arise in diseased cases.
I have studied optimal branching patterns for lymphatic development, and the implications of this branching structure for lymph transport. This project considered a computational fluid dynamics approach to model flow through dermal lymphatic capillary networks, with the goal of understanding how the system typically functions vs. abnormalities that arise in diseased cases.
Cancer and Immunotherapy
My work on microbial populations inspired my next research question: how do other systems grow? I began studying the trajectories of cancerous tumors, which share many characteristics with better-understood growing populations. After all, a tumor is a collection of cells! However, things get a little messier when we move from a laboratory setting to a clinical setting with significantly less control over the (in vivo) sample. I worked with Rick Durrett to explore a variety of models, including basic exponential growth, power law growth, Gompertzian growth, and generalized logistic growth. Based on data obtained prior to treatment of the patients, we found that a breast cancer data set and two liver cancer data sets exhibit simple exponential growth, whereas neurological two cancer data sets follow a 2/3 power law model more closely.
Having a basic understanding of what cancer does when left untreated, we then began to consider treatment options, specifically adoptive immunotherapy as one of the newer "hot topics" for cancer research. We chose to study immunotherapy working in the body as a dynamical system, simplified so that it resembled an enhanced "predator-prey" model. Our work was to determine the most effective way of mathematically studying immunotherapy, with an ultimate goal of clinical application. We reviewed several existing models from recent literature, and then modified them to suggest conditions under which immunotherapy can be successful.
Read about our research in the news!
http://www.dukechronicle.com/article/2016/02/researchers-successfully-model-tumor-growth-with-limited-data
http://today.duke.edu/2015/11/cancermath
https://math.duke.edu/news/anne-talkington
My work on microbial populations inspired my next research question: how do other systems grow? I began studying the trajectories of cancerous tumors, which share many characteristics with better-understood growing populations. After all, a tumor is a collection of cells! However, things get a little messier when we move from a laboratory setting to a clinical setting with significantly less control over the (in vivo) sample. I worked with Rick Durrett to explore a variety of models, including basic exponential growth, power law growth, Gompertzian growth, and generalized logistic growth. Based on data obtained prior to treatment of the patients, we found that a breast cancer data set and two liver cancer data sets exhibit simple exponential growth, whereas neurological two cancer data sets follow a 2/3 power law model more closely.
Having a basic understanding of what cancer does when left untreated, we then began to consider treatment options, specifically adoptive immunotherapy as one of the newer "hot topics" for cancer research. We chose to study immunotherapy working in the body as a dynamical system, simplified so that it resembled an enhanced "predator-prey" model. Our work was to determine the most effective way of mathematically studying immunotherapy, with an ultimate goal of clinical application. We reviewed several existing models from recent literature, and then modified them to suggest conditions under which immunotherapy can be successful.
Read about our research in the news!
http://www.dukechronicle.com/article/2016/02/researchers-successfully-model-tumor-growth-with-limited-data
http://today.duke.edu/2015/11/cancermath
https://math.duke.edu/news/anne-talkington
Microbial Population Growth
I studied the growth patterns of Photorhabdus luminescens microbial populations in the Sartorius-Stedim Biotechnology Research and Training Lab. The overall aim of the project was to determine a more efficient and reliable way to measure microbial growth rate. Working from a controlled population of bacteria, maintained in the lab's fermenter, I developed a new Taylor series-based model for accurate population modeling and prediction of the rate parameter. This technique was adopted by the lab, and I went on to explore the formula in the context of other growing biological systems, including cancerous tumors.
I studied the growth patterns of Photorhabdus luminescens microbial populations in the Sartorius-Stedim Biotechnology Research and Training Lab. The overall aim of the project was to determine a more efficient and reliable way to measure microbial growth rate. Working from a controlled population of bacteria, maintained in the lab's fermenter, I developed a new Taylor series-based model for accurate population modeling and prediction of the rate parameter. This technique was adopted by the lab, and I went on to explore the formula in the context of other growing biological systems, including cancerous tumors.